JP6327092B2 - Transformer connection phase determination program, transformer connection phase determination method, and transformer connection phase determination device - Google Patents

Description

  The present invention relates to a transformer connection phase determination program, a transformer connection phase determination method, and a transformer connection phase determination device.

  2. Description of the Related Art In recent years, a distributed power source represented by a photovoltaic power generation (PV) system has become popular in general households. Moreover, when examining a new voltage management system, the connection phase of the transformer connected to any two of the three or more distribution lines in the distribution network may be determined.

  As a prior art, for example, there is a grid interconnection inverter device linked to an existing power system. In this grid-connected inverter device, when the commercial power system is cut off, the transformer takes out the AC output at the linkage point, and the phase comparison circuit controls the voltage before the AC output from the transformer and the commercial power system are cut off. The phase difference with the AC output from the oscillation circuit is detected. Further, when the comparison result in the phase comparison circuit exceeds a predetermined value, the detection circuit generates a control signal, and the conversion unit stops the conversion of the DC output by the control signal from the detection circuit.

JP-A-8-275399

  However, in the related art, when a customer who has introduced a solar power generation system is included in a customer under a transformer in the distribution network, the determination accuracy of the connection phase of the transformer may be lowered.

  In one aspect, an object of the present invention is to provide a transformer connection phase determination program, a transformer connection phase determination method, and a transformer connection phase determination apparatus that improve the determination accuracy of a connection phase of a transformer.

  According to one aspect of the present invention, among the power values in each time zone within a measurement period consumed by a consumer connected to a transformer connected to any two of the three or more distribution lines, the solar power Obtaining the power value of each time zone excluding the power value of the specific time zone consumed by the consumer in which photovoltaic power generation was introduced, and obtaining each time for each distribution line of the three or more distribution lines Based on the power value of the band and the current value of each time zone flowing through each of the distribution lines, a correlation value indicating the correlation between power and current is calculated, and the calculated correlation value of each distribution line Based on the above, a transformer connection phase determination program, a transformer connection phase determination method, and a transformer connection phase determination device for determining two distribution lines connected to the transformer among the three or more distribution lines are proposed.

  According to one aspect of the present invention, it is possible to improve the determination accuracy of the connection phase of the transformer.

FIG. 1 is an explanatory diagram of an example of the transformer connection phase determination method according to the embodiment. FIG. 2 is an explanatory diagram showing a system configuration example of the transformer connection phase determination system 200. FIG. 3 is a block diagram illustrating a hardware configuration example of the transformer connection phase determination apparatus 101. FIG. 4 is an explanatory diagram showing a specific example of the sensor line current data 400. FIG. 5 is an explanatory diagram showing a specific example of customer power data 500. FIG. 6 is an explanatory diagram showing a specific example of the power distribution network equipment data 600. FIG. 7 is a block diagram illustrating a functional configuration example of the transformer connection phase determination apparatus 101. FIG. 8 is an explanatory diagram (part 1) illustrating an example of the contents stored in the daytime data DB 800. FIG. 9 is an explanatory diagram (part 1) illustrating an example of the contents stored in the night data DB 900. FIG. 10 is an explanatory diagram (part 2) of an example of the contents stored in the daytime data DB 800. FIG. 11 is an explanatory diagram (part 2) illustrating an example of the contents stored in the night data DB 900. FIG. 12 is an explanatory diagram showing an example of the stored contents of the daytime total power table 1200. FIG. 13 is an explanatory diagram showing an example of the contents stored in the night total power table 1300. FIG. 14 is an explanatory diagram showing an example of the contents stored in the correlation value table 1400. FIG. 15 is a flowchart illustrating an example of the transformer connection phase determination processing procedure of the transformer connection phase determination device 101. FIG. 16 is a flowchart illustrating an example of a specific processing procedure of the connection phase determination process. FIG. 17 is an explanatory diagram illustrating a configuration example of the power distribution network 1700. FIG. 18 is an explanatory diagram showing the correct answer rate of the transformer connection phase determination result.

  Exemplary embodiments of a transformer connection phase determination program, a transformer connection phase determination method, and a transformer connection phase determination device according to the present invention will be described below in detail with reference to the drawings.

(One Example of Transformer Connection Phase Determination Method)
FIG. 1 is an explanatory diagram of an example of the transformer connection phase determination method according to the embodiment. In FIG. 1, a transformer connection phase determination device 101 is a computer that determines a connection phase of a pole transformer. The pole transformer is a transformer that changes the voltage of an alternating current by the action of electromagnetic induction. In the following description, the pole transformer is simply referred to as “transformer”.

  The connection phase of the transformer is a combination of two distribution lines connected to the transformer among a plurality of distribution lines in the distribution network. For example, in a distribution network with a three-phase three-wire distribution system, a combination of two distribution lines connected to a transformer among three types of distribution lines is a connection phase of the transformer. In addition to the three-phase three-wire system, for example, there is a three-phase four-wire system.

  Here, the solar power generation system is an environmentally friendly power generation system, but has a demerit that it adversely affects the quality of power supplied by the power company. For example, the Electricity Business Law stipulates that electric power companies maintain a specified voltage of 95V to 107V when supplying 100V. However, in the distribution network of the distributed power source, there is a case where a reverse power flow for power sale occurs and the voltage rises, and the voltage of the distribution network deviates from the specified voltage.

  As a method for controlling the voltage, for example, various methods such as changing the sending voltage of the distribution substation, thickening the electric wire, and reviewing the transformer tap can be considered. However, in order to determine where and how to change, it is necessary to know the current voltage distribution of the distribution network. For this reason, it is conceivable to obtain the voltage by circuit calculation called power flow calculation.

  Power flow calculation requires input of various information on the power distribution system, but it is difficult to manage the connection phase of the transformer. For example, in a three-phase three-wire distribution network, it is difficult to manage which phase is connected to the high-voltage side of a combination of three types of distribution lines for a transformer at a contact point with a customer.

  This problem can occur not only in a three-phase three-wire system but also in a distribution network using three or more wires such as a three-phase four-wire system. For this reason, there is a possibility that an incorrect connection phase is used as the management value, and when performing the power flow calculation, the current state of the connection phase of the transformer will be investigated. However, since manual confirmation is costly, it is required to automatically determine the connection phase of the transformer.

  As a method for determining the connection phase of the transformer, for example, depending on the difference in correlation between the current value measured by the sensor capable of measuring the current of each of the three-phase three-wires and the power value of the smart meter installed under the transformer Some determine the connection phase. However, the current value of each line of the three-phase three-wire and the power value of the smart meter installed under the transformer often have a common tendency to take a large value in the morning and evening, for example, in winter, In some cases, the correlation difference cannot be extracted well.

  For this reason, for example, it is conceivable to apply a digital filter to the current value of each of the three-phase three-wires and the power value of the smart meter installed under the transformer to remove common components. As the digital filter, for example, a high-pass filter that attenuates a low-frequency component and passes a high-frequency component can be used.

  However, if a solar power generation system (hereinafter simply referred to as “solar power generation”) is installed in a customer under the transformer, even if a digital filter is applied, the determination accuracy of the connection phase of the transformer is reduced. May end up. For example, the power generation amount of solar power generation can be approximated by “α × area of solar panel × amount of solar radiation” (α is a constant). In addition, the amount of solar radiation in the same region is often almost the same. For this reason, the transition of photovoltaic power generation under the same transformer is almost common for all consumers for both high-frequency components and low-frequency components, making it difficult to remove common components using a digital filter.

  Therefore, in the present embodiment, a transformer connection phase determination method for accurately determining the connection phase of a transformer even when a customer who has introduced solar power generation is included in the consumers under the transformer will be described. . Hereinafter, a processing example of the transformer connection phase determination apparatus 101 will be described with reference to FIG.

  However, in the present embodiment, the “three-phase three-wire system” will be described as an example of the distribution network wiring method. In addition, the three types of distribution lines may be referred to as “a line”, “b line”, and “c line”, respectively. Also, the combination of a line and b line is expressed as “ab phase”, the combination of b line and c line is expressed as “bc phase”, and the combination of c line and a line is expressed as “ca phase”. May be written.

  In the example of FIG. 1, a power distribution network EN is shown as a three-phase three-wire power distribution network. The distribution network EN includes a substation SU that controls the flow of electric power, a sensor SE, and a transformer T1 as one of a plurality of transformers T. The sensor SE measures each value (current value) of a line current flowing through the a line, the b line, and the c line.

Consumers C ₁₁ and C ₁₂ are connected to the transformer T1. The consumer C is a power consumption source that consumes power, and is, for example, a general household or a company. In addition, smart meters SM ₁₁ and SM ₁₂ are respectively introduced to the consumers C ₁₁ and C ₁₂ . The smart meter SM is a power meter having a communication function, and measures the value of power consumed by the customer C (power value).

The customer C ₁₁ is a customer C into which no photovoltaic power generation is introduced. The consumer C ₁₂ is a consumer C into which solar power generation has been introduced. Here, a case where the transformer connection phase determination device 101 determines the connection phase of the transformer T1 on the assumption that the connection phase of the transformer T1 is unknown will be described.

  (1) The transformer connection phase determination device 101 is consumed by the customer C into which photovoltaic power generation has been introduced among the power values in each time zone within the measurement period MP consumed by the customer C connected to the transformer T. The power value of each time zone excluding the specified power value of the specific time zone is acquired.

  Here, the measurement period MP is a period in which the power value consumed by the customer C is measured, and can be arbitrarily set. As the measurement period MP, for example, a period of several months to several years is set. Each time zone in the measurement period MP is a section divided by dividing the measurement period by a time interval such as 30 minutes or 1 hour, for example.

  The specific time zone is a time zone in which the power generation amount of the photovoltaic power generation introduced to the customer C cannot be regarded as “0 (zero)”. In other words, the specific time zone is a time zone in which the amount of solar radiation in the region including the customer C cannot be ignored. The amount of solar radiation is a measure of the amount of radiant energy from the sun.

  For example, during the bright period from sunrise to sunset, there is a high possibility that the amount of solar radiation in the area including the customer C will not be ignored. For this reason, the specific time zone is set in consideration of the time zone from sunrise to sunset, for example. In the example of FIG. 1, it is assumed that the specific time zone is set to a time zone from 6:00 to 18:00. Moreover, it is set as the area which divided each time slot | zone at intervals of 30 minutes.

In this case, the transformer connected phase determination unit 101, of the power value of each time zone in the measurement period MP consumed by the customer C _11, C ₁₂ connected to the transformer T1, which is consumed by the customer C ₁₂ The power value of each time slot excluding the power value of each time slot from 6:00 to 18:00 is acquired. Consumer C ₁₂ is a consumer C that solar power has been introduced.

Power value of each time zone in the customer C _11, C ₁₂ measurement period spent in the MP, the power value measured respectively by customer C _11, a smart meter SM _11, SM _12, which is introduced into the C ₁₂ is there. For the consumer C ₁₁ into which solar power generation has not been introduced, the power consumption P _cspt itself of the consumer C ₁₁ is obtained as the measured value of the smart meter SM ₁₁ .

On the other hand, for the customer C ₁₂ into which solar power generation has been introduced, the sum of the power consumption P _cspt of the customer C ₁₂ and the power generation power P _sun generated by solar power generation is obtained as the measured value of the smart meter SM ₁₂ ( The power consumption P _cspt takes a positive value and the generated power P _sun takes a negative value). For this reason, if the electric power P _sun generated by solar power generation is larger than the electric power consumption P _cspt , the electric power for surplus power generation is recorded as a power value (negative value) in the smart meter SM ₁₂ . However, the smart meter SM _12, the power consumption P _CSPT the generated power P _sun respective values can not be obtained.

  As an example, assuming that each time zone in the measurement period MP is a section obtained by dividing the measurement period MP at 30-minute intervals, the acquired power values in each time zone are as follows, for example.

Power value of each time zone from 6:00 to 18:00 ⇒ Power value consumed by customer C ₁₁ Power value of each time zone from 18:00 to 6:00 ⇒ Consumer C ₁₁ , C Total power consumed in ₁₂

  (2) The transformer connection phase determination apparatus 101 is based on the acquired power value of each time zone and the current value of each time zone flowing through each distribution line for each distribution line of the a-line to c-line. Then, a correlation value indicating a correlation between electric power and current is calculated. The current value that flows through each distribution line of the a-line to the c-line in each time zone within the measurement period MP is a current value measured by the sensor SE. A specific calculation example of the correlation value will be described later with reference to FIG.

  (3) The transformer connection phase determination device 101 determines two distribution lines connected to the transformer T among the a-line to c-line based on the calculated correlation value of each distribution line. Specifically, for example, the transformer connection phase determination device 101 determines the connection phase of the transformer T from the value having the smallest correlation among the calculated correlation values of the respective distribution lines.

In the example of FIG. 1, the correlation value of the a line is “ρ _a ”, the correlation value of the b line is “ρ _b ”, and the correlation value of the c line is “ρ _c ”. Further, the size relationship of the correlation values ρ _a ~ρ _c and "ρ _c <ρ _a, ρ _b". In this case, the transformer connection phase determination device 101 determines that the connection phase of the transformer T1 is an ab phase (combination of a line and b line).

  Thus, according to the transformer connection phase determination device 101, among the power values of each time zone consumed by the customer C under the transformer, the specific time consumed by the customer C who has already introduced solar power generation The correlation value indicating the correlation between the power and the current can be calculated by excluding the band power value. As a result, it is possible to perform correlation analysis by removing information including common components that are unnecessary for the correlation analysis due to the influence of photovoltaic power generation, and to improve the determination accuracy of the connected phase of the transformer T.

(System configuration example of transformer connection phase judgment system)
Next, a case where the transformer connection phase determination device 101 is applied to a transformer connection phase determination system that determines the connection phase of the transformer T existing on the distribution network EN will be described.

  FIG. 2 is an explanatory diagram showing a system configuration example of the transformer connection phase determination system 200. In FIG. 2, a transformer connection phase determination system 200 is a system for determining a connection phase of a transformer T existing on the distribution network EN. The transformer connection phase determination system 200 includes a power distribution network EN and a transformer connection phase determination device 101.

Grid EN includes a substation SU, and sensor SE, and the transformer Tl to Tn, and a customer C (e.g., customer _{C 11, C 12, C n1} ). Each customer C is installed with a smart meter SM (for example, smart meters SM ₁₁ , SM ₁₂ , SM _n1 ). However, the customer C which has not introduced the smart meter SM may be included in the customer C.

  The substation SU controls the flow of electric power and sends out electric power to the consumer C. The transformers T1 to Tn are transformers that change the voltage of the alternating current by the action of electromagnetic induction. In the following description, the transformers T1 to Tn from the substation SU in the distribution network EN may be referred to as “high voltage distribution network”. Further, the portion from the transformers T1 to Tn to the customer C connected to each of the transformers T1 to Tn in the distribution network EN may be referred to as a “low voltage distribution network”.

  The sensor SE is installed closer to the substation SU than the transformers T1 to Tn, and measures the current of the high-voltage distribution network. Here, in the present embodiment, the high-voltage distribution network transmits power by three-phase three-wires, that is, three electric wires. Therefore, the sensor SE calculates the current values of the three electric wires (a line to c line). measure.

  The transformer connection phase determination device 101 is a computer that determines the connection phases of the transformers T1 to Tn. Moreover, the transformer connection phase determination apparatus 101 can acquire the value measured by the sensor SE and the power value measured by the smart meter SM introduced by the customer C.

(Hardware configuration example of transformer connection phase determination apparatus 101)
FIG. 3 is a block diagram illustrating a hardware configuration example of the transformer connection phase determination apparatus 101. In FIG. 3, a transformer connection phase determination apparatus 101 includes a CPU (Central Processing Unit) 301, a memory 302, a disk drive 303, a disk 304, an I / F (Interface) 305, a display 306, and an input device 307. And having. Each component is connected by a bus 300.

  Here, the CPU 301 governs overall control of the transformer connection phase determination apparatus 101. The memory 302 has, for example, a ROM (Read-Only Memory), a RAM (Random Access Memory), a flash ROM, and the like. Specifically, for example, a flash ROM or ROM stores various programs, and a RAM is used as a work area for the CPU 301. The program stored in the memory 302 is loaded into the CPU 301 to cause the CPU 301 to execute the coded process.

  The disk drive 303 controls reading / writing of data with respect to the disk 304 according to the control of the CPU 301. The disk 304 stores data written under the control of the disk drive 303. Examples of the disk 304 include a magnetic disk and an optical disk.

  The I / F 305 is connected to the network 310 through a communication line, and is connected to other devices via the network 310. The I / F 305 controls an internal interface with the network 310 and controls input / output of data from other devices. The network 310 is, for example, a local area network (LAN), a wide area network (WAN), or the Internet.

  A display 306 displays data such as a document, an image, and function information as well as a cursor, an icon, or a tool box. As the display 306, for example, a CRT (Cathode Ray Tube), a liquid crystal display, a plasma display, or the like can be adopted.

  The input device 307 has keys for inputting characters, numbers, various instructions, and the like, and inputs data. The input device 307 may be a keyboard or a mouse, or may be a touch panel type input pad or a numeric keypad.

  Note that the transformer connection phase determination device 101 may not include, for example, the display 306 or the input device 307 among the components described above. Moreover, the transformer connection phase determination apparatus 101 may include, for example, an SSD (Solid State Drive), a scanner, and a printer in addition to the above-described components.

(Specific example of sensor line current data 400)
Next, a specific example of the sensor line current data 400 will be described. The sensor line current data 400 is time-series data indicating the current value of each line (a line to c line) measured by the sensor SE in the distribution network EN.

  FIG. 4 is an explanatory diagram showing a specific example of the sensor line current data 400. In FIG. 4, the sensor line current data 400 indicates a current value that flows in each line (a line to c line) in each time zone within the measurement period MP. Here, the measurement period MP is assumed to be one year from January 1 to December 31 in 20XX. In addition, each time zone in the measurement period MP is a section obtained by dividing the measurement period MP at intervals of 30 minutes.

Here, the times t1 to t17520 correspond to the respective time zones in the measurement period MP. For example, time t1 indicates a time period from 0:00 to 0:30 on January 1. Therefore, the current value I _a (1) indicates the current value that flows through the a line during the time period from 0:00 to 0:30 on January 1.

The current value I _b (1) indicates a current value that flows through the b line during the time period from 0:00 to 0:30 on January 1. Further, the current value I _c (1) indicates a current value that flows through the c-line in the time zone from 0:00 to 0:30 on January 1. That is, the sensor line current data 400 is an example in which the current value flowing through each line (a line to c line) is measured at 48 points per day and 17520 points per year.

(Specific example of customer power data 500)
Next, a specific example of customer power data 500 will be described. The consumer power data 500 is a time series indicating the power value measured by the smart meter SM installed in the consumer C connected to the transformer T in the distribution network EN, that is, the power value consumed by the consumer C. It is data. Here, as an example, illustrating the power value consumed by the customer C ₁₁ which is connected to the transformer T1 in the distribution network EN as an example.

FIG. 5 is an explanatory diagram showing a specific example of customer power data 500. In FIG. 5, consumer power data 500 indicates a power value consumed by the consumer C ₁₁ in each time zone within the measurement period MP. Here, the measurement period MP is assumed to be one year from January 1 to December 31 in 20XX. In addition, each time zone in the measurement period MP is a section obtained by dividing the measurement period MP at intervals of 30 minutes.

Here, the times t1 to t17520 correspond to the respective time zones in the measurement period MP. For example, time t1 indicates a time period from 0:00 to 0:30 on January 1. Therefore, the power value P ₁₁ (1) indicates the power value consumed by the consumer C ₁₁ during the time period from 0:00 to 0:30 on January 1.

The power value P ₁₁ (2) indicates the power value consumed by the customer C ₁₁ during the time period from 0:30 to 1:00 on January 1st. That is, the consumer power data 500 is an example in which the power value consumed by the consumer C ₁₁ is measured 48 points per day and 17520 points per year.

(Specific example of distribution network equipment data 600)
FIG. 6 is an explanatory diagram showing a specific example of the power distribution network equipment data 600. In FIG. 6, distribution network facility data 600 is information regarding facilities in the distribution network EN. The distribution network equipment data 600 includes fields of transformer ID, connection phase, smart meter installed customer ID, and photovoltaic power generation installed customer ID. By setting information in each field, the distribution network facility information 600 −1 to 600-n are stored as records.

  Here, the transformer ID is an identifier for identifying the transformer T. The connection phase indicates a connection phase (ab phase, bc phase, or ca phase) of the transformer T. However, when the connection phase of the transformer T has not been determined, “not yet” is set in the connection phase field. The smart meter installed customer ID is an identifier for identifying the customer C installed with the smart meter under the transformer.

The customer ID already introduced with photovoltaic power generation is an identifier for identifying the customer C who has introduced the smart meter under the transformer and has already introduced photovoltaic power generation. For example, the distribution network facility information 600-1 indicates the customers C ₁₁ and C ₁₂ that have been introduced to the smart meter and are connected to the transformer T1 in the distribution network EN, and the customer C ₁₂ that has already been introduced with solar power generation.

(Functional configuration example of transformer connection phase determination apparatus 101)
FIG. 7 is a block diagram illustrating a functional configuration example of the transformer connection phase determination apparatus 101. In FIG. 7, the transformer connection phase determination device 101 includes a control unit 701 and a storage unit 702. The control unit 701 includes an acquisition unit 711, a data classification unit 712, a filter application unit 713, a correlation analysis unit 714, a connection phase determination unit 715, and an output unit 716. The control unit 701 realizes the function by causing the CPU 301 to execute a program stored in a storage device such as the memory 302 and the disk 304 illustrated in FIG. 3 or by using the I / F 305, for example. The processing result of each functional unit is stored in, for example, a register included in the CPU 301 or the memory 302. The storage unit 702 is realized by a storage device such as the memory 302 and the disk 304.

  The acquisition unit 711 acquires sensor line current data 400 (see, for example, FIG. 4) indicating the current value of each line (a line to c line) measured by the sensor SE in the power distribution network EN. Specifically, for example, the acquisition unit 711 acquires the sensor line current data 400 from the sensor SE in the power distribution network EN. For example, the acquisition unit 711 may acquire the sensor line current data 400 by a user operation input using the input device 307 illustrated in FIG. 3.

  In addition, the acquisition unit 711 acquires customer power data 500 (for example, see FIG. 5) indicating the power value measured by the smart meter SM installed in the customer C connected to the transformer T in the distribution network EN. To do. Specifically, for example, the acquisition unit 711 acquires customer power data 500 from each smart meter SM in the power distribution network EN. For example, the acquisition unit 711 may acquire the customer power data 500 by a user operation input using the input device 307.

  In addition, the acquisition unit 711 acquires distribution network facility data 600 relating to facilities in the distribution network EN. Specifically, for example, the acquisition unit 711 may acquire the distribution network facility data 600 by a user operation input using the input device 307. The acquired power distribution network equipment data 600 is stored in the storage unit 702, for example.

  The data classifying unit 712 refers to the acquired sensor line current data 400 and sets the current value of each line (a line to c line) to a current value in a specific time zone and other values different from the specific time zone. The current value is classified into the time zone. As described above, the specific time zone is a time zone in which the power generation amount of the photovoltaic power generation introduced to the customer C cannot be regarded as “0 (zero)”.

  In the following explanation, a specific time zone in one day is expressed as “day time zone”, and another time zone different from the specific time zone in one day is expressed as “night time zone”. There is a case. Further, a case where “time zone from 6:00 to 18:00” is set as the daytime time zone will be described as an example.

  In this case, the data classifying unit 712 refers to the sensor line current data 400 and determines the current value of each line (a line to c line) as the current value of the daytime period (6:00 to 18:00). And current values in the night time zone (18:00 to 6:00).

For example, time t13 indicates a time zone from 6:00 to 6:30. That is, the current value I _a (13) indicates the current value that flows through the a line during the time period from 6:00 to 6:30. Therefore, the data classification unit 712 classifies the current value I _a (13) as _a current value in the daytime period.

  The current value of each line (line a to line c) classified in the daytime time zone is stored, for example, in a daytime data DB (database) 800 shown in FIG.

Further, for example, time t12 indicates a time zone from 5:30 to 6:00. That is, the current value I _a (12) indicates the current value that flows through the a line during the time period from 5:30 to 6:00. Therefore, the data classification unit 712 classifies the current value I _a (12) as _a current value in the night time zone.

  The current value of each line (a line to c line) classified in the night time zone is stored in, for example, the night data DB 900 shown in FIG. 9 described later.

  However, the length of the day may vary depending on the time. For example, the length of the day varies depending on the season of spring, summer, autumn and winter. For this reason, the daytime period may be set for each season in consideration of a different daytime length depending on the season. For example, in summer (June, July, and August), the daytime is longer than in other seasons, so the time zone of the day may be set to a longer period than in other seasons.

  In addition, the data classification unit 712 refers to the acquired customer power data 500 and classifies the power value of each customer C into a power value in a day time zone and a power value in a night time zone. .

For example, the power value P ₁₁ (13) indicates the power value consumed by the customer C ₁₁ during the time period from 6:00 to 6:30. Therefore, the data classification unit 712 classifies the power value P ₁₁ (13) as a power value in the daytime time zone. The power value of each customer C classified into the daytime time zone is stored in, for example, a daytime data DB 800 shown in FIG.

Further, for example, the power value P ₁₁ (12) indicates the power value consumed by the customer C ₁₁ in the time zone from 5:30 to 6:00. Therefore, the data classification unit 712 classifies the power value P ₁₁ (12) as a power value in the night time zone. The current value of each line (a line to c line) classified in the night time zone is stored in, for example, the night data DB 900 shown in FIG. 9 described later.

Here, the contents stored in the day data DB 800 and the night data DB 900 will be described. The day data DB 800 and the night data DB 900 are stored in the storage unit 702, for example. Here, as the customer C connected to the transformer T in the distribution network EN, the customers C ₁₁ and C ₁₂ connected to the transformer T1, the customers C ₂₁ and C ₂₂ connected to the transformer T2, and the transformer T3 are used. A description will be given by taking the connected customers C ₃₁ and C ₃₂ as an example.

FIG. 8 is an explanatory diagram (part 1) illustrating an example of the contents stored in the daytime data DB 800. In FIG. 8, the daytime data DB 800 stores the current values of the daytime hours (6:00 to 18:00) of each line (line a to line c). In addition, the daytime data DB 800 stores the power value of each customer C ₁₁ , C ₁₂ , C ₂₁ , C ₂₂ , C ₃₁ , C _{32 during} the daytime period (6:00 to 18:00). Yes.

In FIG. 8, in order to identify that the current value of each line and the power value of each customer C are in the daytime period, “ _{_Day} "is added.

FIG. 9 is an explanatory diagram (part 1) illustrating an example of the contents stored in the night data DB 900. In FIG. 9, the night data DB 900 stores the current value of the night time zone (18:00 to 6:00) of each line (a line to c line). The night data DB 900 stores the power value of each customer C ₁₁ , C ₁₂ , C ₂₁ , C ₂₂ , C ₃₁ , C _{32 in} the night time zone (18:00 to 6:00). Yes.

In FIG. 9, in order to identify that the current value of each line and the power value of each customer C are in the night time zone, the current value of each line and the power value of each customer C are suffixed with “ _{_Night} "is added.

  Returning to the description of FIG. 7, the filter application unit 713 applies a filter that attenuates the low-frequency component to the current value of each line and the power value of each customer C. As the filter, a high-pass filter (for example, a FIR (Finite Impulse Response) filter) that attenuates low-frequency components and passes high-frequency components can be used.

Specifically, for example, the filter application unit 713 refers to the daytime data DB 800 and selects a filter determined by a pre-recorded filter coefficient as the daytime time zone (6:00 a.m.) of each line (line a to line c). To 18:00). Further, the filter application unit 713 refers to the daytime data DB 800 and applies the same filter to the daytime time zone (6:00) of each customer C ₁₁ , C ₁₂ , C ₂₁ , C ₂₂ , C ₃₁ , C _32. Applies to the power value of minutes to 18:00).

The filter application unit 713 refers to the night data DB 900 and applies the same filter to the current value in the night time zone (18:00 to 6:00) of each line (a line to c line). To do. The filter application unit 713 refers to the night data DB 900 and applies the same filter to the night time zone (18:00) of each customer C ₁₁ , C ₁₂ , C ₂₁ , C ₂₂ , C ₃₁ , C _32. Applies to the power value between min and 6:00).

More specifically, for example, the filter application unit 713 can obtain the current value in the daytime period of each line (line a to line c) after applying the filter by using the following formula (1). However, I _{X daytime_filter} (t) is a current value in the daytime time zone of each line (line a to line c) after application of the filter (X = a, b, c).

I _{X noon _filter (t) = - 1/} 3 × I X (t-1) _ _{day + 2/3 × I X (} t) _ _day
−1 / 3 × I _X (t + 1) _{_daytime} (1)

  The current value of the daytime time zone of each line (a line to c line) after the filter application is stored in, for example, the daytime data DB 800.

Further, for example, the filter application unit 713 uses the following formula (2) to calculate the electric power in the daytime hours of the customers C ₁₁ , C ₁₂ , C ₂₁ , C ₂₂ , C ₃₁ , and C ₃₂ after application of the filter. The value can be determined. However, P _{XX daytime_filter} (t) is a power value in the daytime time period of each customer C ₁₁ , C ₁₂ , C ₂₁ , C ₂₂ , C ₃₁ , C ₃₂ after applying the filter (XX = 11, 12, 21, 22, 31, 32).

P _{XX noon _filter (t) = - 1/} 3 × P XX (t-1) _ _{daytime + 2/3 × P XX (} t) _ _noon
−1 / 3 × P _XX (t + 1) _{_Daytime} (2)

The electric power values in the daytime hours of the customers C ₁₁ , C ₁₂ , C ₂₁ , C ₂₂ , C ₃₁ , C ₃₂ after applying the filter are stored in, for example, the day data DB 800.

In addition, for example, the filter application unit 713 can obtain the current value of the night time zone of each line (a line to c line) after the filter application using the following formula (3). However, I _{X night_filter} (t) is a current value in the night time zone of each line (a line to c line) after the filter application (X = a, b, c).

I _{X night _filter (t) = - 1/} 3 × I X (t-1) _ _{night + 2/3 × I X (} t) _ _night
−1 / 3 × I _X (t + 1) _{_night} (3)

  The current value in the night time zone of each line (a line to c line) after applying the filter is stored in the night data DB 900, for example.

In addition, for example, the filter application unit 713 uses the following formula (4) to calculate the electric power in the night time zone of each customer C ₁₁ , C ₁₂ , C ₂₁ , C ₂₂ , C ₃₁ , C ₃₂ after the filter application. The value can be determined. However, P _{XX night_filter} (t) is a power value in the night time zone of each customer C ₁₁ , C ₁₂ , C ₂₁ , C ₂₂ , C ₃₁ , C ₃₂ after applying the filter (XX = 11, 12, 21, 22, 31, 32).

P _{XX night _filter (t) = - 1/} 3 × P XX (t-1) _ _{night + 2/3 × P XX (} t) _ _night
−1 / 3 × P _XX (t + 1) _{_night} ... (4)

The power values in the night time zone of the consumers C ₁₁ , C ₁₂ , C ₂₁ , C ₂₂ , C ₃₁ , C ₃₂ after applying the filter are stored in the night data DB 900, for example.

  Here, the contents stored in the daytime data DB 800 that stores the current value of the daytime time zone of each line (line a to line c) after applying the filter will be described. However, “#” at time t # is reassigned so that “# = 1, 2, 3,...”.

FIG. 10 is an explanatory diagram (part 2) of an example of the contents stored in the daytime data DB 800. In FIG. 10, the daytime data DB 800 stores the current value of the daytime time zone (6:00 to 18:00) of each line (a line to c line) after the filter is applied. In addition, the daytime data DB 800 stores the electric power values in the daytime hours (6: 00-18: 00) of the customers C ₁₁ , C ₁₂ , C ₂₁ , C ₂₂ , C ₃₁ , C ₃₂ after applying the filter. Is remembered.

  Next, the contents stored in the night data DB 900 that stores the current value of the night time zone of each line (a line to c line) after the application of the filter will be described. However, “#” at time t # is reassigned so that “# = 1, 2, 3,...”.

FIG. 11 is an explanatory diagram (part 2) illustrating an example of the contents stored in the night data DB 900. In FIG. 11, the night data DB 900 stores the current value of the night time zone (18:00 to 6:00) of each line (line a to line c) after applying the filter. In addition, the night data DB 900 is an electric power value of each customer C ₁₁ , C ₁₂ , C ₂₁ , C ₂₂ , C ₃₁ , C _{32 in} the night time zone (18:00 to 6:00) after applying the filter. Is remembered.

Returning to the description of FIG. 7, the correlation analysis unit 714 calculates a correlation value ρ (I _X , P) indicating the correlation between power and current for each line (line a to line c) (where X = a, b, c). In the following description, an arbitrary transformer T among the transformers T1 to Tn in the distribution network EN may be referred to as “transformer Ti” (i = 1, 2,..., N). Further, an arbitrary customer C connected to the transformer Ti may be expressed as “customer C _ij ” (j: a natural number of 1 or more).

Specifically, for example, first, the correlation analysis unit 714 refers to the distribution network equipment data 600 shown in FIG. 6, and all the consumers C connected to the transformer Ti (customers that have already installed the smart meter SM). C) is specified. As an example, taking the transformer T1 as an example, the consumers C ₁₁ and C ₁₂ connected to the transformer T1 are specified.

In addition, the correlation analysis unit 714 refers to the distribution network facility data 600 and identifies the customer C that has already introduced the solar power generation among all the customers C connected to the identified transformer Ti. As an example, taking the transformer T1 as an example, already introduced customer C ₁₂ solar power among consumers C _11, C ₁₂ connected to the transformer T1 is identified.

Next, the correlation analysis unit 714 uses the following equation (5) to select each time in the daytime period of the customer C that has not yet introduced solar power generation among the consumers C connected to the identified transformer Ti. The total power value of t is calculated. Power value each time t of the daytime time zone of the connected customer C _ij in the transformer Ti, for example, is specified from noon data DB800 shown in FIG. 10.

However, _{Pi_daytime_filter} (t) is a total electric power value of each time t of the daytime time zone of the consumer C which has not yet introduced solar power generation among the consumers C connected to the transformer Ti. z _ij (t) is, in the case if the customer C _ij is already introducing solar power "z _ij (t) = 0", and the customer C _ij of the non-introduction of the solar power "z _ij ( t) = 1 ".

P _{i —daytime_filter} (t) = Σ _j {z _ij (t) P _{ij daytime_filter} (t)} (5)

As an example, when the total power value P _{1_day_filter} (1) at time t1 in the daytime time period of a customer C that has not yet been introduced with photovoltaic power generation connected to the transformer T1 is taken as an example, “P _{1_day_filter} ( 1) = P _{11 daytime_filter} (1) ”. The calculated total power value P _{i_daytime_filter} (t) is stored in, for example, the daytime total power table 1200 shown in FIG.

  Here, the contents stored in the daytime total power table 1200 will be described. The daytime total power table 1200 is stored in the storage unit 702, for example.

FIG. 12 is an explanatory diagram showing an example of the stored contents of the daytime total power table 1200. In FIG. 12, the daytime total power table 1200 shows the daytime hours of customers C who have not yet introduced solar power generation among the customers C connected to each transformer Ti (6:00 to 18:00). The total power value P _{i_daytime_filter} (t) at each time t is stored.

Returning to the description of FIG. 7, the correlation analysis unit 714 calculates the total power value at each time t in the night time zone of all the consumers C connected to the transformer Ti using the following equation (6). . The power value at each time t in the night time zone of the consumer C _ij connected to the transformer Ti is specified from, for example, the night data DB 900 shown in FIG. However, _{Pi_night_filter} (t) is a total electric power value of each time t of the night time zone of all the consumers C connected to the transformer Ti.

P _i — _{night — filter} (t) = Σ _j {P _{ij night — filter} (t)} (6)

As an example, if the total power value P _{1_night_filter} (1) at time t1 of the night time zone of all the consumers C connected to the transformer T1 is taken as an example, “P _{1_night_filter} (1) = P _{11 night _Filter} (1) + P _{12 night _filter} (1) ". The calculated total power value P _{i_night_filter} (t) is stored in, for example, the night total power table 1300 illustrated in FIG.

  Here, the contents stored in the night total power table 1300 will be described. The night total power table 1300 is stored in the storage unit 702, for example.

FIG. 13 is an explanatory diagram showing an example of the contents stored in the night total power table 1300. In FIG. 13, the total night power table 1300 includes a total power value P _{i_night_filter at} each time t in the night time zone (18:00 to 6:00) of all the consumers C connected to each transformer Ti. (T) is stored.

  In the above description, the case where the total power value at each time t is obtained using the power value after applying the filter has been described as an example, but the present invention is not limited to this. For example, the correlation analysis unit 714 may determine the total power value at each time t using the power value before applying the filter.

Returning to the description of FIG. 7, the correlation analysis unit 714 next selects each time in the daytime period of the customer C into which the photovoltaic power generation connected to the transformer Ti is not introduced for each line (line a to line c). Based on the total power value of t and the current value at each time t in the daytime period of each line (line a to line c), the first correlation value ρ _daytime (I _X) indicating the correlation between power and current , P _i ).

Specifically, for example, the correlation analysis unit 714 can calculate the first correlation value ρ _daytime (I _X , P _i ) using the following formula (7) and the following formula (8) (X = A, b, c). However, ρ (x, y) represents the strength of the correlation between x and y. The time t is assumed to be “t = 1, 2,..., T”. Also, let x (t) be “x (t) = I _{X Daytime_filter} (1), I _{X Daytime_filter} (2),..., _{IX Daytime_filter} (T)”. For example, x (t) is specified from the daytime data DB 800 shown in FIG. Further, y (t) is _assumed to be “y (t) = P _{i_day_filter} (1), P _{i_day_filter} (2),..., P _{i_day_filter} (T)”. For example, y (t) is specified from the daytime total power table 1200 shown in FIG.

Thus, the first correlation values ρ _daytime (I _a , P _i ), ρ _daytime (I _b , P _i ), and ρ _daytime (I _c , P _i ) for each line (line a to line c) are calculated. be able to. The calculated first correlation value ρ _daytime (I _X , P _i ) is stored in, for example, a correlation value table 1400 shown in FIG.

In the above description, the case where the first correlation value ρ _noon (I _X , P _i ) is obtained by using the total power value and the current value after applying the filter at each time t in the daytime period is taken as an example. Although it has been described, it is not limited to this. For example, the correlation analysis unit 714 determines the first correlation value ρ _noon (I _X , P _i ) using the total power value and current value before application of the filter at each time t in the noon time zone. Also good.

In addition, the correlation analysis unit 714, for each line (a line to c line), the total power value at each time t in the night time zone of all the consumers C connected to the transformer Ti, and each line (a line to c line). ) To calculate a second correlation value ρ _night (I _X , P _i ) indicating a correlation between electric power and current based on the current value at each time t in the night time zone.

Specifically, for example, the correlation analysis unit 714 can calculate the second correlation value ρ _night (I _X , P _i ) using the above formula (7) and the above formula (8) (X = A, b, c). However, x (t) is “x (t) = I _{X night_filter} (1), I _{X night_filter} (2),..., I _{X night_filter} (T)”. For example, x (t) is specified from the night data DB 900 shown in FIG. Further, y (t) is _assumed to be “y (t) = P _{i_night_filter} (1), P _{i_night_filter} (2),..., P _{i_night_filter} (T)”. For example, y (t) is specified from the night total power table 1300 shown in FIG.

Accordingly, the second correlation values ρ _night (I _a , P _i ), ρ _night (I _b , P _i ), and ρ _night (I _c , P _i ) for each line (line a to line c) are calculated. be able to. The calculated second correlation value ρ _night (I _X , P _i ) is stored in, for example, the correlation value table 1400 shown in FIG.

In the above description, the case where the second correlation value ρ _night (I _X , P _i ) is obtained by using the total power value and current value after applying the filter at each time t in the night time zone is taken as an example. Although it has been described, it is not limited to this. For example, the correlation analysis unit 714 determines the second correlation value ρ _night (I _X , P _i ) using the total power value and current value before application of the filter at each time t in the night time zone. Also good.

  Here, the contents stored in the correlation value table 1400 will be described. The correlation value table 1400 is stored in the storage unit 702, for example.

FIG. 14 is an explanatory diagram showing an example of the contents stored in the correlation value table 1400. 14, the correlation value table 1400, for each transformer Ti, the first correlation value [rho _noon (I _X, P _i), the second correlation value [rho _night (I _X, P _i) and the correlation value [rho (I _X , P _i ) is stored (X = a, b, c).

In the example of (14-1) illustrated in FIG. 14, the correlation value table 1400 stores the first correlation values ρ _day (I _X , P ₁ ) to ρ _day (I _X , P _n ) of the transformers T1 to Tn. doing. The correlation value table 1400 stores the second correlation values ρ _night (I _X , P ₁ ) to ρ _night (I _X , P _n ) of the transformers T1 to Tn.

Referring back to FIG. 7, correlation analysis unit 714 calculates the first correlation value ρ _noon (I _X, P _i) and the second correlation value ρ _night (I _X, P _i) obtaining a weighted average of the Thus, the correlation value ρ (I _X , P _i ) is calculated (X = a, b, c). Specifically, for example, the correlation analysis unit 714 can calculate the correlation value ρ (I _X , P _i ) using the following equation (9). However, k _day is a weight applied to the first correlation value ρ _day (I _X , P _i ). In addition, k _night is a weight applied to the second correlation value ρ _night (I _X , P _i ). In addition, “k _day + k _night = 1”.

ρ (I _X , P _i ) = k _day × ρ _day (I _X , P _i ) + k _night × ρ _night (I _X , P _i ) (9)

The calculated correlation value ρ (I _X , P _i ) is stored in, for example, the correlation value table 1400 shown in FIG. In the example of (14-2) shown in FIG. 14, the correlation value table 1400 stores the correlation values ρ (I _X , P ₁ ) to ρ (I _X , P _n ) of the transformers T1 to Tn (however, X = a, b, c).

Also, correlation analysis unit 714, customer C that the number N _Day of measurement points in current consumer C power or each line (a line ~c lines) included in the daytime time zone, included in the evening hours based on the number N _night measurement point of the current power or each line (a line ~c line), the weight k _day, it may be to determine the _night k.

For example, correlation analysis unit 714, by counting the number of times t of the daytime data DB800 shown in FIG. 8, it is possible to determine the number N _day of measurement points. Further, for example, the correlation analysis unit 714, by counting the number of times t of the data DB900 night shown in FIG. 9, it is possible to determine the number N _night measurement point.

Here, the number N _day of measurement points, N _night, and the power value of the consumer C as measured by the smart meter SM, the number of data of the current value of each line to be measured by the sensor SE (a line ~c line) Equivalent to. In addition, it can be said that the determination accuracy of the connection phase of the transformer Ti improves as the number of data used for the determination of the connection phase of the transformer Ti increases. Therefore, the correlation analysis unit 714, for example, the larger the value of "N _Day / N _Night", as the weight k _daylight increases, the weight k _day, may be to determine the _night k.

Further, the correlation analysis unit 714 calculates the weights k _day and k _night based on the average value P _{i_day_ave} of the power value in the _{daytime period} and the average value P _{i_night_ave} of the power value in the night time period. It may be decided. Here, the average value P _{i_daytime_ave} is the average of the total power value at each time t in the daytime time period of the customer C that has not yet introduced solar power generation among the consumers C connected to the transformer Ti. The average value P _{i_night_ave} is an average of the total power value at each time t in the night time zone of all the consumers C connected to the transformer Ti.

For example, the correlation analysis unit 714 calculates the average value P _{i_day_ave} by calculating the average of the total power value P _{i_day_filter} (t) with reference to the daytime total power table 1200 illustrated in FIG. Can do. For example, the correlation analysis unit 714 calculates the average value P _{i_night_ave} by _{obtaining the} average of the total power value P _{i_night_filter} (t) with reference to the night total power table 1300 shown in FIG. can do.

Here, it can be said that as the average value P _{i_day_ave} and the average value P _{i_night_ave} are larger, the average value P _{i_day_ave} and the average value P _{i_night_ave} better characterize the total demand under the transformer. Therefore, the correlation analysis unit 714, for example, as the value of "P _{i_ day _ave} / P _{i_ night _ave"} is large, so that the weight k _daylight increases, the weight k _day, be to determine the _night k Good.

The correlation analysis unit 714 also _calculates the ratio R1 of the average value P _{i_day_ave} to the average value I _{day_ave} of the current value of each line (line a to c) in the _daytime period and each line (a based on the average value P _{i_ night} percentage of _{_ave} R2 to the average value I _{night _ave} current value of the line ~c line), the weight k _day, may be to determine the _night k.

For example, the correlation analysis unit 714 refers to the daytime data DB 800 illustrated in FIG. 10 to obtain an average of the current values I _{X daytime_filter} (t) of each line (a line to c line) (X = a, b, c), the average value I _{daytime_ave} can be calculated. Further, for example, the correlation analysis unit 714 refers to the night data DB 900 illustrated in FIG. 11 to obtain the average of the current values I _{X night_filter} (t) of each line (a line to c line) (X = a, b, c), the average value I _{night_ave} can be calculated.

Here, it can be said that as the ratio R1 and the ratio R2 are larger, the value of “feature of power under the transformer / amount of noise to be extracted from sensor data” is larger, and the determination accuracy of the connection phase of the transformer Ti is improved. Therefore, the correlation analysis unit 714, for example, the larger the value of "R1 / R2", as the weight k _daylight increases, the weight k _day, may be to determine the _night k.

However, the weight k _day and k _night may be preset and stored. For example, the case of not using the data of the daytime time zone to determine the connection phase of the transformer Ti is the weight k _{noon, night} k may be set as "k _day = 0, k _Night = 1". Assuming that the more consumers C who have installed solar power under the transformer, the more reliable the data in the night time zone than the data in the day time zone, the weights k are set to _day and _night . You may decide to do it. For example, if the ratio of the number of consumers C that have already installed solar power generation to the number of customers C that have not yet installed solar power generation is “1: 1”, the data in the night time zone is twice as reliable. Assuming that it is possible, the weight k _day and k _night may be set as “k _day = 1/3, k _night = 2/3”.

Based on the calculated correlation values ρ (I _a , P _i ), ρ (I _b , P _i ), ρ (I _c , P _i ) of each line (line a to line c), Two distribution lines connected to the transformer Ti among the a line to the c line are determined. Specifically, for example, the connection phase determination unit 715 has the smallest correlation among the correlation values ρ (I _a , P _i ), ρ (I _b , P _i ), and ρ (I _c , P _i ). From this, the connection phase of the transformer Ti is determined.

  If it demonstrates in detail, the connection phase determination part 715 will determine the phase corresponding to the type | formula which satisfy | fills conditions satisfy | filled among following formula (10)-(12) as a connection phase.

ρ (I _c , P _i ) ≦ ρ (I _a , P _i ), ρ (I _b , P _i ) (10)
ρ (I _a , P _i ) ≦ ρ (I _b , P _i ), ρ (I _c , P _i ) (11)
ρ (I _b , P _i ) ≦ ρ (I _c , P _i ), ρ (I _a , P _i ) (12)

  For example, when the above formula (10) is satisfied, the connection phase determination unit 715 determines the connection phase of the transformer Ti as the ab phase. Moreover, when satisfy | filling said Formula (11), the connection phase determination part 715 determines the connection phase of transformer Ti as a bc phase. Moreover, when satisfy | filling said Formula (12), the connection phase determination part 715 determines the connection phase of transformer Ti as ca phase.

  The determined connection phase of the transformer Ti is set, for example, in the connection phase field of the distribution network equipment data 600 shown in FIG. For example, when the connection phase of the transformer T1 is determined to be the ab phase, “ab phase” is set in the connection phase field of the distribution network facility information 600-1.

  The output unit 716 outputs information indicating the determined connection phase of the transformer Ti. Specifically, for example, the output unit 716 may refer to the distribution network facility data 600 and output information indicating the connection phases of the transformers T1 to Tn. Examples of the output format of the output unit 716 include storage in a storage device such as the memory 302 and the disk 304, display on the display 306, print output to a printer (not shown), and transmission to an external device via the I / F 305. .

Incidentally, in the above description, the first correlation value [rho _noon (I _X, P _i) and the second correlation value [rho _night (I _X, P _i) by obtaining a weighted average of the correlation values [rho (I _X , P _i ) is calculated, but the present invention is not limited to this. For example, correlation analysis unit 714, based on the total power value P _{i night _FILTER} total power value P _{i daytime _FILTER} (t) and the time t the evening hours of the time t daytime time zone (t) The correlation value ρ (I _X , P _i ) may be directly calculated.

(Transformer connection phase determination processing procedure of the transformer connection phase determination device 101)
Next, the transformer connection phase determination processing procedure of the transformer connection phase determination device 101 will be described.

  FIG. 15 is a flowchart illustrating an example of the transformer connection phase determination processing procedure of the transformer connection phase determination device 101. In the flowchart of FIG. 15, first, the transformer connection phase determination device 101 acquires customer power data 500 indicating the power value measured by each smart meter SM in the distribution network EN (step S1501).

  Next, the transformer connection phase determination device 101 acquires sensor line current data 400 indicating the current value of each line (line a to line c) measured by the sensor SE in the distribution network EN (step S1502). Then, the transformer connection phase determination device 101 refers to the acquired sensor line current data 400 to determine the current value of each line (line a to line c) as the current value during the daytime period and the current value during the nighttime period. (Step S1503).

Next, the transformer connection phase determination apparatus 101 applies a filter for attenuating the low frequency component to the classified current value in the daytime period and the current value in the nighttime period (step S1504). Then, the transformer connection phase determination apparatus 101 calculates the weights k _day and k _night based on the number N of measurement points included in the _daytime period and the number N _{nights of} measurement points included in the night time period. Determination is made (step S1505).

  Next, the transformer connection phase determination apparatus 101 sets “i” of the transformer Ti to “i = 1” (step S1506). Then, the transformer connection phase determination apparatus 101 refers to the distribution network facility data 600 and selects the transformer Ti in the distribution network EN (step S1507).

  Next, the transformer connection phase determination device 101 executes connection phase determination processing for determining the connection phase of the selected transformer Ti (step S1508). Note that a specific processing procedure of the connection phase determination process will be described later with reference to FIG.

  Next, the transformer connection phase determination apparatus 101 increments “i” of the transformer Ti (step S1509), and determines whether “i” is larger than “n” (step S1510). Here, when “i” of the transformer Ti is equal to or less than “n” (step S1510: No), the transformer connection phase determination apparatus 101 returns to step S1507.

  On the other hand, when “i” is larger than “n” (step S1510: Yes), the transformer connection phase determination apparatus 101 outputs information indicating the connection phase of each transformer T1 to Tn in the distribution network EN (step S1511). ), A series of processes according to this flowchart is terminated.

  Thereby, the connection phase of each transformer T1-Tn in the power distribution network EN can be automatically determined.

  Next, a specific processing procedure of the connection phase determination processing in step S1508 shown in FIG. 15 will be described.

  FIG. 16 is a flowchart illustrating an example of a specific processing procedure of the connection phase determination process. In the flowchart of FIG. 16, first, the transformer connection phase determination apparatus 101 refers to the distribution network equipment data 600 and identifies the customer C that has introduced the smart meter under the transformer Ti (step S1601). Next, the transformer connection phase determination device 101 refers to the distribution network equipment data 600 and identifies the customer C that has already been installed with the photovoltaic power generation among the identified customers C that have already been introduced with the smart meter (step S1602). .

  Then, the transformer connection phase determination apparatus 101 refers to the customer power data 500, and calculates the power value of the customer C who has introduced the smart meter under the transformer Ti, the power value of the day time zone and the night time zone. The power value is classified (step S1603). Next, the transformer connection phase determination apparatus 101 applies a filter for attenuating a low frequency component to the classified power value in the daytime period and the power value in the nighttime period (step S1604).

Then, the transformer connection phase determination device 101 refers to the power value of the customer C at each time t in the daytime period after the filter application, and the customer C of the solar power generation not introduced connected to the transformer Ti. The total power value P _{i_day_filter} (t) at each time t in the daytime time zone is calculated (step S1605).

Next, the transformer connection phase determination device 101 refers to the power value of the customer C at each time t in the night time zone after applying the filter, and the night time zone of all the consumers C connected to the transformer Ti. The total power value P _{i_night_filter} (t) at each time t is calculated (step S1606).

The transformer connection phase determination apparatus 101 then calculates the total power value P _{i_day_filter} (t) at each time t in the calculated daytime time zone and each time t in the daytime time zone of each line (line a to line c). The first correlation value ρ _daytime (I _X , P _i ) is calculated based on the current value I _{X daytime_filter} (t) after application of the filter (step S1607). However, X is any one of a, b, and c.

Next, the transformer connection phase determination apparatus 101 calculates the total power value P _{i_night_filter} (t) at each time t in the calculated night time zone and each time in the night time zone of each line (a line to c line). A second correlation value ρ _night (I _X , P _i ) is calculated based on the current value I _{X night_filter} (t) after the t filter is applied (step S1608). However, X is any one of a, b, and c.

Then, trans-connected phase determination unit 101 _day weight k determined in step S1505 shown in FIG. 15, using _night k, calculated first correlation value ρ _noon (I _X, P _i) and the second The correlation value ρ (I _X , P _i ) is calculated by obtaining a weighted average with the correlation value ρ _night (I _X , P _i ) (step S1609). However, X is any one of a, b, and c.

Next, the transformer connection phase determination apparatus 101 determines the connection phase of the transformer Ti based on the calculated correlation value ρ (I _X , P _i ) (step S1610), and the step of calling the connection phase determination process is performed. Return. Thereby, two distribution lines connected to transformer Ti among a line -c line in distribution network EN can be judged.

  In the above description, the total power value at each time t in the day / night time zone is calculated after applying the filter to the power value in the day / night time zone. However, the present invention is not limited to this. . For example, the transformer connection phase determination apparatus 101 may apply the filter after calculating the total power value at each time t in the day / night time zone.

(Verification of effects of transformer connection phase judgment method)
Next, verification of the effect of the transformer connection phase determination method according to the embodiment will be described using the distribution network 1700 shown in FIG.

FIG. 17 is an explanatory diagram illustrating a configuration example of the power distribution network 1700. In FIG. 17, a distribution network 1700 is a three-phase three-wire distribution network, and includes a sensor SE, a plurality of transformers T (for example, transformers T1 to T3), and a plurality of customers C (for example, a customer C _11). , C ₁₂ , C ₂₁ , C ₂₂ , C ₃₁ , C ₃₂ ). The connection phase of the transformer T1 is the ab phase. The connection phase of the transformer T2 is the bc phase. The connection phase of the transformer T3 is the ca phase.

Here, the power values at time t of the consumers C ₁₁ , C ₁₂ , C ₂₁ , C ₂₂ , C ₃₁ , C ₃₂ under the transformers T1 to T3 are defined as follows. However, C ₁₂ , C ₂₂ , and C ₃₂ are customers C who have already introduced solar power generation. Further, P _sun (t) is generated power by solar power generation. The time t is “t = 1, 2,..., 17520”.

_Customer C ₁₁ : P _{11 — filter} (t) = X ₁₁ (t)
_Customer C ₁₂ : P _{12_filter} (t) = X ₁₂ (t) + P _sun (t)
_Customer C ₂₁ : P _{21_filter} (t) = X ₂₁ (t)
_Customer C ₂₂ : P _{22 — filter} (t) = X ₂₂ (t) + P _sun (t)
Consumer _{C 31: P 31_filter (t)} = X 31 (t)
_Customer C ₃₂ : P _{32 — filter} (t) = X ₃₂ (t) + P _sun (t)

For the “other” part, the power value at time t of the customer C under the transformer connected to the ab phase is defined as “P _{ab_filter} (t) = 50X _ab (t) + sP _sun (t)”. Further, the power value at time t of the customer C under the transformer connected to the bc phase is defined as “P _{bc_filter} (t) = 50 × _bc (t) + sP _sun (t)”.

Further, the power value at time t of the customer C under the transformer connected to the ca phase is defined as “P _ca — _filter (t) = 50 × _ca (t) + 10P _sun (t)”. However, the coefficient s is a value for adjusting the ratio of the customer C introducing solar power generation in each phase (ab phase, bc phase, ca phase) of the “others” portion.

Here, as an assumption of data, it is assumed that X _** (t) independently follows U (−500,500) for an arbitrary time t and an arbitrary **. However, U (−500, 500) is a random variable that randomly takes a value in the range of −500 to 500, and represents a uniform distribution. Also, P _sun (t) is a value that takes U (−500,500) at daytime and takes “0” at nighttime. The current of the sensor SE is assumed to be an ideal transformer having no line loss and the transformer T having the same turn ratio.

  The correct answer rate when the connected phases of the transformers T1 to T3 are determined based on the respective power values P when such an assumption is made is as shown in FIG.

  FIG. 18 is an explanatory diagram showing the correct answer rate of the transformer connection phase determination result. In FIG. 18, (Method 1) shows the correct answer rate when the connected phases of the transformers T1 to T3 are determined without distinguishing the day / night time zones. That is, (Method 1) is an example in which the connection phase of the transformers T1 to T3 is determined simply using the power value and the current value after applying the filter.

(Method 2), in this transformer connected phase determination method, the correct answer rate in the case of determining the connection phase of the transformer T1~T3 when the weight k _noon, k _night and "k _day = 0, k _Night = 1" Indicates. In (Method 3), in this transformer connection phase determination method, the connection phases of the transformers T1 to T3 when the weight k _day and k _night are “k _day = 1/3, k _night = 2/3” are determined. The correct answer rate is shown.

  As shown in FIG. 18, it is understood that (Method 2) and (Method 3) can ensure the connection phase determination accuracy even if the value of the coefficient s changes. For example, in (Method 1), when the balance of the number of consumers C introducing solar power generation is lost in each phase (ab phase, bc phase, ca phase) of the “others” portion, the determination accuracy On the other hand, in (Method 2) and (Method 3), determination can be made with high accuracy.

  As described above, according to the transformer connection phase determination apparatus 101 according to the embodiment, referring to the consumer power data 500, the power value of each consumer C can be calculated using the power value in the daytime period and the night time. It can be classified into the power value of the time zone. Thereby, the electric power value of each customer C can be classified into the electric power value which should consider the influence of photovoltaic power generation, and the electric power value which does not need to consider the influence of photovoltaic power generation.

  Further, according to the transformer connection phase determination device 101, the current value of each line (line a to line c) is determined by referring to the sensor line current data 400, the current value during the daytime period, and the current during the nighttime period. Can be classified into values. Thereby, the electric current value of each line (a line-c line) can be classified according to the electric power value of each consumer C.

  Moreover, according to the transformer connection phase determination apparatus 101, a filter that attenuates a low-frequency component can be applied to the current value of each line in the day / night time zone and the power value of each customer C. As a result, the low frequency component can be attenuated and a fine change (high frequency component) can be passed to remove a common component unnecessary for the correlation analysis.

Moreover, according to the transformer connection phase determination device 101, the total power value P _{i_daytime} at each time t in the daytime period of the customer C that has not yet introduced solar power generation among the consumers C connected to the transformer Ti. _{_Filter} (t) can be calculated. As a result, the total power value P _{i —} obtained by removing the power value including common components that are unnecessary for the correlation analysis due to the influence of solar power generation as the total power value of the daytime hours consumed by the customer C under the transformer. _{Daytime_filter} (t) can be calculated.

Further, according to the transformer connection phase determination device 101, the total power value P _{i_night_filter} (t) at each time t in the night time zone of all the consumers C connected to the transformer Ti can be calculated. Thereby, the electric power value of the night time zone which is not influenced by solar power generation can be utilized to the maximum extent.

Moreover, according to the transformer connection phase determination apparatus 101, the total power value P _{i daytime_filter} (t) at each time t in the daytime period and each time t in the daytime period of each line (line a to line c). current I _X based on _{daytime _filter} (t), the first correlation value ρ _noon (I _X, P _i) can be calculated (X = a, b, c ). Thereby, the correlation between the electric power and the current during the daytime period can be analyzed. In addition, since the total power value P _{i_daytime_filter} (t) and the current value I _{X daytime_filter} (t) after applying the filter are used, the occurrence of pseudo correlation can be suppressed.

Moreover, according to the transformer connection phase determination apparatus 101, the total power value P _{i_night_filter} (t) at each time t in the night time zone and each time t in the night time zone of each line (line a to c). The second correlation value ρ _night (I _X , P _i ) can be calculated based on the current value I _{X night_filter} (t) (X = a, b, c). Thereby, it is possible to analyze the correlation between the power and the current in the night time zone. Moreover, since the total power value P _{i_night_filter} (t) and the current value I _{X night_filter} (t) after applying the filter are used, the occurrence of pseudo correlation can be suppressed.

Further, according to the transformer connection phase determination unit 101, a first correlation value ρ _noon (I _X, P _i) and the second correlation value ρ _night (I _X, P _i) by obtaining a weighted average of the, Correlation values ρ (I _X , P _i ) can be calculated (X = a, b, c). Then, the transformer connection phase determination device 101 can determine the connection phase of the transformer Ti based on the correlation value ρ (I _X , P _i ).

  As a result, since the power values of all the consumers C under the transformer are reflected, the connection phase determination is performed with a greater weight on the night time zone that can be said to characterize the total demand under the transformer well. It is possible to improve the determination accuracy of the connection phase of the transformer Ti.

Moreover, according to the transformer connection phase determination apparatus 101, the weight k _day and k _night are determined based on the number N of measurement points in the _daytime period and the number N _night of measurement points in the night time period. be able to. As a result, the connection phase determination can be performed by weighting the time zone where the number of data (measurement points) is large, and the determination accuracy of the connection phase of the transformer Ti can be improved.

Further, according to the transformer connection phase determination apparatus 101, the weight k _{daytime is calculated} based on the average value P _{i_day_ave} of the power value in the _{daytime period} and the average value P _{i_night_ave} of the power value in the night time period. , K _nights can be determined. As a result, since the average value of the total power value of the customer C is large, the connection phase determination can be performed by weighting the time zone in which it can be said that the total demand under the transformer is well characterized. It is possible to improve the determination accuracy of the connected phase.

Further, according to the transformer connection phase determination unit 101, the average value P _{i_ noon} ratio of _{_ave} R1 to the average value I _{day _ave} the daytime current time zone, the average value I _night the current value of the night time zone _{_ave} The weight k _day and k _night can be determined based on the average value P _{i_night_ave} ratio R2 with respect to. As a result, the connection phase determination can be performed by weighting the time zone in which the characteristics of the power under the transformer to be extracted from the sensor data increase with respect to the amount of noise in the entire distribution network, and the connection phase of the transformer Ti. The determination accuracy can be improved.

  Therefore, according to the transformer connection phase determination device 101, even if the customer C who has introduced solar power generation is included in the customer C under the transformer in the distribution network EN, the connection phase of the transformer Ti Can be determined with high accuracy.

  The transformer connection phase determination method described in the present embodiment can be realized by executing a program prepared in advance on a computer such as a personal computer or a workstation. The transformer connection phase determination program is recorded on a computer-readable recording medium such as a hard disk, a flexible disk, a CD-ROM, an MO, and a DVD, and is executed by being read from the recording medium by the computer. Further, the transformer connection phase determination program may be distributed through a network such as the Internet.

  The following additional notes are disclosed with respect to the embodiment described above.

(Supplementary note 1)
Of the power values in each time zone within the measurement period consumed by the consumer connected to the transformer connected to any two of the three or more distribution lines, it is a consumer with solar power generation introduced Obtain the power value for each time period excluding the power value for the specific time period consumed,
For each distribution line of the three or more distribution lines, based on the acquired power value of each time zone and the current value of each time zone flowing through each of the distribution lines, the correlation between power and current Calculate a correlation value indicating
Based on the calculated correlation value of each distribution line, two distribution lines connected to the transformer among the three or more distribution lines are determined.
A transformer connection phase determination program characterized in that a process is executed.

(Supplementary note 2)
For each distribution line, based on the power value of the specific time zone among the acquired power values of the time zone and the current value of the specific time zone of the current value of each time zone. Calculating a first correlation value indicating a correlation between electric power and current,
For each distribution line, the power value in another time zone different from the specific time zone among the acquired power values in each time zone, and the other time in the current value in each time zone. Calculating a second correlation value indicating a correlation between the power and the current based on the current value of the belt,
The process of calculating the correlation value includes:
The transformer connection phase according to appendix 1, wherein the correlation value is calculated by obtaining a weighted average of the calculated first correlation value and the second correlation value for each distribution line. Judgment program.

(Supplementary Note 3) The process of calculating the correlation value is as follows:
Based on the number of power or current measurement points included in the specific time zone and the number of power or current measurement points included in the other time zone, the first correlation value and the second The transformer connection phase determination program according to appendix 2, wherein the correlation value is calculated by obtaining a weighted average with the correlation value.

(Supplementary Note 4) The process of calculating the correlation value is as follows:
By calculating a weighted average of the first correlation value and the second correlation value based on the average value of the power values in the specific time zone and the average value of the power values in the other time zones. The transformer connection phase determination program according to appendix 2, wherein the correlation value is calculated.

(Supplementary Note 5) The process of calculating the correlation value is as follows:
The ratio of the average value of the power value of the specific time zone to the average value of the current value of the specific time zone, and the average value of the power value of the other time zone to the average value of the current value of the other time zone The transformer connection phase determination program according to appendix 2, wherein the correlation value is calculated by obtaining a weighted average of the first correlation value and the second correlation value based on a ratio of .

(Appendix 6)
A process of applying a filter for attenuating a low-frequency component is performed on the acquired power value of each time zone and the current value of each time zone,
The process of calculating the correlation value includes:
For each distribution line, a correlation value indicating a correlation between power and current is calculated based on a power value in each time zone to which the filter is applied and a current value in each time zone. The transformer connection phase determination program according to any one of appendices 1 to 5.

(Appendix 7) The computer
Of the power values in each time zone within the measurement period consumed by the consumer connected to the transformer connected to any two of the three or more distribution lines, it is a consumer with solar power generation introduced Obtain the power value for each time period excluding the power value for the specific time period consumed,
For each distribution line of the three or more distribution lines, based on the acquired power value of each time zone and the current value of each time zone flowing through each of the distribution lines, the correlation between power and current Calculate a correlation value indicating
Based on the calculated correlation value of each distribution line, two distribution lines connected to the transformer among the three or more distribution lines are determined.
A transformer connection phase determination method characterized by executing processing.

(Supplementary note 8) Of the power values in each time zone within the measurement period consumed by the consumer connected to the transformer connected to any two of the three or more distribution lines, solar power generation was introduced Obtain the power value of each time period excluding the power value of a specific time period consumed by a consumer,
For each distribution line of the three or more distribution lines, based on the acquired power value of each time zone and the current value of each time zone flowing through each of the distribution lines, the correlation between power and current Calculate a correlation value indicating
Based on the calculated correlation value of each distribution line, two distribution lines connected to the transformer among the three or more distribution lines are determined.
A transformer connection phase determination device comprising a control unit.

(Appendix 9)
Of the power values in each time zone within the measurement period consumed by the consumer connected to the transformer connected to any two of the three or more distribution lines, it is a consumer with solar power generation introduced Obtain the power value for each time period excluding the power value for the specific time period consumed,
For each distribution line of the three or more distribution lines, based on the acquired power value of each time zone and the current value of each time zone flowing through each of the distribution lines, the correlation between power and current Calculate a correlation value indicating
Based on the calculated correlation value of each distribution line, two distribution lines connected to the transformer among the three or more distribution lines are determined.
A computer-readable recording medium having a transformer connection phase determination program for executing processing recorded therein.

DESCRIPTION OF SYMBOLS 101 Transformer connection phase determination apparatus 200 Transformer connection phase determination system 400 Sensor line current data 500 Customer power data 600 Distribution network equipment data 701 Control unit 702 Storage unit 711 Acquisition unit 712 Data classification unit 713 Filter application unit 714 Correlation analysis unit 715 Connection Phase determination unit 716 Output unit 800 Daytime data DB
900 Night Data DB
1200 Total power table for daytime 1300 Total power table for nighttime 1400 Correlation value table 1700, EN Distribution network C Consumer SE Sensor SM Smart meter SU Substation

Claims (8)

On the computer,
Of the power values in each time zone within the measurement period consumed by the consumer connected to the transformer connected to any two of the three or more distribution lines, it is a consumer with solar power generation introduced Obtain the power value for each time period excluding the power value for the specific time period consumed,
For each distribution line of the three or more distribution lines, based on the acquired power value of each time zone and the current value of each time zone flowing through each of the distribution lines, the correlation between power and current Calculate a correlation value indicating
Based on the calculated correlation value of each distribution line, two distribution lines connected to the transformer among the three or more distribution lines are determined.
A transformer connection phase determination program characterized in that a process is executed. In the computer,
For each distribution line, based on the power value of the specific time zone among the acquired power values of the time zone and the current value of the specific time zone of the current value of each time zone. Calculating a first correlation value indicating a correlation between electric power and current,
For each distribution line, the power value in another time zone different from the specific time zone among the acquired power values in each time zone, and the other time in the current value in each time zone. Calculating a second correlation value indicating a correlation between the power and the current based on the current value of the belt,
The process of calculating the correlation value includes:
The transformer connection according to claim 1, wherein the correlation value is calculated by obtaining a weighted average of the calculated first correlation value and the second correlation value for each distribution line. Phase judgment program. The process of calculating the correlation value includes:
Based on the number of power or current measurement points included in the specific time zone and the number of power or current measurement points included in the other time zone, the first correlation value and the second The transformer connection phase determination program according to claim 2, wherein the correlation value is calculated by calculating a weighted average with the correlation value. The process of calculating the correlation value includes:
By calculating a weighted average of the first correlation value and the second correlation value based on the average value of the power values in the specific time zone and the average value of the power values in the other time zones. The transformer connection phase determination program according to claim 2, wherein the correlation value is calculated. The process of calculating the correlation value includes:
The ratio of the average value of the power value of the specific time zone to the average value of the current value of the specific time zone, and the average value of the power value of the other time zone to the average value of the current value of the other time zone The transformer connection phase determination according to claim 2, wherein the correlation value is calculated by obtaining a weighted average of the first correlation value and the second correlation value based on a ratio of the first correlation value and the second correlation value. program. In the computer,
A process of applying a filter for attenuating a low-frequency component is performed on the acquired power value of each time zone and the current value of each time zone,
The process of calculating the correlation value includes:
For each distribution line, a correlation value indicating a correlation between power and current is calculated based on a power value in each time zone to which the filter is applied and a current value in each time zone. The transformer connection phase determination program according to any one of claims 1 to 5. Computer
Of the power values in each time zone within the measurement period consumed by the consumer connected to the transformer connected to any two of the three or more distribution lines, it is a consumer with solar power generation introduced Obtain the power value for each time period excluding the power value for the specific time period consumed,
For each distribution line of the three or more distribution lines, based on the acquired power value of each time zone and the current value of each time zone flowing through each of the distribution lines, the correlation between power and current Calculate a correlation value indicating
Based on the calculated correlation value of each distribution line, two distribution lines connected to the transformer among the three or more distribution lines are determined.
A transformer connection phase determination method characterized by executing processing. Of the power values in each time zone within the measurement period consumed by the consumer connected to the transformer connected to any two of the three or more distribution lines, it is a consumer with solar power generation introduced Obtain the power value for each time period excluding the power value for the specific time period consumed,
For each distribution line of the three or more distribution lines, based on the acquired power value of each time zone and the current value of each time zone flowing through each of the distribution lines, the correlation between power and current Calculate a correlation value indicating
Based on the calculated correlation value of each distribution line, two distribution lines connected to the transformer among the three or more distribution lines are determined.
A transformer connection phase determination device comprising a control unit.

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