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CN112990678A - Icing early warning judgment method based on multi-source data fusion - Google Patents
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CN112990678A - Icing early warning judgment method based on multi-source data fusion - Google Patents

Icing early warning judgment method based on multi-source data fusion Download PDF

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CN112990678A
CN112990678A CN202110241698.6A CN202110241698A CN112990678A CN 112990678 A CN112990678 A CN 112990678A CN 202110241698 A CN202110241698 A CN 202110241698A CN 112990678 A CN112990678 A CN 112990678A
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甄超
夏令志
季坤
刘宇舜
程洋
郑浩
朱太云
操松元
严波
刘静
方登洲
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Electric Power Research Institute of State Grid Anhui Electric Power Co Ltd
State Grid Anhui Electric Power Co Ltd
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Abstract

本发明公开了基于多源数据融合的覆冰预警判定方法,本发明涉及覆冰预警技术领域,解决了现有技术中不能够对各个区域覆冰可能性进行预测导致预警的准确性降低,通过覆冰预测单元对环境信息进行分析,从而对各个区域是否覆冰进行预测,将待检测电路线路所在的区域划分为若干个子区域,获取到环境信息,通过公式获取到子区域的覆冰预测系数Xi,若子区域的覆冰预测系数Xi≥子区域的覆冰预测系数阈值,则将对应子区域标记为覆冰子区域,获取到冷空气的风向,随后根据冷空气抵达子区域的时间先后顺序,将各个覆冰子区域进行排序,对各个区域覆冰进行预测,同时对区域覆冰的时间先后顺序进行判定,提高预警的准确性能。

Figure 202110241698

The invention discloses an icing early warning determination method based on multi-source data fusion, relates to the technical field of icing early warning, and solves the problem that the accuracy of early warning is reduced due to the inability to predict the possibility of icing in each area in the prior art. The icing prediction unit analyzes the environmental information to predict whether each area is icing, divides the area where the circuit to be detected is located into several sub-regions, obtains the environmental information, and obtains the icing prediction coefficient of the sub-region through the formula Xi, if the icing prediction coefficient Xi of the sub-region is ≥ the icing prediction coefficient threshold of the sub-region, the corresponding sub-region is marked as the icing sub-region, and the wind direction of the cold air is obtained, and then the cold air arrives in the sub-region according to the time sequence of the arrival of the sub-region. , sort each icing sub-region, predict icing in each region, and judge the temporal sequence of regional icing to improve the accuracy of early warning.

Figure 202110241698

Description

Icing early warning judgment method based on multi-source data fusion
Technical Field
The invention relates to the technical field of icing early warning, in particular to an icing early warning judgment method based on multi-source data fusion.
Background
The ice coating of the transmission line not only can influence the operation and maintenance work, but also can cause serious accidents such as line-touching short circuit, insulator flashover, short-line tower falling and the like in serious cases. The ice damage of the power transmission line has the characteristics of long duration, high occurrence frequency, large coverage area, wide influence range and the like, and seriously threatens the safe and stable operation and the power supply reliability of the power grid. Factors that affect ice coating on the lines are many. The icing condition of the cable cannot be effectively monitored in real time in the ordinary manual inspection or helicopter inspection, and once the high-voltage transmission line is excessively iced, the cable cannot be effectively deiced, so that the consequences are beyond the assumption.
However, in the prior art, the icing possibility of each area cannot be predicted, so that the accuracy of early warning is reduced.
Disclosure of Invention
The invention aims to provide an icing early warning judgment method based on multi-source data fusion, which comprises the steps of analyzing power line data in each area through a circuit monitoring unit, monitoring power lines of icing subareas to obtain the power line data, obtaining power line analysis coefficients JCo of the icing subareas through a formula, judging that the power lines of the corresponding icing subareas are abnormal if the power line analysis coefficients JCo of the icing subareas are not less than a power line analysis coefficient threshold value, generating a line abnormal signal, sending the line abnormal signal and the corresponding icing subareas to an early warning management platform, and generating a line maintenance signal and sending the line maintenance signal to a mobile phone terminal of a maintenance worker after the early warning management platform receives the line abnormal signal; and the abnormal line is early-warned, so that the influence of the line abnormality on the power utilization is reduced.
The purpose of the invention can be realized by the following technical scheme:
an icing early warning judgment method based on multi-source data fusion specifically comprises the following steps:
step one, registering and logging, wherein a manager and a maintenance worker register and log in through a mobile phone terminal;
secondly, ice coating prediction is carried out, and environment information is analyzed through an ice coating prediction unit, so that whether each area is coated with ice or not is predicted;
step three, circuit monitoring, namely analyzing the power line data of each ice-coated subregion through a circuit monitoring unit so as to monitor the power line of each ice-coated subregion;
analyzing road conditions, namely analyzing the road information to which the electric power line of each ice-coated subregion belongs through a road condition analysis unit, so as to detect the road to which the electric power line of each ice-coated subregion belongs;
step five, power dispatching, namely analyzing the region information through a power dispatching unit so as to select a proper region for power dispatching;
the ice coating prediction unit in the second step is used for analyzing the environment information so as to predict whether each region is coated with ice, the environment information comprises temperature data, humidity data and wind speed data, the temperature data is the maximum temperature change value of each sub-region all day, the humidity data is the average humidity value of each sub-region all day, the wind speed data is the wind speed change value of each sub-region all day per hour, and the specific analysis and prediction process is as follows:
step S1: dividing the area where the circuit line to be detected is located into a plurality of sub-areas, and then marking the sub-areas as i, i is 1, 2, … …, n, n is a positive integer;
step S2: acquiring the maximum temperature change value of the whole day in each sub-area, and marking the maximum temperature change value of the whole day in each sub-area as WBi;
step S3: acquiring the average humidity value of all days in each sub-area, and marking the average humidity value of all days in each sub-area as PSi;
step S4: acquiring the whole-day and every-hour wind speed change value of each sub-region, and marking the whole-day and every-hour wind speed change value of each sub-region as FBi;
step S5: by the formula
Figure BDA0002962447010000031
Acquiring an icing prediction coefficient Xi of a subregion, wherein a1, a2 and a3 are proportional coefficients, a1 is larger than a2 and larger than a3 and larger than 0, and beta is an error correction factor and is 2.36521;
step S6: comparing the icing prediction coefficient Xi of the sub-area with an icing prediction coefficient threshold of the sub-area:
if the icing prediction coefficient Xi of the sub-region is larger than or equal to the icing prediction coefficient threshold of the sub-region, marking the corresponding sub-region as an icing sub-region, generating an icing prediction signal and sending the icing prediction signal and the icing sub-region to an early warning management platform;
if the icing prediction coefficient Xi of the sub-region is smaller than the icing prediction coefficient threshold of the sub-region, marking the corresponding sub-region as an icing-free sub-region, generating an icing-free prediction signal and sending the icing-free prediction signal and the icing-free sub-region to an early warning management platform;
step S7: the method comprises the steps of obtaining the wind direction of cold air, sequencing all ice-coated subareas according to the time sequence of the cold air reaching the subareas, and sending the sequenced ice-coated subareas to a mobile phone terminal of a manager.
Further, the road condition analysis unit is configured to analyze the road information to which the power line of each ice-coating sub-area belongs, so as to detect the road condition of the ice-coating sub-area, where the road information to which the power line of each ice-coating sub-area belongs includes quantity data, speed data, and frequency data, the quantity data is the average number of vehicles passing through the power line around the ice-coating sub-area all day, the speed data is the average speed of vehicles passing through the power line around the ice-coating sub-area all day, the frequency data is the frequency of vehicles passing through the power line around the ice-coating sub-area all day, the ice-coating sub-area is marked as o, o is 1, 2, … …, m, and m is a positive integer, and the specific analysis and detection process is as:
step SS 1: acquiring the average number of vehicles passing by the power line surrounding roads in the icing sub-area all day, and marking the average number of vehicles passing by the power line surrounding roads in the icing sub-area all day as So;
step SS 2: acquiring the average speed of all-day passing vehicles on the roads around the power line in the icing sub-area, and marking the average speed of all-day passing vehicles on the roads around the power line in the icing sub-area as Vo;
step SS 3: acquiring the all-day vehicle passing frequency of the roads around the power line in the ice-covered sub-area, and marking the all-day vehicle passing frequency of the roads around the power line in the ice-covered sub-area as Po;
step SS 4: by the formula Xo ═ e (So × b1+ Vo × b2+ Po × b3)b1+b2+b3Acquiring a road condition analysis coefficient Xo of an ice-covered subregion, wherein b1, b2 and b3 are proportional coefficients, b1 is greater than b2 and is greater than b3 and is a natural constant;
step SS 5: comparing the road condition analysis coefficient of the ice-coated subarea with a road condition analysis coefficient threshold value:
if the road condition analysis coefficient of the ice-coated subarea is larger than or equal to the road condition analysis coefficient threshold value, judging that vehicle control needs to be carried out on the corresponding ice-coated subarea, generating a vehicle control signal, sending the vehicle control signal and the corresponding ice-coated subarea to an early warning management platform, acquiring the predicted duration of cold air after the early warning management platform receives the vehicle control signal, and then sending the predicted duration and the vehicle control signal to a mobile phone terminal of a manager;
and if the road condition analysis coefficient of the ice-coated subarea is less than the road condition analysis coefficient threshold value, judging that the corresponding ice-coated subarea does not need to implement vehicle control, generating a vehicle no-control signal, and sending the vehicle no-control signal and the corresponding ice-coated subarea to the early warning management platform.
Further, the circuit monitoring unit is configured to analyze power line data in each area, so as to monitor the power line in the ice-coated subregion, where the power line data includes sag of wires in the power line, a relative safety distance between the wires in the power line, and a shearing force applied to a connection point in the power line, and the specific analysis and monitoring process is as follows:
step T1: acquiring the sag of a wire in the power line, and marking the sag of the wire in the power line as CDo;
step T2: acquiring a relative safe distance between leads in the power circuit, and marking the relative safe distance between the leads in the power circuit as JLO;
step T3: acquiring the shearing force applied to the connection point in the power line, and marking the shearing force applied to the connection point in the power line as JQo;
step T4: by the formula
Figure BDA0002962447010000051
Acquiring power line analysis coefficients JCo of each ice coating subregion, wherein v1, v2 and v3 are proportionality coefficients, and v1 is more than v2 is more than v3 is more than 0;
step T5: comparing the power line analysis coefficient JCo for each ice coating subregion to a power line analysis coefficient threshold:
if the power line analysis coefficient JCo of each ice-coated subregion is larger than or equal to the power line analysis coefficient threshold value, judging that the power line corresponding to the ice-coated subregion is abnormal, generating a line abnormal signal and sending the line abnormal signal and the corresponding ice-coated subregion to an early warning management platform, and after receiving the line abnormal signal, generating a line maintenance signal and sending the line maintenance signal to a mobile phone terminal of a maintenance worker by the early warning management platform;
and if the power line analysis coefficient JCo of each ice-coated subregion is less than the power line analysis coefficient threshold value, judging that the power line corresponding to the ice-coated subregion is normal, generating a normal line signal and sending the normal line signal and the corresponding ice-coated subregion to the early warning management platform.
Further, the power scheduling unit is configured to analyze the region information, so as to select a suitable region for power scheduling, where a specific analysis and selection process is as follows:
step TT 1: acquiring an icing subarea corresponding to the line abnormity, marking the corresponding icing subarea as a maintenance area, and then acquiring area information of the maintenance area and a peripheral area, wherein the area information comprises the spacing distance between the maintenance area and the peripheral area, the average power consumption of the peripheral area of the maintenance area all day and the sum of the number of residents and factories in the peripheral area of the maintenance area;
step TT 2: acquiring the spacing distance between a maintenance area and the peripheral area, the average electricity consumption of the peripheral area of the maintenance area all day and the sum of the number of residents and the number of factories in the peripheral area of the maintenance area, and respectively marking the spacing distance between the maintenance area and the peripheral area, the average electricity consumption of the peripheral area of the maintenance area all day and the number of residents and factories in the peripheral area of the maintenance area as JL, DL and ZH;
step TT 3: by the formula
Figure BDA0002962447010000061
Obtaining a scheduling coefficient DD of a peripheral area of the maintenance area, wherein s1, s2 and s3 are all proportionality coefficients, and s1 > s2>s3>0;
Step TT 4: sequencing the peripheral areas of the maintenance areas according to the sequence of the scheduling coefficients from large to small, marking the peripheral area with the first sequence as a scheduling selected area, simultaneously marking the peripheral area with the second sequence as a scheduling alternative area, and then sending the scheduling selected area and the scheduling alternative area to a mobile phone terminal of a manager.
Further, the registration login unit in the first step is used for the manager and the maintainer to submit the manager information and the maintainer information through the mobile phone terminal for registration, and the manager information and the maintainer information which are successfully registered are sent to the database for storage, the manager information comprises the name, the age, the time of entry and the mobile phone number of the personal real name authentication, and the maintainer information comprises the name, the age, the time of entry and the mobile phone number of the personal real name authentication.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, the environmental information is analyzed through the icing prediction unit, so that whether each area is iced or not is predicted, the area where a circuit to be detected is located is divided into a plurality of sub-areas, and then the sub-areas are marked as i, i is 1, 2, … …, n, and n is a positive integer; obtaining environmental information, obtaining an icing prediction coefficient Xi of a sub-region through a formula, if the icing prediction coefficient Xi of the sub-region is larger than or equal to an icing prediction coefficient threshold of the sub-region, marking the corresponding sub-region as an icing sub-region, generating an icing prediction signal, and sending the icing prediction signal and the icing sub-region to an early warning management platform; acquiring the wind direction of cold air, sequencing the ice-coated subareas according to the time sequence of arrival of the cold air at the subareas, and sending the sequenced ice-coated subareas to a mobile phone terminal of a manager; the icing of each region is predicted, and the time sequence of the icing of the regions is judged at the same time, so that the accuracy of early warning is improved;
2. according to the method, the circuit monitoring unit is used for analyzing the power line data in each area, so that the power lines of the ice-coated subareas are monitored, the power line data are obtained, the power line analysis coefficient JCo of each ice-coated subarea is obtained through a formula, if the power line analysis coefficient JCo of each ice-coated subarea is larger than or equal to the power line analysis coefficient threshold value, the power line corresponding to the ice-coated subarea is judged to be abnormal, a line abnormal signal is generated, the line abnormal signal and the corresponding ice-coated subarea are sent to an early warning management platform, and after the early warning management platform receives the line abnormal signal, a line maintenance signal is generated and sent to a mobile phone terminal of a maintenance worker; and the abnormal line is early-warned, so that the influence of the line abnormality on the power utilization is reduced.
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In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the icing early warning determination method based on multi-source data fusion specifically includes the following steps:
step one, registering and logging, wherein a manager and a maintenance worker register and log in through a mobile phone terminal;
secondly, ice coating prediction is carried out, and environment information is analyzed through an ice coating prediction unit, so that whether each area is coated with ice or not is predicted;
step three, circuit monitoring, namely analyzing the power line data of each ice-coated subregion through a circuit monitoring unit so as to monitor the power line of each ice-coated subregion;
analyzing road conditions, namely analyzing the road information to which the electric power line of each ice-coated subregion belongs through a road condition analysis unit, so as to detect the road to which the electric power line of each ice-coated subregion belongs;
step five, power dispatching, namely analyzing the region information through a power dispatching unit so as to select a proper region for power dispatching;
the method comprises the steps that firstly, a registration login unit is used for a manager and a maintainer to submit manager information and maintainer information through a mobile phone terminal for registration, and the manager information and the maintainer information which are successfully registered are sent to a database for storage, wherein the manager information comprises the name, the age, the time of entry and the mobile phone number of real-name authentication of the manager, and the maintainer information comprises the name, the age, the time of entry and the mobile phone number of real-name authentication of the maintainer;
the icing prediction unit in the second step is used for analyzing the environment information so as to predict whether each area is iced, the environment information comprises temperature data, humidity data and wind speed data, the temperature data is the maximum temperature change value of each sub-area all day, the humidity data is the average humidity value of each sub-area all day, the wind speed data is the wind speed change value of each sub-area all day per hour, and the specific analysis prediction process is as follows:
step S1: dividing the area where the circuit line to be detected is located into a plurality of sub-areas, and then marking the sub-areas as i, i is 1, 2, … …, n, n is a positive integer;
step S2: acquiring the maximum temperature change value of the whole day in each sub-area, and marking the maximum temperature change value of the whole day in each sub-area as WBi;
step S3: acquiring the average humidity value of all days in each sub-area, and marking the average humidity value of all days in each sub-area as PSi;
step S4: acquiring the whole-day and every-hour wind speed change value of each sub-region, and marking the whole-day and every-hour wind speed change value of each sub-region as FBi;
step S5: by the formula
Figure BDA0002962447010000081
Acquiring an icing prediction coefficient Xi of a subregion, wherein a1, a2 and a3 are proportional coefficients, a1 is larger than a2 and larger than a3 and larger than 0, and beta is an error correction factor and is 2.36521;
step S6: comparing the icing prediction coefficient Xi of the sub-area with an icing prediction coefficient threshold of the sub-area:
if the icing prediction coefficient Xi of the sub-region is larger than or equal to the icing prediction coefficient threshold of the sub-region, marking the corresponding sub-region as an icing sub-region, generating an icing prediction signal and sending the icing prediction signal and the icing sub-region to an early warning management platform;
if the icing prediction coefficient Xi of the sub-region is smaller than the icing prediction coefficient threshold of the sub-region, marking the corresponding sub-region as an icing-free sub-region, generating an icing-free prediction signal and sending the icing-free prediction signal and the icing-free sub-region to an early warning management platform;
step S7: acquiring the wind direction of cold air, sequencing the ice-coated subareas according to the time sequence of arrival of the cold air at the subareas, and sending the sequenced ice-coated subareas to a mobile phone terminal of a manager;
the road condition analysis unit is used for analyzing the road information to which the power line of each ice-coating subregion belongs, so as to detect the road condition of the ice-coating subregion, the road information to which the power line of each ice-coating subregion belongs comprises quantity data, speed data and frequency data, the quantity data is the average number of vehicles passing through all day around the power line in the ice-coating subregion, the speed data is the average speed of the vehicles passing through all day around the power line in the ice-coating subregion, the frequency data is the frequency of the vehicles passing through all day around the power line in the ice-coating subregion, the ice-coating subregion is marked as o, o is 1, 2, … …, m, and m is a positive integer, and the specific analysis and detection process is as follows:
step SS 1: acquiring the average number of vehicles passing by the power line surrounding roads in the icing sub-area all day, and marking the average number of vehicles passing by the power line surrounding roads in the icing sub-area all day as So;
step SS 2: acquiring the average speed of all-day passing vehicles on the roads around the power line in the icing sub-area, and marking the average speed of all-day passing vehicles on the roads around the power line in the icing sub-area as Vo;
step SS 3: acquiring the all-day vehicle passing frequency of the roads around the power line in the ice-covered sub-area, and marking the all-day vehicle passing frequency of the roads around the power line in the ice-covered sub-area as Po;
step SS 4: by the formula Xo ═ e (So × b1+ Vo × b2+ Po × b3)b1+b2+b3Acquiring a road condition analysis coefficient Xo of an ice-covered subregion, wherein b1, b2 and b3 are proportional coefficients, b1 is greater than b2 and is greater than b3 and is a natural constant;
step SS 5: comparing the road condition analysis coefficient of the ice-coated subarea with a road condition analysis coefficient threshold value:
if the road condition analysis coefficient of the ice-coated subarea is larger than or equal to the road condition analysis coefficient threshold value, judging that vehicle control needs to be carried out on the corresponding ice-coated subarea, generating a vehicle control signal, sending the vehicle control signal and the corresponding ice-coated subarea to an early warning management platform, acquiring the predicted duration of cold air after the early warning management platform receives the vehicle control signal, and then sending the predicted duration and the vehicle control signal to a mobile phone terminal of a manager;
if the road condition analysis coefficient of the ice-coated subarea is less than the road condition analysis coefficient threshold value, judging that the corresponding ice-coated subarea does not need to implement vehicle control, generating a vehicle non-control signal and sending the vehicle non-control signal and the corresponding ice-coated subarea to the early warning management platform;
the circuit monitoring unit is used for analyzing the electric power circuit data in each area, so that the electric power circuit in the ice-coated subarea is monitored, the electric power circuit data comprise the sag of the wires in the electric power circuit, the relative safe distance between the wires in the electric power circuit and the shearing force applied to the connecting point in the electric power circuit, and the specific analysis and monitoring process comprises the following steps:
step T1: acquiring the sag of a wire in the power line, and marking the sag of the wire in the power line as CDo;
step T2: acquiring a relative safe distance between leads in the power circuit, and marking the relative safe distance between the leads in the power circuit as JLO;
step T3: acquiring the shearing force applied to the connection point in the power line, and marking the shearing force applied to the connection point in the power line as JQo;
step T4: by the formula
Figure BDA0002962447010000111
Acquiring power line analysis coefficients JCo of each ice coating subregion, wherein v1, v2 and v3 are proportionality coefficients, and v1 is more than v2 is more than v3 is more than 0;
step T5: comparing the power line analysis coefficient JCo for each ice coating subregion to a power line analysis coefficient threshold:
if the power line analysis coefficient JCo of each ice-coated subregion is larger than or equal to the power line analysis coefficient threshold value, judging that the power line corresponding to the ice-coated subregion is abnormal, generating a line abnormal signal and sending the line abnormal signal and the corresponding ice-coated subregion to an early warning management platform, and after receiving the line abnormal signal, generating a line maintenance signal and sending the line maintenance signal to a mobile phone terminal of a maintenance worker by the early warning management platform;
if the power line analysis coefficient JCo of each ice-coated subregion is smaller than the power line analysis coefficient threshold value, judging that the power line of the corresponding ice-coated subregion is normal, generating a normal line signal and sending the normal line signal and the corresponding ice-coated subregion to an early warning management platform;
the power scheduling unit is used for analyzing the regional information, so that a proper region is selected for power scheduling, and the specific analysis and selection process is as follows:
step TT 1: acquiring an icing subarea corresponding to the line abnormity, marking the corresponding icing subarea as a maintenance area, and then acquiring area information of the maintenance area and a peripheral area, wherein the area information comprises the spacing distance between the maintenance area and the peripheral area, the average power consumption of the peripheral area of the maintenance area all day and the sum of the number of residents and factories in the peripheral area of the maintenance area;
step TT 2: acquiring the spacing distance between a maintenance area and the peripheral area, the average electricity consumption of the peripheral area of the maintenance area all day and the sum of the number of residents and the number of factories in the peripheral area of the maintenance area, and respectively marking the spacing distance between the maintenance area and the peripheral area, the average electricity consumption of the peripheral area of the maintenance area all day and the number of residents and factories in the peripheral area of the maintenance area as JL, DL and ZH;
step TT 3: by the formula
Figure BDA0002962447010000121
Acquiring a scheduling coefficient DD of a peripheral area of a maintenance area, wherein s1, s2 and s3 are all proportionality coefficients, and s1 is greater than s2 is greater than s3 is greater than 0;
step TT 4: sequencing the peripheral areas of the maintenance areas according to the sequence of the scheduling coefficients from large to small, marking the peripheral area with the first sequence as a scheduling selected area, simultaneously marking the peripheral area with the second sequence as a scheduling alternative area, and then sending the scheduling selected area and the scheduling alternative area to a mobile phone terminal of a manager.
The working principle of the invention is as follows:
an icing early warning judgment method based on multi-source data fusion specifically comprises the following steps: registering, namely, registering and logging in by a manager and a maintenance worker through a mobile phone terminal; ice coating prediction, namely analyzing the environmental information through an ice coating prediction unit so as to predict whether each area is coated with ice or not; the circuit monitoring is carried out, wherein the circuit monitoring unit is used for analyzing the power line data of each ice-coated subregion so as to monitor the power lines of each ice-coated subregion; analyzing the road condition, namely analyzing the road information to which the power line of each ice-coated subregion belongs through a road condition analysis unit so as to detect the road to which the power line of each ice-coated subregion belongs; and power dispatching, wherein the region information is analyzed by a power dispatching unit, so that a proper region is selected for power dispatching.
The above formulas are all calculated by taking the numerical value of the dimension, the formula is a formula which obtains the latest real situation by acquiring a large amount of data and performing software simulation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.

Claims (5)

1.基于多源数据融合的覆冰预警判定方法,其特征在于,具体覆冰预警判定方法过程如下:1. The icing early warning judgment method based on multi-source data fusion is characterized in that, the concrete icing early warning judgment method process is as follows: 步骤一、注册登录,管理人员和维修人员通过手机终端进行注册登录;Step 1. Register and log in. Management personnel and maintenance personnel register and log in through the mobile terminal; 步骤二、覆冰预测,通过覆冰预测单元对环境信息进行分析,从而对各个区域是否覆冰进行预测;Step 2, icing prediction, analyze the environmental information through the icing prediction unit, so as to predict whether each area is icing; 步骤三、电路监测,通过电路监测单元对各个覆冰子区域的电力线路数据进行分析,从而对各个覆冰子区域的电力线路进行监测;Step 3, circuit monitoring, analyze the power line data of each icing sub-region through the circuit monitoring unit, so as to monitor the power line of each icing sub-region; 步骤四、路况分析,通过路况分析单元对各个覆冰子区域的电力线路所属道路信息进行分析,从而对各个覆冰子区域的电力路线所属道路进行检测;Step 4, road condition analysis, through the road condition analysis unit to analyze the information of the roads to which the power lines of each icing sub-region belong, so as to detect the roads to which the power lines of each icing sub-region belong; 步骤五、电力调度,通过电力调度单元对区域信息进行分析,从而对电力调度选择合适的区域;Step 5: Power dispatching, analyzing the regional information through the power dispatching unit, so as to select a suitable region for power dispatching; 所述步骤二中覆冰预测单元用于对环境信息进行分析,从而对各个区域是否覆冰进行预测,环境信息包括温度数据、湿度数据以及风速数据,温度数据为各个子区域内全天的最大温度变化值,湿度数据为各个子区域内全天的平均湿度值,风速数据为各个子区域全天每小时的风速变化值,具体分析预测过程如下:In the second step, the ice-covering prediction unit is used to analyze the environmental information, so as to predict whether each area is covered with ice. The environmental information includes temperature data, humidity data and wind speed data, and the temperature data is the maximum value of the whole day in each sub-area. The temperature change value, the humidity data is the average humidity value of each sub-area throughout the day, and the wind speed data is the hourly wind speed change value of each sub-area. The specific analysis and prediction process is as follows: 步骤S1:将待检测电路线路所在的区域划分为若干个子区域,随后将若干个子区域标记为i,i=1,2,……,n,n为正整数;Step S1: Divide the area where the circuit line to be detected is located into several sub-areas, and then mark the several sub-areas as i, i=1, 2, ..., n, n is a positive integer; 步骤S2:获取到各个子区域内全天的最大温度变化值,并将各个子区域内全天的最大温度变化值标记为WBi;Step S2: obtaining the maximum temperature change value of the whole day in each sub-area, and marking the maximum temperature change value of the whole day in each sub-area as WBi; 步骤S3:获取到各个子区域内全天的平均湿度值,并将各个子区域内全天的平均湿度值标记为PSi;Step S3: obtaining the average humidity value of the whole day in each sub-area, and marking the average humidity value of the whole day in each sub-area as PSi; 步骤S4:获取到各个子区域全天每小时的风速变化值,并将各个子区域全天每小时的风速变化值标记为FBi;Step S4: obtaining the hourly wind speed variation value of each sub-area throughout the day, and marking the hourly wind speed variation value of each sub-area as FBi; 步骤S5:通过公式
Figure FDA0002962447000000021
获取到子区域的覆冰预测系数Xi,其中,a1、a2以及a3均为比例系数,a1>a2>a3>0,β为误差修正因子,取值为2.36521;
Step S5: Pass the formula
Figure FDA0002962447000000021
Obtain the icing prediction coefficient Xi of the sub-region, where a1, a2 and a3 are proportional coefficients, a1>a2>a3>0, β is an error correction factor, and the value is 2.36521;
步骤S6:将子区域的覆冰预测系数Xi与子区域的覆冰预测系数阈值进行比较:Step S6: Compare the icing prediction coefficient Xi of the sub-region with the icing prediction coefficient threshold of the sub-region: 若子区域的覆冰预测系数Xi≥子区域的覆冰预测系数阈值,则将对应子区域标记为覆冰子区域,生成覆冰预测信号并将覆冰预测信号和覆冰子区域发送至预警管理平台;If the icing prediction coefficient Xi of the sub-region is greater than or equal to the icing prediction coefficient threshold of the sub-region, mark the corresponding sub-region as the icing sub-region, generate the icing prediction signal, and send the icing prediction signal and the icing sub-region to the early warning management platform; 若子区域的覆冰预测系数Xi<子区域的覆冰预测系数阈值,则将对应子区域标记为无覆冰子区域,生成不覆冰预测信号并将不覆冰预测信号和无覆冰子区域发送至预警管理平台;If the icing prediction coefficient Xi of the sub-region is less than the icing prediction coefficient threshold of the sub-region, the corresponding sub-region is marked as the non-icing sub-region, the non-icing prediction signal is generated, and the non-icing prediction signal and the non-icing sub-region are generated. Send it to the early warning management platform; 步骤S7:获取到冷空气的风向,随后根据冷空气抵达子区域的时间先后顺序,将各个覆冰子区域进行排序,随后将排序好的覆冰子区域发送至管理人员的手机终端。Step S7: Obtain the wind direction of the cold air, then sort the icing sub-regions according to the time sequence of the arrival of the cold air in the sub-regions, and then send the sorted icing sub-regions to the mobile terminal of the manager.
2.根据权利要求1所述的基于多源数据融合的覆冰预警判定方法,其特征在于,所述路况分析单元用于对各个覆冰子区域的电力线路所属道路信息进行分析,从而对覆冰子区域的路况进行检测,各个覆冰子区域的电力线路所属道路信息包括数量数据、速度数据以及频率数据,数量数据为覆冰子区域内电力线路周边道路全天平均通行车辆数量,速度数据为覆冰子区域内电力线路周边道路全天通行车辆的平均速度,频率数据为覆冰子区域内电力线路周边道路全天车辆通行的频率,将覆冰子区域标记为o,o=1,2,……,m,m为正整数,具体分析检测过程如下:2 . The method for judging early warning of icing based on multi-source data fusion according to claim 1 , wherein the road condition analysis unit is used to analyze the road information to which the power lines of each icing sub-region belong, so as to analyze the icing warning. 3 . The road conditions in the icy sub-region are detected. The road information of the power lines in each icing sub-region includes quantity data, speed data and frequency data. The quantity data is the average number of vehicles passing through the roads around the power line in the icing sub-region, and the speed data is the average speed of vehicles passing through the roads around the power line in the icy sub-region, and the frequency data is the frequency of vehicles passing on the roads around the power line in the icing sub-region. Mark the icing sub-region as o, o=1, 2, ..., m, m are positive integers. The specific analysis and detection process is as follows: 步骤SS1:获取到覆冰子区域内电力线路周边道路全天平均通行车辆数量,并将覆冰子区域内电力线路周边道路全天平均通行车辆数量标记为So;Step SS1: Obtain the average number of vehicles passing through the roads around the power line in the Bic sub area throughout the day, and mark the average number of vehicles passing through the roads around the power line in the Bic sub area as So; 步骤SS2:获取到覆冰子区域内电力线路周边道路全天通行车辆的平均速度,并将覆冰子区域内电力线路周边道路全天通行车辆的平均速度标记为Vo;Step SS2: Obtain the average speed of vehicles passing all day on the roads surrounding the power line in the ice-covering sub-region, and mark the average speed of vehicles passing on the roads surrounding the power line in the ice-covering sub-region as Vo; 步骤SS3:获取到覆冰子区域内电力线路周边道路全天车辆通行的频率,并将覆冰子区域内电力线路周边道路全天车辆通行的频率标记为Po;Step SS3: Acquire the all-day vehicle traffic frequency of the roads surrounding the power line in the Bing sub-region, and mark the all-day vehicle traffic frequency of the roads surrounding the power line in the Bing sub-region as Po; 步骤SS4:通过公式Xo=(So×b1+Vo×b2+Po×b3)eb1+b2+b3获取到覆冰子区域的路况分析系数Xo,其中,b1、b2以及b3均为比例系数,且b1>b2>b3>0,e为自然常数;Step SS4: Obtain the road condition analysis coefficient Xo of the icy sub-region through the formula Xo=(So×b1+Vo×b2+Po×b3)e b1+b2+b3 , wherein b1, b2 and b3 are proportional coefficients, And b1>b2>b3>0, e is a natural constant; 步骤SS5:将覆冰子区域的路况分析系数与路况分析系数阈值进行比较:Step SS5: Compare the road condition analysis coefficient of the icing sub-region with the road condition analysis coefficient threshold: 若覆冰子区域的路况分析系数≥路况分析系数阈值,则判定对应覆冰子区域需实施车辆管制,生成车辆管制信号并将车辆管制信号和对应的覆冰子区域发送至预警管理平台,预警管理平台接收到车辆管制信号后,获取到冷空气预计持续时间,随后将预计持续时间和车辆管制信号发送至管理人员的手机终端;If the road condition analysis coefficient of the icing sub-area is greater than or equal to the threshold of the road condition analysis coefficient, it is determined that the corresponding icing sub-area needs to implement vehicle control, a vehicle control signal is generated, and the vehicle control signal and the corresponding icing sub-area are sent to the early warning management platform. After the management platform receives the vehicle control signal, it obtains the estimated duration of cold air, and then sends the estimated duration and vehicle control signal to the manager's mobile terminal; 若覆冰子区域的路况分析系数<路况分析系数阈值,则判定对应覆冰子区域不需实施车辆管制,生成车辆不管制信号并将车辆不管制信号和对应的覆冰子区域发送至预警管理平台。If the road condition analysis coefficient of the icing sub-area is less than the threshold of the road condition analysis coefficient, it is determined that the corresponding icing sub-area does not need to implement vehicle control, a vehicle non-control signal is generated, and the vehicle non-control signal and the corresponding icing sub-area are sent to the early warning management platform. 3.根据权利要求1所述的基于多源数据融合的覆冰预警判定方法,其特征在于,所述电路监测单元用于对各个区域内的电力线路数据进行分析,从而对覆冰子区域的电力线路进行监测,电力线路数据包括电力线路中导线的垂度、电力线路中导线之间的相对安全距离以及电力线路中连接点受到的剪切力,具体分析监测过程如下:3. The method for judging early warning of icing based on multi-source data fusion according to claim 1, wherein the circuit monitoring unit is used to analyze the power line data in each area, so as to analyze the icing sub-regions. The power line is monitored. The power line data includes the sag of the conductors in the power line, the relative safety distance between the conductors in the power line, and the shear force on the connection points in the power line. The specific analysis and monitoring process is as follows: 步骤T1:获取到电力线路中导线的垂度,并将电力线路中导线的垂度标记为CDo;Step T1: Obtain the sag of the wires in the power line, and mark the sag of the wires in the power line as CDo; 步骤T2:获取到电力线路中导线之间的相对安全距离,并将电力线路中导线之间的相对安全距离标记为JLo;Step T2: Obtain the relative safety distance between the wires in the power line, and mark the relative safety distance between the wires in the power line as JLo; 步骤T3:获取到电力线路中连接点受到的剪切力,并将电力线路中连接点受到的剪切力标记为JQo;Step T3: Obtain the shear force on the connection point in the power line, and mark the shear force on the connection point in the power line as JQo; 步骤T4:通过公式
Figure FDA0002962447000000041
获取到各个覆冰子区域的电力线路分析系数JCo,其中,v1、v2以及v3均为比例系数,且v1>v2>v3>0;
Step T4: Pass the formula
Figure FDA0002962447000000041
Obtain the power line analysis coefficient JCo of each icing sub-region, where v1, v2 and v3 are proportional coefficients, and v1>v2>v3>0;
步骤T5:将各个覆冰子区域的电力线路分析系数JCo与电力线路分析系数阈值进行比较:Step T5: Compare the power line analysis coefficient JCo of each icing sub-region with the power line analysis coefficient threshold: 若各个覆冰子区域的电力线路分析系数JCo≥电力线路分析系数阈值,则判定对应覆冰子区域的电力线路异常,生成线路异常信号并将线路异常信号和对应的覆冰子区域发送至预警管理平台,预警管理平台接收到线路异常信号后,生成线路维修信号并将线路维修信号发送至维修人员的手机终端;If the power line analysis coefficient JCo of each icing sub-area is greater than or equal to the threshold value of the power line analysis coefficient, it is determined that the power line of the corresponding icing sub-region is abnormal, a line abnormal signal is generated, and the line abnormal signal and the corresponding icing sub-region are sent to the early warning The management platform, the early warning management platform, after receiving the line abnormal signal, generates the line maintenance signal and sends the line maintenance signal to the mobile phone terminal of the maintenance personnel; 若各个覆冰子区域的电力线路分析系数JCo<电力线路分析系数阈值,则判定对应覆冰子区域的电力线路正常,生成线路正常信号并将线路正常信号和对应的覆冰子区域发送至预警管理平台。If the power line analysis coefficient JCo of each icing sub-region is less than the threshold value of the power line analysis coefficient, it is determined that the power line of the corresponding icing sub-region is normal, a line normal signal is generated, and the line normal signal and the corresponding icing sub-region are sent to the early warning management platform.
4.根据权利要求1所述的基于多源数据融合的覆冰预警判定方法,其特征在于,所述电力调度单元用于对区域信息进行分析,从而对电力调度选择合适的区域,具体分析选择过程如下:4. The method for judging early warning of icing based on multi-source data fusion according to claim 1, wherein the power dispatching unit is used to analyze the regional information, so as to select a suitable region for power dispatching, and the specific analysis and selection The process is as follows: 步骤TT1:获取到线路异常对应的覆冰子区域,并将对应的覆冰子区域标记为维修区域,随后获取到维修区域与周边区域的区域信息,区域信息包括维修区域与周边区域的间隔距离,维修区域的周边区域全天平均用电量以及维修区域的周边区域内居民数量与工厂数量之和;Step TT1: Acquire the icing sub-area corresponding to the line abnormality, mark the corresponding icing sub-area as a maintenance area, and then acquire the area information of the maintenance area and the surrounding area, and the area information includes the distance between the maintenance area and the surrounding area , the average daily electricity consumption in the surrounding area of the maintenance area and the sum of the number of residents and the number of factories in the surrounding area of the maintenance area; 步骤TT2:获取到维修区域与周边区域的间隔距离,维修区域的周边区域全天平均用电量以及维修区域的周边区域内居民数量与工厂数量之和,并将维修区域与周边区域的间隔距离,维修区域的周边区域全天平均用电量以及维修区域的周边区域内居民数量与工厂数量分别标记为JL、DL以及ZH;Step TT2: Obtain the distance between the maintenance area and the surrounding area, the average electricity consumption in the surrounding area of the maintenance area throughout the day, and the sum of the number of residents and the number of factories in the surrounding area of the maintenance area, and calculate the distance between the maintenance area and the surrounding area. , the daily average electricity consumption in the surrounding area of the maintenance area and the number of residents and factories in the surrounding area of the maintenance area are marked as JL, DL and ZH respectively; 步骤TT3:通过公式
Figure FDA0002962447000000051
获取到维修区域的周边区域的调度系数DD,其中,s1、s2以及s3均为比例系数,且s1>s2>s3>0;
Step TT3: Pass the formula
Figure FDA0002962447000000051
Obtain the dispatch coefficient DD of the surrounding area of the maintenance area, wherein s1, s2 and s3 are proportional coefficients, and s1>s2>s3>0;
步骤TT4:将维修区域的周边区域按照调度系数从大到小的顺序进行排序,并将排序第一的周边区域标记为调度选中区域,同时间排序第二的周边区域标记为调度备选区域,随后将调度选中区域和调度备选区域发送至管理人员的手机终端。Step TT4: Sort the surrounding areas of the maintenance area in descending order of the scheduling coefficient, and mark the surrounding area ranked first as the scheduling selected area, and the surrounding area ranked second at the same time as the scheduling candidate area, Then, the selected area for scheduling and the candidate area for scheduling are sent to the manager's mobile phone terminal.
5.根据权利要求1所述的基于多源数据融合的覆冰预警判定方法,其特征在于,所述步骤一中注册登录单元用于管理人员和维修人员通过手机终端提交管理人员信息和维修人员信息进行注册,并将注册成功的管理人员信息和维修人员信息发送至数据库进行储存,管理人员信息包括管理人员的姓名、年龄、入职时间以及本人实名认证的手机号码,维修人员信息包括维修人员的姓名、年龄、入职时间以及本人实名认证的手机号码。5. The method for judging icing early warning based on multi-source data fusion according to claim 1, wherein in the step 1, the registration and logging unit is used for management personnel and maintenance personnel to submit management personnel information and maintenance personnel through mobile phone terminals. Register the information, and send the successfully registered management personnel information and maintenance personnel information to the database for storage. The management personnel information includes the management personnel's name, age, entry time and the mobile phone number of their real-name authentication. The maintenance personnel information includes the maintenance personnel's information. Name, age, entry time and mobile phone number for real-name authentication.
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116052371A (en) * 2023-01-03 2023-05-02 广州市市政工程试验检测有限公司 A method and system for monitoring and early warning of ice falling risk of cables
CN116050599A (en) * 2022-12-30 2023-05-02 泰豪软件股份有限公司 Method, system, storage medium and equipment for predicting line icing faults
CN116697907A (en) * 2023-07-31 2023-09-05 山西锦烁生物医药科技有限公司 Distributed optical fiber sensing ice coating thickness measuring system and method for ice rink
CN118211781A (en) * 2024-03-13 2024-06-18 湖北太宝科技有限公司 An intelligent management system and management method for construction equipment
CN119851431A (en) * 2025-03-21 2025-04-18 长春工程学院 Power transmission line icing early warning method based on weather factors

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103091680A (en) * 2013-01-11 2013-05-08 北京国网富达科技发展有限责任公司 Measuring method and measuring system for distance from wires to ground
US20130154854A1 (en) * 2011-12-14 2013-06-20 Industrial Technology Research Institute Exceptional road-condition warning device, system and method for a vehicle
US20140067271A1 (en) * 2012-08-30 2014-03-06 International Business Machines Corporation Predicting ice coating status on transmission lines
CN103779808A (en) * 2013-12-30 2014-05-07 国家电网公司 Power transmission line intelligent inspection system based on LiDAR
CN105158821A (en) * 2015-08-17 2015-12-16 国家电网公司 Meteorological early warning method of power transmission line icing gallop disasters
CN109300312A (en) * 2018-12-06 2019-02-01 深圳市泰比特科技有限公司 A kind of road condition analyzing method and system based on vehicle big data
CN110188914A (en) * 2019-03-25 2019-08-30 华北电力大学 A kind of intelligent Forecasting for grid power transmission route ice covering thickness
CN111210086A (en) * 2020-01-15 2020-05-29 杭州华网信息技术有限公司 National power grid icing disaster prediction method
CN112114384A (en) * 2020-08-27 2020-12-22 中国南方电网有限责任公司超高压输电公司检修试验中心 Power transmission line icing occurrence probability forecasting method

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130154854A1 (en) * 2011-12-14 2013-06-20 Industrial Technology Research Institute Exceptional road-condition warning device, system and method for a vehicle
US20140067271A1 (en) * 2012-08-30 2014-03-06 International Business Machines Corporation Predicting ice coating status on transmission lines
CN103091680A (en) * 2013-01-11 2013-05-08 北京国网富达科技发展有限责任公司 Measuring method and measuring system for distance from wires to ground
CN103779808A (en) * 2013-12-30 2014-05-07 国家电网公司 Power transmission line intelligent inspection system based on LiDAR
CN105158821A (en) * 2015-08-17 2015-12-16 国家电网公司 Meteorological early warning method of power transmission line icing gallop disasters
CN109300312A (en) * 2018-12-06 2019-02-01 深圳市泰比特科技有限公司 A kind of road condition analyzing method and system based on vehicle big data
CN110188914A (en) * 2019-03-25 2019-08-30 华北电力大学 A kind of intelligent Forecasting for grid power transmission route ice covering thickness
CN111210086A (en) * 2020-01-15 2020-05-29 杭州华网信息技术有限公司 National power grid icing disaster prediction method
CN112114384A (en) * 2020-08-27 2020-12-22 中国南方电网有限责任公司超高压输电公司检修试验中心 Power transmission line icing occurrence probability forecasting method

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
KUAN-JUN ZHU: "Statistical analysis and research on galloping characteristics and damage for iced conductors of transmission lines in China", 《2010 INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY》 *
夏令志: "2018年1月安徽电网输电线路覆冰舞动故障规律分析", 《安徽电气工程职业技术学院学报》 *
徐啸峰等: "基于"320系统"的城市主要道路实时路况研究", 《中国管理信息化》 *
李天源: "高压输电线路状态监测系统技术探讨", 《通讯世界》 *
温华洋: "安徽省电线积冰标准冰厚的气象估算模型", 《应用气象学报》 *
陈在铁等: "500kV紧凑型线路导线非线性运动分析", 《清华大学学报(自然科学版)》 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116050599A (en) * 2022-12-30 2023-05-02 泰豪软件股份有限公司 Method, system, storage medium and equipment for predicting line icing faults
CN116052371A (en) * 2023-01-03 2023-05-02 广州市市政工程试验检测有限公司 A method and system for monitoring and early warning of ice falling risk of cables
CN116052371B (en) * 2023-01-03 2025-06-03 广州市市政工程试验检测有限公司 Inhaul cable ice falling risk monitoring and early warning method and system
CN116697907A (en) * 2023-07-31 2023-09-05 山西锦烁生物医药科技有限公司 Distributed optical fiber sensing ice coating thickness measuring system and method for ice rink
CN118211781A (en) * 2024-03-13 2024-06-18 湖北太宝科技有限公司 An intelligent management system and management method for construction equipment
CN119851431A (en) * 2025-03-21 2025-04-18 长春工程学院 Power transmission line icing early warning method based on weather factors

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