JP7843253B2 - Wind power diagnostic and evaluation device, wind power diagnostic and evaluation method, and program - Google Patents

Description

Embodiments of the present invention relate to a wind power diagnostic and evaluation device, a wind power diagnostic and evaluation method, and a program.

In wind power generation systems, wind turbulence and other factors can increase the load on the blades. This raises concerns about an increased risk of damage to mechanical components. Therefore, an operational control method has been proposed that involves switching to a lower-output operating state than normal, thereby reducing power generation. While this type of output-suppressed operation results in a loss of power generation, it can reduce the load on mechanical components.

Patent No. 3962645 Patent No. 6421134 Japanese Patent Publication No. 2021-88972 Patent No. 7009237 Special Publication No. 2022-530198 Japanese Patent Publication No. 2020-112035

One of the mechanical components in a wind turbine is a bearing installed on the rotating shaft that transmits the rotational force of the blades to the generator. This bearing is susceptible to damage when the wind turbine starts up under adverse wind conditions such as turbulence.

However, conventional operating control systems determine start and stop based on wind speed values, not the load applied to the blades. Therefore, it is difficult to address bearing damage caused by overload during startup, as described above.

The problem that this invention aims to solve is to propose a wind power diagnostic and evaluation device, a wind power diagnostic method, and a program that can reduce the risk of bearing damage during the startup of a wind power generation device.

One embodiment of the wind power diagnostic and evaluation device is a wind power diagnostic and evaluation device for a wind turbine power generation system comprising at least a blade that rotates with wind power, a generator that generates electricity using the rotational force of the blade, a rotating shaft that transmits the rotational force to the generator, and a bearing installed on the rotating shaft. This wind power diagnostic and evaluation device includes a wind load analysis unit that analyzes the wind load applied to the blade by wind power, a bearing state evaluation unit that evaluates the state quantities of the bearing at startup of the wind turbine power generation system based on the analysis results of the wind load analysis unit, and an output setting unit that sets an output command value including the amount of power generated by the wind turbine power generation system at startup based on the evaluation results of the bearing state evaluation unit.

According to this embodiment, it is possible to reduce the risk of bearing damage during the startup of the wind power generation system.

This is a schematic diagram showing the general configuration of a wind power generation system. This is a cross-sectional view showing an example of a bearing structure. This is a block diagram showing a schematic configuration of a wind power diagnostic and evaluation device and a data acquisition device according to the first embodiment. This figure shows an example of wind speed distribution. This figure shows an example of a load analysis database. This figure shows an example of a past damage database. This is a diagram illustrating the remaining lifespan database. This is a flowchart showing the calculation process of the calculation unit according to the first embodiment. This is a diagram illustrating how to calculate the remaining lifespan of a bearing. This figure shows an example of the relationship between the total operating time of the wind power generation device 10 and the profit from selling electricity, broken down by output command value. (a) is a front view of the wind turbine, (b) is a top view of the tower at a height H1 from the ground, (c) is a top view of the tower at a height H2 from the ground, and (d) is a schematic diagram showing the direction and magnitude of the wind load on each floor. (a) is a front view of a wind power generation device, (b) is a diagram showing an example of strain measurement data, and (c) is a diagram showing the relationship between wind load and azimuth angle. This is a block diagram showing a schematic configuration of a wind power diagnostic and evaluation device and a data acquisition device according to the second embodiment. This is a flowchart showing the control operation procedure of the control unit according to the second embodiment.

The embodiments of the present invention will be described below with reference to the drawings. The embodiments described below are not intended to limit the present invention.

(First Embodiment)
Figure 1 is a schematic diagram showing the general configuration of a wind power generation device. The wind power generation device 10 shown in Figure 1 comprises a nacelle 1 which is a housing, a tower 2 which is a support column that supports the nacelle 1 from below, a plurality of blades 3, and a hub 4 that supports the plurality of blades 3. The wind power generation device 10 may be installed offshore or on land.

The nacelle 1 houses the rotating shaft 5, bearings 6, a transmission mechanism 7, and a generator 8. One end of the rotating shaft 5 is rotatably fixed to the hub 4 by bearings 6. The other end of the rotating shaft 5 is connected to the transmission mechanism 7.

The gear shifting mechanism 7 is connected to the generator 8 via an appropriate coupling mechanism or the like. In this embodiment, the base ends of the three blades 3 are fixed to the hub 4 at 120-degree intervals in the rotational direction. Note that in this embodiment, the gear shifting mechanism 7 may not be provided. In this case, the other end of the rotating shaft 5 is connected to the generator 8.

During operation of the wind turbine 10, the multiple blades 3, which rotate together with the hub 4 and the rotating shaft 5, convert fluid energy obtained from wind into rotational energy. This rotational energy is transmitted to the generator 8 via the gearbox 7 by the rotating shaft 5. At this time, the gearbox 7 decelerates or increases the rotational speed. The generator 8 uses the transmitted rotational energy to generate electricity.

The wind condition measuring instrument 9 is installed on the outer perimeter of the nacelle 1. The wind condition measuring instrument 9 is an example of a wind speed sensor that functions as an anemometer capable of measuring wind direction or a wind direction indicator capable of measuring wind direction. The wind condition measuring instrument 9 measures wind condition data, such as the average wind speed and changes in wind speed, in the installation area where the wind power generation device 10 is installed.

Figure 2 is a cross-sectional view showing an example of the structure of bearing 6. In this embodiment, bearing 6 is a rolling bearing having a plurality of rollers 61, a bearing outer ring portion 62, lubricating oil 63, and a bearing inner ring portion 64. Each roller 61 rotates in the lubricating oil 63. When the wind power generation device 10 is started, the gap between the rollers 61 and the bearing outer ring portion 62 and bearing inner ring portion 64 becomes smaller. Therefore, the thickness t of the oil film of lubricating oil 63 formed in these gaps also becomes smaller. As a result, when the wind power generation device 10 starts operating based on the output command value of the rated power generation amount under conditions such as turbulence, an excessive load may be applied to bearing 6. In this case, bearing 6 becomes more susceptible to damage, and its lifespan is shortened.

Therefore, in this embodiment, the wind power diagnostic and evaluation device 20 sets the output command value for the wind power generation system 10 at startup using various data collected by the data acquisition device 30. The installation area of the wind power diagnostic and evaluation device 20 and the data acquisition device 30 is not particularly limited. For example, the wind power diagnostic and evaluation device 20 may be installed around the operation control device 40 of the wind power generation system 10.

Figure 3 is a block diagram showing a schematic configuration of the wind power diagnostic and evaluation device 20 and data acquisition device 30 according to the first embodiment. The wind power diagnostic and evaluation device 20 and data acquisition device 30 may be connected by a wired connection or by a wireless connection via a network.

First, let's explain the data collected by the data acquisition device 30. The data acquisition device 30 collects, for example, SCADA (Supervisory Control And Data Acquisition) data 31, temperature measurement data 32, weather forecast data 33, displacement measurement data 34, strain measurement data 35, and wind condition measurement data 36.

SCADA (Supervisory Control And Data Acquisition) data 31 is data used to monitor and control the operation of the wind turbine 10. The SCADA data 31 includes, for example, power generation, wind speed, wind direction, and the pitch and yaw angles of the blades 3. The wind speed and wind direction in the SCADA data 31 are wind condition data measured, for example, by a wind condition measuring instrument 9.

The temperature measurement data 32 shows the ambient temperature and the temperature of the bearing 6. The ambient temperature is, for example, temperature data measured by the wind condition measuring instrument 9. The temperature of the bearing 6 is, for example, temperature data measured using a thermocouple.

The weather forecast data 33 includes weather analysis models such as the MSM (Meso Scale Model) and LFM (Local Forecast Model) provided by the Japan Meteorological Agency.

The displacement measurement data 34 shows the displacement δ of the bearing 6 due to the wind load. The displacement δ is the relative radial and axial displacement from point α to point β due to the load, as shown, for example, in Figure 2. The displacement measurement data 34 shows the displacement δ at three or more points measured by a displacement sensor (not shown).

The strain measurement data 35 shows at least one of the strain amounts of the blades 3 and the tower 2. The strain amount of the blades 3 is measured at at least two points on one or more blades 3 using strain sensors (not shown). The strain amount of the tower 2 is measured using multiple strain sensors (not shown). The strain sensors are installed at three or more measurement points spaced circumferentially on the outer perimeter of at least one floor. Specifically, if there is only one outer perimeter of the floor, the thrust load applied to the blades 3 can be measured. Furthermore, if there are two or more outer perimeters of floors at different heights from the ground, the moment load applied to the blades 3 can be measured.

The wind condition measurement data 36 shows the actual measurement data obtained by the wind condition measurement device 50 (see Figure 1) installed around the wind power generation device 10. The wind condition measurement device 50 is, for example, a LiDAR (Light Detection and Ranging) system. A LiDAR system emits laser light into the atmosphere, receives scattered light from the atmosphere, and observes wind speed and direction from its Doppler frequency.

Next, the configuration of the wind power diagnostic and evaluation device 20 will be described. As shown in Figure 3, the wind power diagnostic and evaluation device 20 comprises a communication unit 21, an operation unit 22, a display unit 23, a storage unit 24, and a calculation unit 25. Each unit will be described below.

The communication unit 21 functions as a communication interface when communicating with the data acquisition device 30.

The operation unit 22 receives user input. The operation unit 22 includes, for example, an input device such as a keyboard or mouse.

The display unit 23 displays various images, such as the results of the calculation unit 25's processing. The display unit 23 has a display device, such as a liquid crystal display.

The memory unit 24 stores the wind condition analysis database 241, the load analysis database 242, the past damage database 243, and the remaining life database 244. Each database is described below.

The wind condition analysis database 241 shows the vertical wind speed distribution for each of several wind condition conditions. Here, the wind speed distribution of the wind condition analysis database 241 will be explained with reference to Figure 4.

Figure 4 shows an example of a wind speed distribution. In Figure 4, the horizontal axis represents the wind speed at the location of Tower 2, and the vertical axis represents the elevation of that location. Figure 4 shows the wind speed distribution for three wind conditions C1, C2, and C3. Each wind condition includes wind speed, wind direction, turbulence intensity, and temperature. Turbulence intensity is the value obtained by dividing the standard deviation of the wind speed by the average wind speed. Note that Figure 4 shows the wind speed distribution at a certain position of Nacelle 1. However, Nacelle 1 is rotatable around Tower 2. Therefore, the wind speed distribution in the wind condition analysis database 241 includes the wind speed profile in the YZ plane shown in Figure 1, depending on the rotation position of Nacelle 1. Here, in the XYZ coordinate axes shown in Figure 1, the origin is the height position of the hub 4 and the tip position where wind flows into the wind turbine. The X axis is the direction along the rotation axis 5. The Z axis is the vertical direction. The Y axis is the direction perpendicular to the X and Z axes. In this embodiment, since an upwind wind turbine is being considered, the origin is located at the hub 4. However, this embodiment may also be used for a downwind wind turbine. In this case, the origin would be the tip of the nacelle 1 into which the wind flows (see the black circle in Figure 1), and therefore the direction of the X-axis would be opposite to that shown in Figure 1.

Figure 5 shows an example of the load analysis database 242. The load analysis database 242 shown in Figure 5 displays the wind load F for each of the multiple load analysis conditions for each of the multiple output command values. Each load analysis condition corresponds to the wind speed distribution described above. The wind load F is the load applied to the blade 3 by the wind force. The multiple output command values represent different power generation amounts from the wind power generator 10 (generator 8). The wind load F corresponding to each output command value is pre-calculated based on load analysis conditions such as wind speed distribution, turbulence intensity, rotation speed of the rotating shaft 5, and the pitch angle of the blade 3. In this embodiment, the wind load F is the thrust load received by the blade 3 when the angle of attack is changed while the same wind is blowing.

Figure 6 shows an example of a past damage database 243. The past damage database 243 shown in Figure 6 indicates threshold values for the oil film thickness formed on the bearing 6 for each of several output command values. The threshold values shown in the past damage database 243 are pre-set for each output command value based on the oil film thickness when the bearing 6 was damaged in the past. For example, if an output command value a is set when the wind power generation device 10 is started, if the oil film thickness t of the bearing 6 at the time of startup is below the threshold value t _th1 , the likelihood of the bearing 6 being damaged increases.

Figure 7 is a diagram illustrating the remaining life database 244. The remaining life database 244 contains data indicating how much of the bearing 6's life is consumed as the operating time of the wind turbine 10 elapses. In the remaining life database 244 shown in Figure 7, the total operating time of the wind turbine 10 is associated with the remaining life rate of the bearing 6. The remaining life rate represents the ratio of the remaining life to the life of 1.0 when the total operating time of the wind turbine 10 is 0, minus the life consumed when operating at a certain output command value. The remaining life database 244 may be actual data shown as a solid line, or average data shown as a dotted line. The average data is the average life consumption rate obtained by averaging multiple actual data sets.

The databases described above are used for the calculation processing of the calculation unit 25. The calculation unit 25 includes a data acquisition unit 251, a wind condition analysis unit 252, a wind load analysis unit 253, a bearing condition evaluation unit 254, a bearing life calculation unit 255, and an output setting unit 256. If the functions of each unit are implemented by a CPU (Central Processing Unit) that executes calculation processing based on a computer program, for example, this computer program is stored in the storage unit 24.

Here, we will explain the calculation process of the calculation unit 25 as a wind power diagnostic and evaluation method using the wind power diagnostic and evaluation device 20.

Figure 8 is a flowchart showing the calculation process of the calculation unit 25 according to the first embodiment. In this flowchart, first, the data acquisition unit 251 acquires data from the data collection device 30 via the communication unit 21 (step S11). In this embodiment, the data acquisition unit 251 acquires, for example, SCADA data 31 and temperature measurement data 32. Note that in step S11, the data acquisition unit 251 may also acquire wind condition measurement data 36.

Next, the wind condition analysis unit 252 performs a wind condition analysis using the data acquired by the data acquisition unit 251 and the wind condition analysis database 241 (step S12). In step S12, first, the wind condition analysis unit 252 identifies wind conditions based on the wind speed, wind direction, etc., shown in the SCADA data 31. Subsequently, the wind condition analysis unit 252 selects a wind speed distribution corresponding to the identified wind conditions from the wind condition analysis database 241. Then, based on the selected wind speed distribution, the wind condition analysis unit 252 determines the wind speed distribution acting on the entire blade 3, including the height H (Figure 4) of the nacelle 1 where the bearings 6 etc. are housed from the ground.

Furthermore, if the data acquisition unit 251 also acquires wind condition measurement data 36 in step S11, in step S1, the wind condition analysis unit 252 determines the wind speed distribution acting on the entire blade 3, including height H, from the wind condition measurement data 36. In this case, the wind condition analysis unit 252 outputs a wind condition analysis result obtained by multiplying, for example, the value obtained from the wind condition analysis database 241 and the value obtained from the wind condition measurement data 36 by the same or different weighting coefficients and adding them together.

Furthermore, in step S12, the wind condition analysis unit 252 may determine the average wind speed, which represents the average wind speed at height H (Figure 4) over a predetermined time period, or the turbulence intensity.

Once the wind condition analysis by the wind condition analysis unit 252 is complete, the wind load analysis unit 253 then analyzes the wind load using the wind condition analysis results from the wind condition analysis unit 252 and the load analysis database 242 (step S13). In step S13, the wind load analysis unit 253 first identifies the load analysis conditions based on the wind condition analysis results from the wind condition analysis unit 252, specifically the wind speed in the wind speed distribution acting on the entire blade 3, including height H. Next, the wind load analysis unit 253 selects the wind load corresponding to the identified load analysis conditions from the load analysis database 242 for each output command value. Thus, in step S13, the wind load analysis unit 253 converts the wind condition analysis results from the wind condition analysis unit 252 into wind loads using the load analysis database 242.

Furthermore, when the wind condition analysis unit 252 calculates the average wind speed in step S12, the load analysis database 242 already has the average wind load registered. Therefore, in step S13, the wind load analysis unit 253 identifies the load analysis conditions based on the average wind speed at height H, and selects the average wind load corresponding to the identified load analysis conditions from the load analysis database 242 for each output command value. This average wind speed may, for example, be the average of wind speeds measured multiple times within a pre-set evaluation time range, or it may be the average of multiple wind speeds in the vertical direction (at each elevation) shown in the wind speed distribution.

Furthermore, when the wind condition analysis unit 252 determines the turbulence intensity in step S12, the maximum wind load is pre-registered in the load analysis database 242. Therefore, in step S13, the wind load analysis unit 253 identifies the load analysis conditions based on the turbulence intensity at height H, and selects the maximum wind load corresponding to the identified load analysis conditions from the load analysis database 242 for each output command value.

Once the load analysis by the wind load analysis unit 253 is complete, the bearing condition evaluation unit 254 then evaluates the state quantities of the bearing 6 in relation to wind force using the analysis results from the wind load analysis unit 253 and the past damage database 243 (step S14). In step S14, the bearing condition evaluation unit 254 first calculates the bearing load applied to the bearing 6 by the wind load using a bearing element model. The bearing element model is, for example, a calculation model for converting the wind load obtained for each output command value into the surface pressure applied to the rollers 61 of the bearing 6. This calculation model is predetermined according to the number and type of rollers 61, and the gap between the rollers 61 and the bearing outer ring portion 62 and bearing inner ring portion 64. In this embodiment, the integral value of the surface pressure applied to the rollers 61 corresponds to the bearing load. Note that if the wind load analysis unit 253 selects an average or maximum value of the wind load in step S13, the bearing condition evaluation unit 254 calculates the average or maximum value of the bearing load.

Next, the bearing condition evaluation unit 254 calculates the oil film thickness t of the lubricating oil 63 for each output command value based on the bearing load and the temperature of the bearing 6. At this time, the bearing condition evaluation unit 254 identifies the temperature of the bearing 6 from the SCADA data 31 or temperature measurement data 32 acquired by the data acquisition unit 251. Furthermore, the bearing condition evaluation unit 254 calculates the oil film thickness t using a predetermined formula with the bearing load and the temperature of the bearing 6 as parameters. This predetermined formula can be derived, for example, by referring to "High Efficiency Improvement and Tribology of Rolling Bearings" by Hiroki Matsuyama, JTEKT ENGINEERING JOURNAL No. 1009, 2011.

Next, the bearing condition evaluation unit 254 compares the calculated oil film thickness t with the threshold _tth of the past damage database 243 for each output command value. If there is an output command value for which the oil film thickness t is less than _or equal to the threshold tth, the bearing condition evaluation unit 254 evaluates that there is a high probability that the bearing 6 will be damaged when the wind power generation device 10 is started up for that output command value. In this case, for example, the bearing condition evaluation unit 254 displays that output command value on the display unit 23.

On the other hand, if there is an output command value where the oil film thickness t is greater than the threshold _tth , the bearing life calculation unit 255 uses the remaining life database 244 to calculate the remaining life of the bearing 6 when the wind power generation device 10 is started with that output command value (step S15). Step S15 will now be explained with reference to Figure 9.

Figure 9 shows that, for example, if there are three output command values a, b, and c (a < b < c) where the oil film thickness t is greater than the threshold t _th , the bearing life calculation unit 255 first uses the remaining life database 244 to identify the time when the total operating time reaches time T1 as the startup time of the wind power generation device 10, and identifies the remaining life rate of the bearing 6 at time T1. Subsequently, the bearing life calculation unit 255 calculates the change in the remaining life rate of the bearing 6 after time T1, in other words, the life consumption of the bearing 6, for each output command value a, b, and c. At this time, the bearing life calculation unit 255 can calculate the life consumption using average data that represents the relationship between the total operating time and the remaining life rate as a linear function, as shown in Figure 9.

As described above, once the bearing life calculation unit 255 calculates the remaining life of the bearing 6 from the time of startup of the wind power generation system 10, the output setting unit 256 uses the calculation result of the bearing life calculation unit 255 to set the output command value at the time of startup of the wind power generation system 10 (step S16). In step S16, the output setting unit 256 sets the output command value b, which satisfies the condition that the remaining life rate is not 0 when the total operating time of the wind power generation system 10 reaches the design life time T2, among the output command values a to c for which the remaining life has been calculated by the bearing life calculation unit 255, and which has the highest power generation amount, as the output command value at the time of startup of the wind power generation system 10. However, the output setting unit 256 may also consider, for example, the profit obtained from selling electricity, in addition to the lifespan when setting the output command value.

Figure 10 shows an example of the relationship between the total operating time of the wind power generation system 10 and the electricity sales profit for each output command value. In Figure 10, the horizontal axis represents the total operating time of the wind power generation system 10. The vertical axis represents the electricity sales profit. The electricity sales profit is the amount obtained by subtracting the equipment maintenance costs from the electricity sales revenue, which is calculated by multiplying the amount of electricity sold by the electricity sales price. These equipment maintenance costs include the costs incurred for replacing the bearings 6.

The output setting unit 256 predicts the electricity sales profit from the start-up of the wind turbine 10 (time T1) to the design lifespan T2, based on past performance figures or an average of these past performance figures, for each of the output command values a to c. As shown in Figure 9, if output command value c is set at the start-up of the wind turbine 10, the remaining lifespan becomes 0 at time T3, before the design lifespan T2. In this case, the bearing 6 will need to be replaced. Therefore, as shown in Figure 10, if output command value c is set, the electricity sales profit decreases at time T3.

The output setting unit 256 sets the output command value that maximizes electricity sales profit at the design life time T2 as the output command value at startup of the wind power generation device 10. In the example in Figure 10, the electricity sales profit is highest with output command value b at the design life time T2. In this case, the output setting unit 256 sets output command value b as the output command value at startup of the wind power generation device 10.

Furthermore, the method for selecting the output command value by the output setting unit 256 is not limited to the electricity sales profit at the design life time T2. For example, in summer, wind power is weaker, resulting in lower electricity sales profits. Therefore, if the output setting unit 256 estimates that the bearing 6 replacement time (time T3) will be in summer, it may select an output command value c to maximize electricity sales profits. Also, when the output setting unit 256 sets the output command value, parameters such as the availability of spare parts for the bearing 6 may be added. For example, if sufficient spare parts are available, the bearing 6 replacement work can be started immediately, thus shortening the replacement work time. This reduces replacement costs, minimizing the decrease in electricity sales profits.

Finally, the output setting unit 256 outputs the set output command value to the operation control device 40. The operation control device 40 controls the operation of the wind power generation system 10 based on the output command value set by the output setting unit 256.

According to the embodiment described above, the wind power diagnostic and evaluation device 20 predicts the remaining life of the bearing 6 based on the wind load on the blade 3, and sets the output command value at startup of the wind power generation device 10 based on the predicted remaining life. In this way, the wind power diagnostic and evaluation device 20 diagnoses the remaining life of the bearing 6, which is affected by wind conditions via the blade 3, and then sets the output command value. Therefore, the risk of damage to the bearing 6 at startup of the wind power generation device 10 can be reduced.

In this embodiment, the wind power diagnostic and evaluation device 20 may also perform the processes described in steps S11 to S16 even after the wind power generation device 10 has been started. In this case, in step S11, the data acquisition unit 251 also acquires weather forecast data 33. Subsequently, in step S12, the wind condition analysis unit 252 estimates the future wind speed distribution using the weather forecast data 33.

Next, in step S13, the wind load analysis unit 253 predicts the future wind load using the wind speed distribution estimated in step S12. Then, in step S14, the bearing condition evaluation unit 254 calculates the state variable (oil film thickness t) of the bearing 6 using the wind load predicted in step S13. Next, in step S15, the bearing life calculation unit 255 calculates the remaining life of the bearing 6 using the state variable of the bearing 6 calculated in step S14. Finally, in step S16, the output setting unit 256 sets the output command value for the wind power generation system 10 at startup based on the remaining life of the bearing 6 calculated in step S15. In this case, the accuracy of predicting the wind load during operation of the wind power generation system 10 is improved, and therefore the accuracy of calculating the remaining life of the bearing 6 is also improved. Thus, it becomes possible to reduce the risk of damage to the bearing 6 even during operation of the wind power generation system 10.

Furthermore, in this embodiment, if the wind power diagnostic and evaluation device 20 sets the output command value even after the wind power generation device 10 has started up, the bearing condition evaluation unit 254 may evaluate the vibration value measured by a vibration sensor (not shown) installed on the bearing 6 as a condition variable of the bearing 6. If this vibration value is large, the risk of damage to the bearing 6 increases, and its lifespan may be shortened. Therefore, the output setting unit 256 sets the output command value so that the vibration value of the bearing 6 is reduced.

(Variation 1)
The following describes Modification 1 of the First Embodiment. Here, we will focus on the differences from the First Embodiment.

In this embodiment, in step S11, the data acquisition unit 251 also acquires strain measurement data 35. The strain measurement data 35 in this modified example shows the amount of strain measured in the tower 2. This strain measurement data 35 is used in step S13 when the wind load analysis unit 253 analyzes the wind load. Now, referring to Figures 11(a) to 11(d), the wind load analysis step in this modified example will be explained.

Figure 11(a) is a front view of the wind power generation device 10. As shown in Figure 11(a), in this modified example, the strain of the floor at a height H1 from the ground and the strain of the floor at a height H2 (>H1) from the ground are measured by strain sensors (not shown) in the tower 2. Note that at least one floor is required for strain measurement, and more than two floors may be used.

Figure 11(b) is a top view of Tower 2 at a height H1 from the ground. Figure 11(c) is a top view of Tower 2 at a height H2 from the ground. In Figures 11(b) and 11(c), the strain is measured at points where the central angle between the outer perimeter and the center of Tower 2 is 0°, 90°, 180°, and 270°. The front of Tower 2 corresponds to a central angle of 0°. In this modified example, strain is measured at four points on each floor of Tower 2, but at least three or more strain measurement points are sufficient.

The wind load analysis unit 253 calculates the load direction and wind load magnitude for each floor from the strain measurements taken at each measurement point. For example, if the measured values at 0° and 90° are greater than those at 180° and 270° among the four measurement points, the wind load analysis unit 253 identifies the range from 0° to 90° as the load direction range. Furthermore, it calculates the load direction and wind load for each floor based on the difference between the measured values at 0° and 90°.

Figure 11(d) is a schematic diagram showing the direction and magnitude of wind loads on each floor. Figure 11(d) shows the wind load F _H1 at height H1 and the wind load F _H2 at height H2 as vectors. The wind load analysis unit 253 performs vector calculations on these two wind loads to determine the wind load F _H at height H from the ground.

Next, the wind load analysis unit 253 outputs a value as the wind load analysis result obtained by multiplying the wind load F selected from the load analysis database 242 described in the first embodiment and the wind load _FH calculated based on the amount of strain of the tower 2 by the same or different weighting coefficients and adding them together. After that, the processing of steps S14 to S16 is executed in the same manner as in the first embodiment.

According to the modified configuration described above, the wind load analysis unit 253 analyzes the wind load using not only the wind condition analysis results but also the strain amount of the tower 2. Therefore, the wind load can be calculated with high accuracy. This also improves the accuracy of predicting the remaining life of the bearing 6. Consequently, it becomes possible to set the output command value at the start-up of the wind power generation device 10 more appropriately.

(Variation 2)
The following describes a second modification of the first embodiment. Here again, the focus will be on the differences from the first embodiment.

In this embodiment, in step S11, the data acquisition unit 251 also acquires strain measurement data 35. The strain measurement data 35 in this modified example is data measuring the amount of strain of the blade 3. The acquired strain measurement data 35 is used in step S13 when the wind load analysis unit 253 analyzes the wind load. Now, referring to Figures 12(a) to 12(c), the wind load analysis step in this modified example will be explained.

Figure 12(a) is a front view of the wind turbine 10. As shown in Figure 12(a), in this modified example, the strain amounts at four measurement points i, j, k, and m, each at a different distance from the base of each blade 3, are measured by a strain sensor (not shown). Note that the number of strain measurement points is not limited to four; at least two are sufficient.

Figure 12(b) shows an example of strain measurement data 35. Figure 12(b) shows the change in strain measured at measurement point m over time. The wind load analysis unit 253 averages the strain at each measurement point over a certain period of time. Subsequently, the wind load analysis unit 253 calculates the wind load at each measurement point using a functional equation based on the average strain value, the material constants of the blade 3, and the azimuth angle of the blade 3.

Figure 12(c) shows the relationship between wind load and azimuth angle. In Figure 12(c), the horizontal axis represents the azimuth angle of the blade 3, and the vertical axis represents the wind load. The wind load analysis unit 253 calculates the load and moment applied to the base of the blade 3 by integrating the wind loads at the four measurement points i, j, k, and m shown in Figure 12(c). A nacelle 1 is provided at the base of the blade 3, and the bearing 6 is housed in the nacelle 1. Therefore, the load at the base of the blade 3 is close to the load applied to the bearing 6.

Next, the wind load analysis unit 253 outputs a value as the wind load analysis result, obtained by multiplying the wind load F selected from the load analysis database 242 described in the first embodiment and the wind load calculated based on the strain amount of the blade 3 by the same or different weighting coefficients and adding them together. After that, the processing of steps S14 to S16 is executed, similar to the first embodiment.

According to the modified configuration described above, the wind load analysis unit 253 analyzes the wind load using not only the wind condition analysis results but also the strain amount of the blade 3. Therefore, even in this modified configuration, the wind load can be calculated with high accuracy. This also improves the accuracy of predicting the remaining life of the bearing 6. Consequently, it becomes possible to set the output command value at the start-up of the wind power generation device 10 more appropriately.

In this modified example, the wind load analysis unit 253 may use the amount of strain of the tower 2 described in Modification 1 when analyzing the wind load. In this case, the wind load analysis unit 253 outputs a value as the wind load analysis result obtained by multiplying the wind load F selected from the load analysis database 242 described in the first embodiment, the wind load _FH based on the amount of strain of the tower 2 described in the first modified example, and the wind load based on the amount of strain of the blade 3 described in this modified example by the same or different weighting coefficients and adding them together. As a result, the wind load is calculated based on the wind condition analysis result, the amount of strain of the blade 3, and the amount of strain of the tower 2, so the accuracy of the wind load is further improved. Therefore, the accuracy of predicting the remaining life of the bearing 6 is also further improved, making it possible to further reduce the risk of damage to the bearing 6 when the wind power generation device 10 is started up.

(Variation 3)
The following describes a third modification of the first embodiment. Here again, the focus will be on the differences from the first embodiment.

In this embodiment, in step S11, the data acquisition unit 251 also acquires displacement measurement data 34. As described in the first embodiment, the displacement measurement data 34 is data measuring the radial displacement δ of the bearing 6 (see Figure 2). The acquired displacement measurement data 34 is used in step S14 when the bearing condition evaluation unit 254 analyzes the bearing load. The bearing load analysis step according to this modified example will now be described.

The bearing condition evaluation unit 254 calculates the amount the rotating shaft 5 is pushed in and the tilt of the rotating shaft 5 based on the displacement δ measured at at least three measurement points. Subsequently, the bearing condition evaluation unit 254 calculates the bearing load and moment generated by the pushing in and tilt of the rotating shaft 5, based on the relationship formula with the displacement of the bearing 6.

Next, the bearing condition evaluation unit 254 obtains the bearing load analysis result by multiplying the bearing load calculated from the bearing element model described in the first embodiment and the bearing load based on the displacement of the bearing 6 by the same or different weighting coefficients and adding them together. After that, the same processing as in the first embodiment is performed.

According to the modified version described above, the bearing load is calculated not only based on wind condition analysis and wind load analysis, but also on the displacement of the bearing 6. Therefore, the accuracy of the bearing load calculation is improved. Consequently, the accuracy of predicting the remaining life of the bearing 6 is also improved, making it possible to further reduce the risk of damage to the bearing 6 during the startup of the wind power generation device 10.

(Second Embodiment)
Figure 13 is a block diagram showing the schematic configuration of the wind power diagnostic and evaluation device 20a and data acquisition device 30 according to the second embodiment. In Figure 13, the same reference numerals are used for the same components as in the first embodiment described above, and detailed explanations are omitted.

The wind power diagnostic and evaluation device 20a according to this embodiment further includes a control unit 26 in addition to the components of the wind power diagnostic and evaluation device 20 according to the first embodiment. The control unit 26 controls the operation of the wind power diagnostic and evaluation device 20 based on the output command value set by the calculation unit 25. In other words, the functions of the operation control device 40 described in the first embodiment are built into the control unit 26 of the wind power diagnostic and evaluation device 20a according to this embodiment. Now, referring to Figure 14, the control operation of the control unit 26 during the startup of the wind power generation device 10 will be described.

Figure 14 is a flowchart showing the control operation procedure of the control unit 26 according to the second embodiment. In this flowchart, first, the control unit 26 acquires data indicating the output command value set by one of the methods described in the first embodiment or modifications 1 to 3, as well as wind speed data, from the calculation unit 25 (step S21). This wind speed data may be SCADA data 31 from the data acquisition device 30, or wind condition measurement data 36.

Next, the control unit 26 determines whether the measured wind speed shown in the wind speed data satisfies the wind speed condition that is greater than the cut-in wind speed and less than the cut-out wind speed (step S22). The cut-in wind speed is the minimum wind speed at which the wind power generator 10 can generate electricity. On the other hand, the cut-out wind speed is the maximum wind speed at which the wind power generator 10 can generate electricity.

When the measured value meets the above wind speed conditions, the control unit 26 starts the wind turbine 10 with the output command value set by the calculation unit 25 (step S23). The wind turbine 10 sets the pitch angle of the blades 3 and the torque value of the generator 8 so that the amount of power generated reaches the output command value.

In this embodiment, if the calculation unit 25 continues setting the output command value even after the wind power generation device 10 has started up, the control unit 26 also continues to control the operation of the wind power generation device 10 based on the output command value set by the calculation unit 25.

In this embodiment, as described above, the output command value at startup of the wind turbine 10 is set after diagnosing the remaining lifespan of the bearing 6, which is affected by wind conditions via the blades 3. Therefore, the risk of damage to the bearing 6 at startup of the wind turbine 10 can be reduced.

Furthermore, in this embodiment, since the wind power diagnostic and evaluation device 20a also has an operation control function for the wind power generation device 10, the operation control device 40 described in the first embodiment becomes unnecessary. This makes it possible to achieve operation control of the wind power generation device 10 using optimized output command values with a single device.

While several embodiments have been described above, these embodiments are presented only as examples and are not intended to limit the scope of the invention. The novel system described herein can be implemented in various other forms. Furthermore, various omissions, substitutions, and modifications can be made to the system embodiments described herein without departing from the spirit of the invention. The appended claims and equivalents are intended to include such forms and modifications included in the scope and spirit of the invention.

1: Nacelle 2: Tower 3: Blade 4: Hub 5: Rotating shaft 6: Bearing 7: Transmission mechanism 8: Generator 9: Wind condition measuring instrument 10: Wind power generation device 20, 20a: Wind force diagnostic evaluation device 21: Communication unit 22: Operation unit 23: Display unit 24: Storage unit 25: Calculation unit 26: Control unit 30: Data acquisition device 31: SCADA data 32: Temperature measurement data 33: Weather forecast data 34: Displacement measurement data 35: Strain measurement data 36: Wind condition measurement data 40: Operation control device 50: Wind condition measurement device 241: Wind condition analysis database 242: Load analysis database 243: Past damage database 244: Remaining life database 251: Data acquisition unit 252: Wind condition analysis unit 253: Wind load analysis unit 254: Bearing condition evaluation unit 255: Bearing life calculation unit 256: Output setting unit

Claims (15)

A wind power diagnostic and evaluation device for diagnosing and evaluating a wind power generation device comprising at least a blade that rotates with wind power, a generator that generates electricity using the rotational force of the blade, a rotating shaft that transmits the rotational force to the generator, and a rolling bearing installed on the rotating shaft,
A wind load analysis unit analyzes the wind load applied to the blade by the wind force,
Based on the analysis results of the wind load analysis unit, a bearing state evaluation unit evaluates the state quantities of the rolling bearings at the time of startup of the wind power generation device,
Based on the evaluation results of the bearing condition evaluation unit, an output setting unit sets an output command value including the amount of power generated by the wind power generation device at startup,
A wind power diagnostic and evaluation device equipped with the following features. The wind load analysis unit continues to analyze the wind load even after the wind power generation device has been started.
The bearing condition evaluation unit predicts the state quantity after startup based on the analysis results of the wind load after startup,
The wind force diagnostic and evaluation apparatus according to claim 1, wherein the output setting unit sets the output command value based on the state quantity after startup. The system further includes a wind condition analysis unit for analyzing wind conditions related to the aforementioned wind power,
The wind load analysis unit calculates the wind load based on the analysis results of the wind condition analysis unit, as described in claim 1 or 2. The wind condition analysis unit calculates the average wind speed as a result of the analysis,
The wind load analysis unit calculates the average value of the wind load based on the average wind speed,
The wind force diagnostic and evaluation device according to claim 3, wherein the bearing condition evaluation unit evaluates the condition quantity based on the average value. The wind condition analysis unit calculates the turbulence intensity as a result of the wind condition analysis,
The wind load analysis unit calculates the maximum value of the wind load based on the turbulence intensity,
The wind force diagnostic and evaluation apparatus according to claim 3, wherein the bearing condition evaluation unit predicts the condition quantity based on the maximum value. The system further includes a storage unit that stores a wind condition analysis database showing the vertical wind speed distribution for each of multiple wind condition conditions, and a load analysis database showing the wind load for each of multiple load analysis conditions corresponding to the wind speed distribution for each of the output command values.
The wind condition analysis unit selects the wind speed distribution using the wind condition analysis database,
The wind load analysis unit uses the load analysis database to select a wind load corresponding to the wind speed distribution identified by the wind condition analysis unit for each of the multiple output command values, as described in claim 3, for the wind load analysis device according to claim 3. The wind power generation device further comprises a nacelle housing the generator, the rotating shaft, and the rolling bearings , and a tower supporting the nacelle.
The wind load analysis unit calculates the wind load applied to the blades using the amount of strain of the tower measured on at least one floor, as described in claim 6. The wind load analysis unit calculates the wind load applied to the blade using the amount of strain of the blade measured at three or more measurement points at different distances from the base of the blade, as described in claim 6 of the wind force diagnostic and evaluation apparatus. The wind force diagnostic and evaluation device according to claim 6, wherein the bearing condition evaluation unit calculates the bearing load applied to the rolling bearing using the displacement of the rolling bearing measured at at least three measurement points, and evaluates the condition quantity using the calculated bearing load. The wind force diagnostic and evaluation apparatus according to claim 6, wherein the bearing condition evaluation unit evaluates the thickness of the oil film formed on the rolling bearing as the condition quantity. The memory unit further stores a past damage database that indicates thresholds set based on the oil film thickness when the rolling bearing was damaged in the past.
The wind force diagnostic and evaluation apparatus according to claim 10, wherein the bearing condition evaluation unit evaluates the condition quantity based on the result of comparing the thickness of the oil film with the threshold value. The system further includes a bearing life calculation unit that calculates the remaining life of the rolling bearings at startup of the wind power generation device based on the evaluation results of the bearing condition evaluation unit.
The wind force diagnostic and evaluation apparatus according to claim 6, wherein the output setting unit sets the output command value based on the calculation result of the bearing life calculation unit. The wind power diagnostic and evaluation device according to claim 12, wherein the output setting unit sets the output command value based on the calculation result of the bearing life calculation unit, the electricity sales profit from the start-up of the wind power generation device to the design life time, and the cost incurred by replacing the rolling bearings . A wind power diagnostic and evaluation method for diagnosing and evaluating a wind power generation device comprising at least a blade that rotates with wind power, a generator that generates electricity using the rotational force of the blade, a rotating shaft that transmits the rotational force to the generator, and a rolling bearing installed on the rotating shaft,
The wind load applied to the blade by the wind force is analyzed,
Based on the analysis results of the wind load, the state quantities of the rolling bearings at the time of startup of the wind power generation device are evaluated.
A wind power diagnostic evaluation method that sets an output command value, including the amount of power generated by the wind power generation device at startup, based on the results of evaluating the state quantities of the rolling bearings . A program for diagnosing and evaluating a wind power generation device comprising at least a blade that rotates with wind power, a generator that generates electricity using the rotational force of the blade, a rotating shaft that transmits the rotational force to the generator, and a rolling bearing installed on the rotating shaft,
A process for analyzing the wind load applied to the blade by the wind force,
Based on the analysis results of the wind load, a process is performed to evaluate the state quantities of the rolling bearings at the time of startup of the wind power generation device,
Based on the results of evaluating the state quantities of the rolling bearings , a process is performed to set an output command value including the amount of power generated by the wind power generation device at startup.
A program that causes a computer to execute something.