Self-consumption power evaluation method, device, medium and wind turbine generator set

Abstract

The present disclosure provides a self-consumption power evaluation method, device, medium and wind turbine generator set. The self-consumption power evaluation method comprises: obtaining rated active power of each electrical subsystem consuming power in the wind turbine generator set and the number of each electrical subsystem; determining the operation load of each electrical subsystem according to the rated active power of each electrical subsystem and the corresponding demand coefficient; and determining the self-consumption power of the wind turbine generator set based on the operation load of each electrical subsystem and the number of the corresponding electrical subsystem. The self-consumption power evaluation method according to the embodiment of the present disclosure can accurately calculate the self-consumption power of the machine set, make the design power curve closer to the actual operation curve, and ensure the design quality of the machine set.

Description

Technical Field

[0001] This disclosure generally relates to the field of wind power, and more specifically, to a method, apparatus, medium, and wind turbine generator for assessing self-consumption power. Background Technology

[0002] During the product development phase, the power loss of wind turbine generator sets is crucial for evaluating the unit's power curve, overall load, and economic viability.

[0003] The power loss of a wind turbine can vary significantly depending on its operating status and environmental parameters.

[0004] Currently, the power loss (i.e., self-consumed power) of wind turbine generators is mainly obtained by separately estimating the losses of the main power-consuming components that cause unit losses and then superimposing them.

[0005] However, the main components of a wind turbine generator that affect power loss include: converters, cables, pitch motors, yaw motors, generator lighting, and generator cooling systems.

[0006] The loss curve (loss between rotor input power and grid-connected power at different grid-connected power levels) has a significant impact on power generation. The power loss assessment methods vary for different models, making it difficult to accurately obtain the wind turbine's own power loss, which reduces the design quality and efficiency of the wind turbine. Summary of the Invention

[0007] One of the objectives of the exemplary embodiments disclosed herein is to provide a self-consumption power assessment method capable of accurately calculating the self-consumption power of a wind turbine generator set.

[0008] One of the objectives of the exemplary embodiments disclosed herein is to establish a calculation model for the self-consumption power of wind turbine generators, so that the design power curve is closer to the actual operating curve.

[0009] According to one aspect of this disclosure, a method for assessing the self-consumption power of a wind turbine generator set is provided. The method includes: obtaining the rated active power of each electrical subsystem consuming power in the wind turbine generator set and the number of each electrical subsystem; determining the operating load of each electrical subsystem based on the rated active power of each electrical subsystem and the corresponding demand coefficient; and determining the self-consumption power of the wind turbine generator set based on the operating load of each electrical subsystem and the corresponding number of electrical subsystems.

[0010] According to another aspect of this disclosure, a self-consumption power assessment device for a wind turbine generator set is provided. The self-consumption power assessment device includes: a parameter acquisition unit for acquiring the rated active power of each electrical subsystem consuming power in the wind turbine generator set and the number of each electrical subsystem; and a calculation unit for determining the operating load of each electrical subsystem based on the rated active power of each electrical subsystem and the corresponding demand coefficient, and determining the self-consumption power of the wind turbine generator set based on the operating load of each electrical subsystem and the corresponding number of electrical subsystems.

[0011] According to another aspect of this disclosure, a self-consumption power assessment device for a wind turbine generator set is provided. The self-consumption power assessment device includes a memory and a processor. The memory stores programs or instructions that, when executed by the processor, implement the self-consumption power assessment method as described above.

[0012] According to another aspect of this disclosure, a computer-readable storage medium is provided that stores instructions or programs that, when executed by a processor, implement the self-consumption power assessment method for wind turbine generators as described above.

[0013] According to another aspect of this disclosure, a wind turbine generator set is provided, which includes the self-consumption assessment device as described above or the computer-readable storage medium as described above.

[0014] The self-consumption power assessment method and self-consumption power assessment device according to the embodiments of this disclosure divide the grid-connected power into sections and establish a model of self-consumption load, grid-connected power, and ambient temperature. Attached Figure Description

[0015] The above and other objects and features of exemplary embodiments of this disclosure will become clearer from the following description taken in conjunction with the accompanying drawings, which exemplarily illustrate the embodiments, wherein:

[0016] Figure 1 This is a flowchart illustrating a self-consumption power evaluation method according to a first embodiment of the present disclosure;

[0017] Figure 2 This is a flowchart illustrating the determination of the operating load of various electrical subsystems according to embodiments of the present disclosure;

[0018] Figure 3 This is a flowchart illustrating the transfer function between the grid-connected power correlation coefficient of each section and the ambient temperature according to embodiments of the present disclosure;

[0019] Figure 4 This is a block diagram illustrating a self-consumption power evaluation device according to an embodiment of the present disclosure. Detailed Implementation

[0020] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings, examples of which are illustrated in the drawings, wherein the same reference numerals always refer to the same parts. The embodiments will now be described with reference to the accompanying drawings in order to explain this disclosure.

[0021] The self-consumption power assessment method and self-consumption power assessment apparatus according to embodiments of the present disclosure are used to assess or calculate the self-consumption power of wind turbine generator sets.

[0022] According to the self-consumption power assessment method and self-consumption power assessment device of the embodiments of this disclosure, the operating load of each power-consuming sub-component is determined by the rated active power and demand factor of each power-consuming component.

[0023] According to embodiments of this disclosure, the operating load of each power-consuming sub-component is determined based on the grid-connected power. The operating load of each power-consuming sub-component in each power segment can be assessed by dividing the grid-connected power into sections. Furthermore, the final demand factor can be determined based on the ambient temperature.

[0024] Exemplary embodiments of this disclosure will now be described in conjunction with the accompanying drawings.

[0025] Figure 1 This is a flowchart illustrating a self-consumption power evaluation method according to a first embodiment of the present disclosure.

[0026] In step S110, the rated active power of each electrical subsystem consuming electricity in the wind turbine generator set and the number of each electrical subsystem are obtained.

[0027] The rated active power of each electrical subsystem is known, and the rated active power and quantity of each electrical subsystem can be obtained from the design data.

[0028] As an example, the electrical subsystems that consume power in a wind turbine generator set may include converters, cables, pitch motors, yaw motors, generator-mounted lighting, and generator cooling systems (e.g., generator cooling fans). For example, the rated active power of the generator cooling fans and the number of generator cooling fans (e.g., 10) can be obtained from design data.

[0029] In step S120, the operating load of each electrical subsystem is determined based on the rated active power of each electrical subsystem and the corresponding demand factor.

[0030] The corresponding demand factors can be predetermined. The actual load of each electrical subsystem consuming power in a wind turbine generator set is related to the grid-connected power of the wind turbine generator set, and the operating load of each electrical subsystem can be determined based on the grid-connected power. Furthermore, the actual load of each electrical subsystem consuming power in a wind turbine generator set can be related to the grid-connected power and the ambient temperature. In this case, the demand factor of each electrical subsystem can be determined based on both the grid-connected power of the wind turbine generator set and the ambient temperature.

[0031] In step S130, the self-consumption power of the wind turbine generator is determined based on the operating load of each electrical subsystem and the number of corresponding electrical subsystems.

[0032] The operating load of each electrical subsystem can be calculated as the product of the operating load of each electrical subsystem and the number of corresponding electrical subsystems. The self-discharge power of a wind turbine generator is the sum of the total operating loads of all electrical subsystems.

[0033] For example, when the electrical subsystems of a wind turbine include lighting and cooling devices, assuming that the number of lighting and cooling devices is 10 each, the rated active power of each lighting device is P1 and the demand factor is K1, and the rated active power of each cooling device is P2 and the demand factor is K2, then the self-consumption power of the wind turbine is: 10×P1×K1+10×P2×K2.

[0034] Note that the numbers of lighting devices and unit cooling devices mentioned here refer to the actual number of lighting devices and unit cooling devices that are activated during operation. If only a portion of the lighting devices and unit cooling devices (e.g., 8 lighting devices and 4 unit cooling devices) are in operation, then the self-discharge power of the wind turbine generator can be 8×P1×K1+4×P2×K2.

[0035] As mentioned above, the corresponding demand factor is related to the grid-connected power. The grid-connected power of wind turbine generators can be divided into multiple sections, and then the operating load of each electrical subsystem in each section can be determined.

[0036] Figure 2 This is a flowchart illustrating the determination of the operating load of various electrical subsystems according to embodiments of the present disclosure.

[0037] The step S120, which determines the operating load of each electrical subsystem based on its rated active power and corresponding demand factor, may include steps S1210 and S1220.

[0038] In step S1210, the grid-connected power of the wind turbine generator is divided into multiple sections.

[0039] The grid-connected power within a certain statistical time period can be divided into equal intervals. For example, the grid-connected power can be divided into equal intervals of 50kW.

[0040] As an example, 0 to 50 kW can be used as the first segment (that is, the power points of the above electrical subsystems within the grid-connected power of 0 kW to 50 kW are uniformly divided into segments and averaged), 50 kW to 100 kW can be used as the second segment, and so on, until the rated grid-connected power Pn of the wind turbine generator set is reached.

[0041] In step S1220, the operating load of each electrical subsystem in each warehouse section is determined based on the rated active power of each electrical subsystem and the demand coefficient of each electrical subsystem in each warehouse section.

[0042] In other words, the demand coefficients of each electrical subsystem can be different in each section. The larger the grid-connected power, the larger the demand coefficients of each electrical subsystem in each section can be.

[0043] For example, the larger the grid-connected power, the greater the demand factor for the generator cooling fans, which are part of the power-consuming electrical subsystem of the wind turbine generator set. Therefore, the number of generator cooling fans (the actual number started and running) can be increased. The number of generator cooling fans is generally the total number of generator cooling fans, but a portion of the generator cooling fans can also be activated based on the grid-connected power.

[0044] Step S1220, which determines the self-consumption power of the wind turbine generator set based on the operating load of each electrical subsystem and the corresponding number of electrical subsystems, includes:

[0045] Based on the product of the operating load of each electrical subsystem in each section and the corresponding number of electrical subsystems, the self-consumption power of each electrical subsystem in each section is determined, and the sum of the self-consumption power of each electrical subsystem in each section is taken as the self-consumption power of the wind turbine generator set.

[0046] As an example, the demand coefficient of each electrical subsystem in each section can be calculated by multiplying the grid-connected power correlation coefficient and the correction coefficient of each electrical subsystem in each section of grid-connected power.

[0047] The correction factor here is primarily used to correct for calculation errors caused by the difference between the actual operating power and the rated active power of the various electrical subsystems consuming electricity in a wind turbine generator set. As an example, the correction factor can also be used to correct errors caused by the actual operating time and the actual number of operating subsystems.

[0048] The grid-connected power correlation coefficient of each warehouse section can be calculated by the ratio of the operating time of each electrical subsystem in each warehouse section to the total time of each warehouse section. Furthermore, the correction coefficient of each warehouse section can be calculated by the ratio R1 of the actual operating power of each electrical subsystem in each warehouse section to the rated active power of each electrical subsystem.

[0049] If the various electrical subsystems are not always operating in the corresponding warehouse section, or if a part of a certain electrical subsystem in the corresponding warehouse section is not operating, the above correction factors can also be used to correct this.

[0050] Here, the total time for each storage segment is the product of the total number of data points for each corresponding storage segment and the time for a single data point. The time for a single data point is calculated by multiplying the self-consumption power calculation cycle of each electrical subsystem by a predetermined value.

[0051] For example, assuming that the power consumption calculation cycle of a certain electrical subsystem is 20ms, and 50 power calculation cycles are used as a data statistical value, that is, one data point per second, the total number of data points in the corresponding grid-connected power section is the total time of the section. If there are 1000 data points in the grid-connected power section (100kW~150kW), and one data point per second, then the total time of the grid-connected power section is 1000s.

[0052] As an example, if the generator cooling fan is turned on for 400 seconds in the grid-connected power range (100kW~150kW), then the correction factor can be 0.4R1.

[0053] As an example, if only 8 out of 10 generator cooling fans are generator cooling fans in a 100kW to 150kW compartment, then the correction factor for the generator cooling fans, which are one of the various electrical subsystems, in the 100kW to 150kW compartment is 0.4×0.8R1.

[0054] In other words, when only a portion of each electrical subsystem is operating in a certain section, the correction factor for each section can be calculated using the ratio R1 of the actual operating power of each electrical subsystem in each section to its rated active power, and the proportion of equipment actually operating in each section for each electrical subsystem. The correction factor for each electrical subsystem in each section can also be calculated using the ratio R1 of the actual operating power of each electrical subsystem in each section to its rated active power, the proportion of equipment actually operating in each section for each electrical subsystem, and the proportion of the actual operating time of each electrical subsystem in each section to the total time of that section.

[0055] The grid-connected power correlation coefficients for each section can be further determined based on ambient temperature. In other words, the grid-connected power correlation coefficients for each electrical subsystem may differ under different ambient temperatures.

[0056] The self-consumption power assessment method according to embodiments of the present disclosure may further include: determining the grid-connected power correlation coefficient of each electrical subsystem in each warehouse section at the ambient temperature based on the ambient temperature of the wind turbine generator set and a pre-determined transfer function between the grid-connected power correlation coefficient of each electrical subsystem in each warehouse section and the ambient temperature.

[0057] The system can record the correlation coefficient of grid-connected power and the corresponding ambient temperature for each section, thereby determining the mathematical relationship between the ambient temperature and the correlation coefficient of grid-connected power. For example, the mathematical relationship between ambient temperature and the correlation coefficient of grid-connected power can be fitted using curve fitting, and the correlation coefficient of grid-connected power at a specific temperature can be calculated given the ambient temperature.

[0058] Furthermore, the ambient temperature can be divided into zones, and then the transfer function between the grid-connected power correlation coefficient and the ambient temperature for each compartment within each ambient temperature range can be determined. As an example, the grid-connected power correlation coefficients of all electrical subsystems within a certain compartment can be determined to be the same for a given ambient temperature range. When the ambient temperature range is small, the grid-connected power correlation coefficients of all electrical subsystems within a certain compartment can be determined to be the same for that range.

[0059] Figure 3 This is a flowchart illustrating the transfer function between the grid-connected power correlation coefficient of each section and the ambient temperature, according to embodiments of the present disclosure.

[0060] The steps for determining the transfer function may include steps S310, S320, S330, and S340.

[0061] In step S310, the highest ambient temperature and the most recent ambient temperature within a predetermined historical time period are statistically analyzed.

[0062] It can statistically analyze the highest ambient temperature and the most recent ambient temperature within a test year. For example, it can statistically analyze that the highest ambient temperature in the area where the wind turbine is located in 2015 was 40℃ and the most recent ambient temperature was 15℃.

[0063] In step S320, the temperature is divided based on the highest ambient temperature and the most recent ambient temperature to obtain multiple ambient temperature ranges. For example, if the highest ambient temperature in the area where the wind turbine is located in 2015 is 40℃ and the most recent ambient temperature is 15℃, then five temperature ranges are obtained at 5℃ intervals.

[0064] In step S330, the correlation coefficient of grid-connected power and the corresponding ambient temperature in each warehouse section are calculated.

[0065] The correlation coefficient of grid-connected power may vary depending on the ambient temperature range and the temperature range of the warehouse. It may also vary within the same warehouse.

[0066] In step S340, the transfer function between the grid-connected power correlation coefficient of each section and the ambient temperature is determined for each ambient temperature range.

[0067] For example, within the same grid-connected power range, the transfer function between the grid-connected power correlation coefficient and ambient temperature for various electrical subsystems can be considered as a temperature coefficient. Assuming that the grid-connected power correlation coefficient for the 100kW-150kW range is 0.4 within an ambient temperature range of 15℃ to 20℃, then within an ambient temperature range of 20℃ to 25℃, due to the increased ambient temperature, the operating time of each electrical subsystem within the 100kW-150kW range is longer (e.g., 600s), resulting in a higher grid-connected power correlation coefficient (e.g., 0.6). With more ambient temperature ranges, the transfer function between the grid-connected power correlation coefficient and ambient temperature for the 100kW-150kW range can be obtained (which can be determined through curve fitting). Other grid-connected power ranges can also be calculated and determined using this method.

[0068] Figure 4 This is a block diagram illustrating a self-consumption power evaluation device according to an embodiment of the present disclosure.

[0069] The self-consumption power evaluation device 400 according to embodiments of the present disclosure may include a parameter acquisition unit 410 and a calculation unit 420.

[0070] The parameter acquisition unit 410 can acquire the rated active power of each electrical subsystem consuming power in the wind turbine generator set and the quantity of each electrical subsystem. For example, the parameter acquisition unit 410 can store the rated power of each electrical subsystem consuming power in the wind turbine generator set and the quantity of each type of electrical subsystem.

[0071] The calculation unit 420 can determine the operating load of each electrical subsystem based on the rated active power and the corresponding demand coefficient of each electrical subsystem, and determine the self-consumption power of the wind turbine generator set based on the operating load of each electrical subsystem and the number of corresponding electrical subsystems.

[0072] The calculation unit 420 can determine the operating load of each electrical subsystem by multiplying its rated active power by the corresponding demand factor. The calculation unit 420 can further determine the self-consumption power of the wind turbine generator set based on the operating load of each electrical subsystem and the number of corresponding electrical subsystems.

[0073] The corresponding number of electrical subsystems here refers to the number of electrical subsystems actually in operation, which can be determined based on the start-up and shutdown status of each electrical subsystem.

[0074] The calculation unit 420 can divide the grid-connected power of the wind turbine generator into multiple compartments. Based on the rated active power of each electrical subsystem and the demand coefficient of each electrical subsystem in each compartment, the operating load of each electrical subsystem in each compartment is determined.

[0075] When dividing grid-connected power into equal-interval sections, the power can be divided into multiple equal-interval sections. The number of sections can be determined by the grid-connected power and the interval. Generally speaking, the more sections there are, the higher the accuracy of determining the self-discharge power of the wind turbine generator.

[0076] The calculation unit 420 can determine the operating load of each electrical subsystem in each warehouse section based on the product of the rated active power of each electrical subsystem and the demand coefficient of each electrical subsystem in each warehouse section.

[0077] As an example, the calculation unit 420 can further determine the self-consumption power of each electrical subsystem in each section based on the operating load of each electrical subsystem in each section and the corresponding number of electrical subsystems, and take the sum of the self-consumption power of each electrical subsystem in each section as the self-consumption power of the wind turbine generator set.

[0078] The calculation unit 420 can calculate the demand factor of each electrical subsystem in each section based on the grid-connected power correlation coefficient and correction coefficient of each electrical subsystem in each section of grid-connected power. The calculation unit 420 can calculate the grid-connected power correlation coefficient of each section by using the ratio of the operating time of each electrical subsystem in each section to the total time of each section, and can calculate the correction coefficient of each section by using the ratio of the actual operating power of each electrical subsystem in each section to the rated active power of each electrical subsystem.

[0079] As an example, the correction factor for each section can be determined by the ratio of the actual operating power of each electrical subsystem in each section to the rated active power of each electrical subsystem, the ratio of the operating time of each electrical subsystem in each section to the total time, and the ratio of the number of times each electrical subsystem operates in each section to the total number.

[0080] The calculation unit 420 can also determine the grid-connected power correlation coefficient of each electrical subsystem in each section under the ambient temperature, based on the ambient temperature of the wind turbine generator set and the transfer function between the pre-determined grid-connected power correlation coefficient of each electrical subsystem in each section and the ambient temperature.

[0081] The calculation unit 420 can record the correlation coefficient of grid-connected power and the corresponding ambient temperature of each section, thereby determining the mathematical relationship between ambient temperature and the correlation coefficient of grid-connected power.

[0082] As an example, the calculation unit 420 can fit the mathematical relationship between ambient temperature and grid-connected power correlation coefficient by curve fitting. Given the ambient temperature, the grid-connected power correlation coefficient at a specific temperature or a specific temperature range can be calculated.

[0083] Specifically, the calculation unit 420 can statistically analyze the highest ambient temperature and the most recent ambient temperature within the test year. For example, the calculation unit 420 statistically analyzes that the highest ambient temperature in the area where the wind turbine is located in 2015 was 40°C and the most recent ambient temperature was 15°C.

[0084] The calculation unit 420 can divide the temperature based on the highest ambient temperature and the most recent ambient temperature to obtain multiple ambient temperature ranges. For example, if the highest ambient temperature in the area where the wind turbine is located in 2015 was 40℃ and the most recent ambient temperature was 15℃, five temperature ranges are obtained at 5℃ intervals.

[0085] The calculation unit 420 can statistically analyze the correlation coefficient of grid-connected power and the corresponding ambient temperature in each storage section, and determine the transfer function between the correlation coefficient of grid-connected power and ambient temperature in each storage section under each ambient temperature range.

[0086] As mentioned above, due to the increase in ambient temperature, the operating time of each electrical subsystem in the grid-connected power range of 100kW to 150kW is longer, and the grid-connected power correlation coefficient is higher (e.g., 0.6). If there are more ambient temperature ranges, the transfer function of the grid-connected power correlation coefficient and ambient temperature for the grid-connected power range (100kW to 150kW) can be obtained (which can be determined by curve fitting). Other grid-connected power ranges can also be calculated and determined in the same way.

[0087] Although the above shows the calculation unit performing steps such as ambient temperature statistics and transfer function determination, this is just an example. As mentioned above, the ambient temperature statistics and transfer function determination can be predetermined. The calculation unit can directly store the relevant transfer functions and directly determine the grid-connected power correlation coefficient when the ambient temperature or the ambient temperature range is known.

[0088] Each of the above steps can be written as a software program or instructions. Therefore, the self-power consumption assessment method according to the exemplary embodiments of the present disclosure can be implemented via software. The computer-readable storage medium of the exemplary embodiments of the present disclosure can store a computer program that, when executed by a processor, implements the self-power consumption assessment method as described in the exemplary embodiments above.

[0089] Computer-readable storage media include non-transitory computer-readable storage media, such as magnetic media like floppy disks and magnetic tapes, optical media (including optical disc (CD) ROMs and DVD ROMs), magneto-optical media like flexible optical discs, hardware devices such as ROMs, RAMs, and flash memory designed for storing and executing program instructions. The program instructions include language code executable by a computer using an interpreter and machine language code generated by a compiler.

[0090] In one example, the instructions or software comprise machine code (such as machine code generated by a compiler) that is directly executed by one or more processors or computers. In another example, the instructions or software comprise higher-level code that is executed by one or more processors or computers using an interpreter. The instructions or software can be written using any programming language based on the block diagrams and flowcharts shown in the accompanying drawings and the corresponding descriptions in the specification.

[0091] According to various embodiments of this disclosure, apparatus (e.g., modules or their functions) or methods can be implemented by programs or instructions stored in a computer-readable storage medium. When such instructions are executed by a processor, the processor can perform a function corresponding to the instructions or a method corresponding to the instructions (e.g., a self-consumption evaluation method). At least a portion of a module can be implemented (e.g., executed) by a processor. At least a portion of a programmed module can include modules, programs, routines, instruction sets, and procedures for performing at least one function.

[0092] The self-consumption power assessment method according to embodiments of this disclosure can be implemented in software, hardware, or a combination thereof. The hardware device can be implemented using one or more software modules for performing the operations of the various embodiments of this disclosure.

[0093] The modules or programming modules disclosed herein may include at least one of the aforementioned components, with some components omitted or others added. The operations of the modules, programming modules, or other components may be executed sequentially, in parallel, cyclically, or probingly. Furthermore, some operations may be executed in a different order, may be omitted, or may be extended with other operations.

[0094] The computer-readable storage medium and / or self-consumption power assessment device of the exemplary embodiments of this disclosure may be part of a wind turbine generator set.

[0095] According to an exemplary embodiment of the present disclosure, a self-consumption power assessment device may be provided, which may include a processor (not shown) and a memory (not shown), wherein the memory stores a computer program, and when the computer program is executed by the processor, implements the self-consumption power assessment method of the wind turbine generator set as described in the exemplary embodiment above.

[0096] The self-consumption power assessment method and self-consumption power assessment device according to the embodiments of this disclosure establish a self-consumption power assessment model of the wind turbine generator set based on the grid-connected power, making the overall loss calculation more accurate, the design power curve closer to the actual operating curve, and ensuring the design quality of the unit.

[0097] The self-consumption power assessment method and apparatus according to embodiments of the present disclosure take into account the influence of ambient temperature on self-consumption power assessment.

[0098] The self-consumption power assessment method and self-consumption power assessment device according to embodiments of the present disclosure perform wind sifting on grid-connected power and establish a model between self-consumption power load, grid-connected power and ambient temperature.

[0099] While some exemplary embodiments of this disclosure have been described, those skilled in the art will understand that modifications may be made to these embodiments without departing from the principles and spirit of this disclosure, which are defined by the claims and their equivalents. For example, technical features of different embodiments may be combined.

Claims

1. A method for assessing the self-consumption of a wind turbine generator set, characterized in that, include: Obtain the rated active power of each electrical subsystem consuming power in the wind turbine generator set and the number of each electrical subsystem; The operating load of each electrical subsystem is determined based on its rated active power and corresponding demand factor. Based on the operating load of each electrical subsystem and the corresponding number of electrical subsystems, the self-consumption power of the wind turbine generator set is determined. The steps for determining the operating load of each electrical subsystem based on its rated active power and corresponding demand factor include: The grid-connected power of wind turbine generators is divided into multiple sections. Based on the rated active power of each electrical subsystem and the demand factor of each electrical subsystem in each warehouse section, the operating load of each electrical subsystem in each warehouse section is determined. The demand factor for each electrical subsystem in each warehouse section is calculated as the product of the grid-connected power correlation coefficient and the correction coefficient for each electrical subsystem in each warehouse section with the grid-connected power. Specifically, the grid-connected power correlation coefficient for each warehouse section is calculated by using the ratio of the operating time of each electrical subsystem in each warehouse section to the total operating time of each warehouse section. The correction factor for each compartment is calculated by comparing the actual operating power of each electrical subsystem in each compartment with the rated active power of each electrical subsystem.

2. The method for assessing the self-consumption of wind turbine generators according to claim 1, characterized in that, The steps for determining the self-consumption power of the wind turbine generator set based on the operating load of each electrical subsystem and the corresponding number of electrical subsystems include: Based on the product of the operating load of each electrical subsystem in each section and the corresponding number of electrical subsystems, the self-consumption power of each electrical subsystem in each section is determined, and the sum of the self-consumption power of each electrical subsystem in each section is taken as the self-consumption power of the wind turbine generator set.

3. The method for assessing the self-consumption of wind turbine generators according to claim 1, characterized in that, The total time for each warehouse segment is the product of the total number of data points for each corresponding warehouse segment and the time for a single data point. The time for a single data point is calculated by multiplying the self-consumption power calculation cycle of each electrical subsystem by a predetermined value.

4. The method for assessing the self-consumption of wind turbine generator sets according to claim 1, characterized in that, The self-consumption power assessment method also includes: Based on the ambient temperature of the wind turbine generator set and the predetermined transfer function between the grid-connected power correlation coefficient of each electrical subsystem in each section and the ambient temperature, the grid-connected power correlation coefficient of each electrical subsystem in each section at the ambient temperature is determined.

5. The method for assessing the self-consumption of wind turbine generator sets according to claim 4, characterized in that, The transfer function is determined in the following manner: Statistically analyze the highest and most recent ambient temperatures within a predetermined historical time period; Based on the highest ambient temperature and the most recent ambient temperature, multiple ambient temperature ranges are obtained by dividing the temperature range. Statistically analyze the grid-connected power correlation coefficient and corresponding ambient temperature in each warehouse section; Determine the transfer function between the grid-connected power correlation coefficient and the ambient temperature for each section under each ambient temperature range.

6. A self-consumption power assessment device for wind turbine generator sets, characterized in that, include: The parameter acquisition unit acquires the rated active power of each electrical subsystem consuming power in the wind turbine generator set and the number of each electrical subsystem. The calculation unit determines the operating load of each electrical subsystem based on its rated active power and corresponding demand factor, and then determines the self-consumption power of the wind turbine generator based on the operating load of each electrical subsystem and the number of corresponding electrical subsystems. The computing unit is configured as follows: The grid-connected power of wind turbine generators is divided into multiple sections. Based on the rated active power of each electrical subsystem and the demand factor of each electrical subsystem in each warehouse section, the operating load of each electrical subsystem in each warehouse section is determined. The calculation unit is further configured to: calculate the demand coefficient of each electrical subsystem in each section based on the grid-connected power correlation coefficient and correction coefficient of each electrical subsystem in each section of the grid-connected power; Specifically, the grid-connected power correlation coefficient for each warehouse section is calculated by using the ratio of the operating time of each electrical subsystem in each warehouse section to the total operating time of each warehouse section. The correction factor for each compartment is calculated by comparing the actual operating power of each electrical subsystem in each compartment with the rated active power of each electrical subsystem.

7. The self-consumption power assessment device for wind turbine generator sets according to claim 6, characterized in that, The computing unit is further configured as follows: Based on the operating load of each electrical subsystem in each section and the corresponding number of electrical subsystems, the self-consumption power of each electrical subsystem in each section is determined, and the sum of the self-consumption power of each electrical subsystem in each section is taken as the self-consumption power of the wind turbine generator set.

8. A self-consumption power assessment device for wind turbine generator sets, characterized in that, It includes a memory and a processor, the memory storing a program or instructions that, when executed by the processor, implement the self-consumption power assessment method according to any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions or programs that, when executed by a processor, implement the self-consumption power assessment method for wind turbine generators according to any one of claims 1 to 5.

10. A wind turbine generator set, characterized in that, It includes the self-consuming power assessment device according to any one of claims 6 to 8 or the computer-readable storage medium according to claim 9.

Images