Wind Turbine Main Control System and Method Based on PLC

Abstract

The present invention discloses a main control system and method for a wind turbine based on a PLC, relating to the technical field of wind turbines. The main control system for a wind turbine based on a PLC includes: a power generation capacity evaluation module for the wind turbine, a power generation task allocation module for the wind turbine, an operation monitoring and adjustment module for the wind turbine, and an operation monitoring module for the wind turbine. By monitoring the startup process of each wind turbine through the PLC, the present invention can comprehensively evaluate the power generation status of the wind turbine, realize the reasonable allocation of power generation tasks, improve the power generation efficiency, ensure the continuous and stable operation of the main control system of the wind turbine. At the same time, it can diagnose faults and control the operation of the unit according to the power generation capacity correction parameters, ensure the stable operation of the wind farm, solve the problems of incomplete monitoring of the operation process of the wind turbine, simple data processing, and inaccurate adjustment of power generation tasks, facilitate maintenance personnel to quickly locate and repair faults, and reduce the time and cost of fault troubleshooting.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbines, and specifically to a main control system and method for a wind turbine unit based on a PLC. Background Art

[0002] With the increasing demand for clean energy, wind power generation, as a renewable energy technology, has developed rapidly. The wind resources in coastal areas, open plains, mountains and other regions are relatively rich and suitable for the construction of large-scale wind farms. Along with the construction of large-scale wind farms in regions, the monitoring demand level for wind turbine units is also increasing day by day.

[0003] The prior art such as the invention patent with the publication number: CN111594384B is a control method and main control system for a voltage source type wind turbine unit, including determining the operating mode of the wind turbine unit based on whether the power grid is in a large frequency disturbance condition; when the wind turbine unit operates in a current source operating mode, determining the generator torque output command sent to the converter and the operating mode of the wind turbine unit based on the generator torque command output by the variable speed variable pitch control loop and the generator torque command required for the power grid frequency active support control loop to output the support frequency; when the wind turbine unit operates in a voltage source operating mode, determining the generator torque output command sent to the converter and the operating mode of the wind turbine unit based on the generator torque command output by the variable speed variable pitch control loop and the generator torque required for virtual synchronization.

[0004] The prior art such as the invention patent with the publication number: CN114233572B is a control system for the integrated linkage of the central control of a wind farm and the main control of a wind turbine unit, including: a tower base controller, a nacelle controller, a yaw frequency converter, a pitch frequency converter, a power converter, an anemometer and wind vane, a control panel, an internal optical fiber communication bus and a data acquisition server; the tower base controller and the nacelle controller realize the integrated control of the tower base controller and the nacelle controller through the internal optical fiber communication bus, and at the same time, the nacelle controller serves as a coupler to realize the data interaction of multiple interfaces and protocols of the yaw frequency converter, the pitch frequency converter, the power converter and the anemometer and wind vane.

[0005] In view of the above solutions, it can be seen that the current main control system of wind turbine units has certain limitations in the design of the control method, only paying attention to some operating modes and control links, that is, adjusting and processing accordingly with the data at the actual operation level in a single dimension. However, the output of wind turbine units in actual operation will be restricted by external factors. If only relying on the mode adjustment under actual operation, there may be insufficient timeliness of adjustment, which may lead to too large an actual adjustment amplitude and cause operation losses of the wind turbine units, and it is difficult to achieve precise adjustment of the power generation task. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides a main control system and method for a wind turbine based on a PLC, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: In the first aspect of the present invention, a main control system for a wind turbine based on a PLC is provided, which is characterized in that it includes: a power generation capacity evaluation module for a wind turbine, which is used to monitor the startup process of each wind turbine through the PLC, collect the startup data of each wind turbine, analyze the startup correction coefficient of each wind turbine, synchronously obtain the historical operation parameters of each wind turbine and the wind farm environment data, and analyze the power generation status evaluation index of each wind turbine.

[0008] A power generation task allocation module for a wind turbine, which is used to determine the power generation mode of each wind turbine according to the startup correction coefficient of each wind turbine and the power generation status evaluation index of each wind turbine.

[0009] An operation monitoring and adjustment module for a wind turbine, which is used to obtain the operation status data of each wind turbine within a preset monitoring period, analyze the power generation status verification index of each wind turbine, and perform self-adaptive adjustment on the power generation tasks of each wind turbine based on the power generation status verification index of each wind turbine.

[0010] An operation monitoring module for a wind turbine, which is used to upload the real-time power generation status information of each wind turbine to the PLC remote monitoring center and perform real-time monitoring on the power generation process of the wind turbine.

[0011] In the second aspect of the present invention, a main control method for a wind turbine based on a PLC is provided, which is characterized in that it includes the following steps: S1, monitor the startup process of each wind turbine through the PLC, collect the startup data of each wind turbine, analyze the startup correction coefficient of each wind turbine, synchronously obtain the historical operation parameters of each wind turbine and the wind farm environment data, and analyze the power generation status evaluation index of each wind turbine.

[0012] S2, determine the power generation mode of each wind turbine according to the startup correction coefficient of each wind turbine and the power generation status evaluation index of each wind turbine.

[0013] S3, within a preset monitoring period, obtain the operation status data of each wind turbine, analyze the power generation status verification index of each wind turbine, and perform self-adaptive adjustment on the power generation tasks of each wind turbine based on the power generation status verification index of each wind turbine.

[0014] S4, upload the real-time power generation status information of each wind turbine to the PLC remote monitoring center and perform real-time monitoring on the power generation process of the wind turbine.

[0015] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects:

[0016] (1) The present invention provides a main control system and method for wind turbines based on PLC. By using PLC to monitor the startup process of each wind turbine, it can comprehensively evaluate the power generation status of the wind turbines, achieve a reasonable distribution of power generation tasks at the initial startup stage, improve power generation efficiency. The adjustment in the initial stage ensures the timeliness of adjustment, avoids the operation loss of the wind turbines caused by an overly large actual adjustment range. At the same time, it uploads the power generation status information to the PLC remote monitoring center for real-time monitoring to ensure the continuous and stable operation of the main control system of the wind turbines. It also includes a fault diagnosis module that can diagnose faults based on the power generation capacity correction parameters and control the operation of the units to ensure the stable operation of the wind farm, solves the problems of incomplete monitoring of the operation process of wind turbines, simple data processing, and inaccurate adjustment of power generation tasks, facilitates maintenance personnel to quickly locate and repair faults, and reduces the time and cost of fault troubleshooting.

[0017] (2) By analyzing the power generation status evaluation indicators of each wind turbine, the present invention can clarify the power generation performance and reliability degree of the wind turbines in the current environment, provide a basis for reasonably determining the power generation mode, achieve an accurate distinction between high-efficiency and low-efficiency power generation modes, improve the power generation efficiency of the entire wind farm. At the same time, by comprehensively considering the environmental data and the data of the units themselves, it can better adapt to the power generation requirements under different environmental conditions, ensure the stable operation of the wind turbines, and improve the operation efficiency and operation management level of the wind farm.

[0018] (3) By making self-adaptive adjustments to the power generation tasks of each wind turbine based on the power generation status verification index of each wind turbine, the present invention can make timely and reasonable adjustments to the power generation tasks according to the real-time operation status data of the wind turbines, which helps the wind turbines to always maintain the best power generation state, improve power generation efficiency. At the same time, it can reduce manual intervention, improve the automation degree and management efficiency of the operation of the wind farm, and ensure the stable power generation of the wind farm.

[0019] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a schematic diagram of the system module connection of the present invention;

[0021] Figure 2 is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] Please refer to Figure 1 As shown, the embodiment of the present invention provides a main control system for a wind turbine based on a PLC, which is characterized in that it includes: a power generation capacity evaluation module for a wind turbine, which is used to monitor the startup process of each wind turbine through the PLC, collect the startup data of each wind turbine, analyze the startup correction coefficient of each wind turbine, synchronously obtain the historical operation parameters of each wind turbine and the environmental data of the wind farm, and analyze the power generation status evaluation index of each wind turbine.

[0024] It should be noted that PLC is the abbreviation of Programmable Logic Controller, which refers to a programmable logic controller.

[0025] In this embodiment, the process of analyzing the startup correction coefficient of each wind turbine is as follows: The startup data of each wind turbine includes the startup feedback duration, the generator startup acceleration duration, the blade startup acceleration duration, the generator output voltage, the generator output current, and the main shaft torque of each wind turbine.

[0026] It should be noted that the startup data of each wind turbine is collected through a set of sensing devices installed on the wind turbine, and the set of sensing devices includes a speed sensor, a voltage sensor, a current sensor, etc.

[0027] The startup feedback duration of a wind turbine refers to the time elapsed from when the wind turbine receives a startup command to when the PLC control system of the unit feedbacks that the unit has reached the rated startup state.

[0028] The generator startup acceleration duration and the blade startup acceleration duration refer to the time elapsed from when the generator and blade speeds reach zero to the rated speed.

[0029] The main shaft torque refers to the torsional moment borne by the main shaft (the shaft connecting the wind wheel and the generator) in the wind turbine.

[0030] According to the startup data of each wind turbine, startup indicators for each wind turbine are obtained through analysis and processing.

[0031] The startup indicators of each wind turbine are numerical results obtained by quantifying the startup data of each wind turbine and are used to characterize the stability of the startup state of the wind turbine.

[0032] In a specific embodiment, the specific acquisition method of the startup indicators of each wind turbine is as follows:

[0033] Extract the ideal startup data of the wind turbine stored in the database. The ideal startup data of the wind turbine includes the reference startup feedback duration, the generator reference startup acceleration duration, the blade reference startup acceleration duration, the ideal generator output voltage, the ideal generator output current, and the ideal main shaft torque of the wind turbine.

[0034] It should be noted that the ideal start-up data of the wind turbines stored in the database is obtained by averaging the historical data of the start-up data of the wind turbines before the database is established.

[0035]

[0036] Among them, C i is the start-up index of the i-th wind turbine, a i is the start-up feedback duration of the i-th wind turbine, a 0 is the reference start-up feedback duration, b i is the generator start-up acceleration duration of the i-th wind turbine, b 0 is the reference generator start-up acceleration duration, c i is the blade start-up acceleration duration of the i-th wind turbine, c 0 is the reference blade start-up acceleration duration, d i is the generator output voltage of the i-th wind turbine, d 0 is the ideal generator output voltage, f i is the generator output current of the i-th wind turbine, f 0 is the ideal generator output current, g i is the main shaft torque of the i-th wind turbine, g 0 is the ideal main shaft torque, x 1 is the weight of the start-up feedback duration, x 2 is the weight of the generator start-up acceleration duration, x 3 is the weight of the blade start-up acceleration duration, x 4 is the weight of the generator output voltage, x 5 is the weight of the generator output current, x 6 is the weight of the main shaft torque. i is the number of each wind turbine, i = 1, 2,..., m, where m is the number of wind turbines, and e is the natural constant.

[0037] It should be noted that the value ranges of the startup feedback duration weight, the generator startup acceleration duration weight, the blade startup acceleration duration weight, the generator output voltage weight, the generator output current weight, and the main shaft torque weight are all between 0 and 1. In this embodiment, the startup feedback duration weight is the preset startup feedback duration weight in the database, which represents the numerical value of the influence degree of the startup feedback duration on the startup index of the wind turbine generator set. The generator startup acceleration duration weight is the preset generator startup acceleration duration weight in the database, which represents the numerical value of the influence degree of the generator startup acceleration duration on the startup index of the wind turbine generator set. The blade startup acceleration duration weight is the preset blade startup acceleration duration weight in the database, which represents the numerical value of the influence degree of the blade startup acceleration duration on the startup index of the wind turbine generator set. The generator output voltage weight is the preset generator output voltage weight in the database, which represents the numerical value of the influence degree of the generator output voltage on the startup index of the wind turbine generator set. The generator output current weight is the preset generator output current weight in the database, which represents the numerical value of the influence degree of the generator output current on the startup index of the wind turbine generator set. The main shaft torque weight is the preset main shaft torque weight in the database, which represents the numerical value of the influence degree of the main shaft torque on the startup index of the wind turbine generator set. When in use, the preset startup feedback duration weight, the generator startup acceleration duration weight, the blade startup acceleration duration weight, the generator output voltage weight, the generator output current weight, and the main shaft torque weight can be directly obtained from the database. Their corresponding relationships can be pre-set mapping relationships. For example, the startup feedback duration, the generator startup acceleration duration, the blade startup acceleration duration, the generator output voltage, the generator output current, and the main shaft torque respectively form a mapping set with the preset startup feedback duration weight, the generator startup acceleration duration weight, the blade startup acceleration duration weight, the generator output voltage weight, the generator output current weight, and the main shaft torque weight in the database. The real-time startup feedback duration, the generator startup acceleration duration, the blade startup acceleration duration, the generator output voltage, the generator output current, and the main shaft torque are respectively input into the mapping set to obtain the startup feedback duration weight, the generator startup acceleration duration weight, the blade startup acceleration duration weight, the generator output voltage weight, the generator output current weight, and the main shaft torque weight, and the mapping relationship therein is one-to-one correspondence.

[0038] It should also be noted that in this embodiment, the start-up index of the wind turbine is obtained by processing the start-up feedback duration, the generator start-up acceleration duration, the blade start-up acceleration duration, the generator output voltage, the generator output current, and the main shaft torque of the wind turbine. This is considering the mutual influence between these parameters. For example, if the generator start-up acceleration duration and the blade start-up acceleration duration are short, it means that the mechanical and electrical parts of the wind turbine can quickly reach a stable operating state, which usually results in a shorter start-up feedback duration. If the generator can quickly establish a stable output voltage and current and meet the conditions for the control system to determine successful start-up, then the start-up feedback duration will be shortened. On the contrary, if the generator output voltage and current rise slowly or fluctuate greatly and cannot quickly meet the requirements, the start-up feedback duration will be extended. During the start-up process, sufficient main shaft torque is a prerequisite for ensuring the smooth acceleration and start-up of the generator and the blades. During the start-up process of the wind turbine, the blades capture wind energy and convert it into mechanical energy, which is transmitted to the generator through the main shaft to drive the generator to rotate. If the blade start-up acceleration duration is short and it can quickly reach a certain rotational speed to provide sufficient power for the generator, then the generator start-up acceleration duration will also be correspondingly shortened. According to Ohm's law, when the internal resistance and external load of the generator are certain, the generator output voltage and output current are interrelated. When the generator output voltage increases, if the external load remains unchanged, the output current will also increase accordingly. On the contrary, when the output voltage decreases, the output current will also decrease.

[0039] In a specific embodiment, by analyzing the start-up indexes of each wind turbine, the mechanical and electrical performance during the start-up process of the wind turbine can be understood, which helps to timely discover potential problems during the start-up process, provides a basis for optimizing the start-up control strategy, improves the reliability and efficiency of the wind turbine start-up, reduces the risk of start-up failure, and ensures that the wind turbine can quickly and stably enter the normal power generation state.

[0040] Extract the wind turbine correction coefficients corresponding to each start-up index interval stored in the database, and map and extract the wind turbine correction coefficients corresponding to the start-up indexes of each wind turbine, which are marked as the start-up correction coefficients of each wind turbine.

[0041] In a specific embodiment, the start-up correction coefficients of each wind turbine can more accurately reflect the actual situation of the start-up state of the wind turbine, comprehensively consider and correct various complex factors during the start-up process. When determining the power generation mode, it can more reasonably evaluate the start-up ability of the wind turbine, provide a reliable basis for subsequent targeted operations according to different power generation modes, and thus improve the overall power generation efficiency and operation stability of the wind farm.

[0042] In this embodiment, historical operation parameters of each wind turbine and wind farm environment data are obtained, where: the historical operation parameters of each wind turbine include the historical average fault-free duration, historical average daily power generation, total historical maintenance times, and historical average start feedback duration of each wind turbine.

[0043] It should be noted that the historical average fault-free duration refers to the average value of the normal operation durations between adjacent two faults among multiple faults during the past operation of the wind turbine.

[0044] The historical operation parameters of each wind turbine are extracted from the maintenance management system of the wind farm.

[0045] The wind farm environment data includes the wind speed, wind direction, temperature, and humidity of the current wind farm.

[0046] It should be noted that the wind farm environment data is collected through the built-in sensing devices of the wind turbines, and the sensing devices include an anemometer and wind vane, a temperature sensor, and a humidity sensor.

[0047] In this embodiment, the power generation status evaluation indexes of each wind turbine are analyzed, and the specific process is as follows: Extract the reference historical operation parameters and reference environment data of the wind turbines stored in the database.

[0048] The reference historical operation parameters of the wind turbines include the reference average fault-free duration, reference average daily power generation, total reference maintenance times, and reference average start feedback duration of the wind turbines.

[0049] The reference environment data includes the reference wind speed of the wind farm, the yaw system wind turbine angle, the reference temperature, and the reference humidity.

[0050] It should be noted that the reference historical operation parameters and reference environment data of the wind turbines stored in the database are obtained by performing mean processing on the historical data of the historical operation parameters of the wind turbines and the wind farm environment data before the database is established.

[0051] Based on the historical operation parameters of each wind turbine, the wind farm environment data, the reference historical operation parameters of the wind turbines, and the reference environment data, the power generation status evaluation indexes of each wind turbine are analyzed and processed. The power generation status evaluation indexes of each wind turbine are used to characterize the power generation performance and reliability degree of each wind turbine under the current environment.

[0052] In a specific embodiment, the process of obtaining the power generation status evaluation indexes of each wind turbine is as follows:

[0053]

[0054] Among them, D i is the power generation status evaluation index of the i-th wind turbine, hi is the historical average trouble-free operation duration of the i-th wind turbine, in hours 0 is the reference average trouble-free operation duration of the wind turbine, in hours i is the historical average daily power generation of the i-th wind turbine, in kWh 0 is the reference average daily power generation of the wind turbine, in kWh i is the total historical maintenance times of the i-th wind turbine, in times 0 is the reference total maintenance times of the wind turbine, in times i is the historical average start feedback duration of the i-th wind turbine, in minutes 0 is the reference average start feedback duration of the wind turbine, in minutes i is the current wind speed of the wind farm, in m / s 0 is the reference wind speed of the wind farm, in m / s i is the current wind direction of the wind farm, in degrees i0 is the yaw system wind turbine angle of the i-th wind turbine, in degrees i is the current temperature of the wind farm, in °C 0 is the reference temperature of the wind farm, in °C i is the current humidity of the wind farm, in % 0 is the reference humidity of the wind farm, in % 1 is the weight of the historical average trouble-free operation duration, dimensionless 2 is the weight of the historical average daily power generation, dimensionless 3 is the weight of the total historical maintenance times, dimensionless 4 is the weight of the historical average start feedback duration, dimensionless 5 is the weight of the wind speed, dimensionless 6 is the weight of the wind direction, dimensionless 7 is the weight of the temperature, dimensionless 8 is the weight of the humidity, where i is the number of each wind turbine, i = 1, 2,..., m, m is the number of wind turbines, and e is the natural constant

[0055] It should be understood that the softsign function is a built-in function in Python

[0056] It should be noted that the weight values of the historical average fault-free operation duration, historical average daily power generation, total historical maintenance times, historical average start-up feedback duration, wind speed, wind direction, temperature, and humidity all have a value range between 0 and 1. In this embodiment, the weight value of the historical average fault-free operation duration is the weight value of the historical average fault-free operation duration preset in the database, which represents the numerical value of the influence degree of the historical average fault-free operation duration on the evaluation index of the power generation state of the wind turbine. The weight value of the historical average daily power generation is the weight value of the historical average daily power generation preset in the database, which represents the numerical value of the influence degree of the historical average daily power generation on the evaluation index of the power generation state of the wind turbine. The weight value of the total historical maintenance times is the weight value of the total historical maintenance times preset in the database, which represents the numerical value of the influence degree of the total historical maintenance times on the evaluation index of the power generation state of the wind turbine. The weight value of the historical average start-up feedback duration is the weight value of the historical average start-up feedback duration preset in the database, which represents the numerical value of the influence degree of the historical average start-up feedback duration on the evaluation index of the power generation state of the wind turbine. The wind speed weight value is the wind speed weight value preset in the database, which represents the numerical value of the influence degree of the wind speed on the evaluation index of the power generation state of the wind turbine. The wind direction weight value is the wind direction weight value preset in the database, which represents the numerical value of the influence degree of the wind direction on the evaluation index of the power generation state of the wind turbine. The temperature weight value is the temperature weight value preset in the database, which represents the numerical value of the influence degree of the temperature on the evaluation index of the power generation state of the wind turbine. The humidity weight value is the humidity weight value preset in the database, which represents the numerical value of the influence degree of the humidity on the evaluation index of the power generation state of the wind turbine. When in use, the preset weight values of the historical average fault-free operation duration, historical average daily power generation, total historical maintenance times, historical average start-up feedback duration, wind speed, wind direction, temperature, and humidity can be directly obtained from the database. Their corresponding relationships can be pre-set mapping relationships. For example, the historical average fault-free operation duration, historical average daily power generation, total historical maintenance times, historical average start-up feedback duration, wind speed, wind direction, temperature, and humidity respectively form a mapping set with the weight values of the historical average fault-free operation duration, historical average daily power generation, total historical maintenance times, historical average start-up feedback duration, wind speed weight value, wind direction weight value, temperature, and humidity weight value preset in the database. The real-time historical average fault-free operation duration, historical average daily power generation, total historical maintenance times, historical average start-up feedback duration, wind speed, wind direction, temperature, and humidity are respectively input into the mapping set to obtain the weight values of the historical average fault-free operation duration, historical average daily power generation, total historical maintenance times, historical average start-up feedback duration, wind speed weight value, wind direction weight value, temperature weight value, and humidity weight value, and the mapping relationship therein is one-to-one correspondence.

[0057] It should also be noted that in this embodiment, the wind turbine power generation status evaluation index is obtained by processing the historical operation parameters of the wind turbine and the wind farm environment data, taking into account the mutual influence between these parameters. For example, a longer historical average fault-free duration usually means that the wind turbine has more time in a normal operation state, thus being able to generate more electricity. A large number of maintenance times may indicate that there are more problems or the unit is prone to failure, which will lead to a shortening of the fault-free duration. For a wind turbine with a longer historical average fault-free duration, the stability and reliability of its system are relatively high, which may make its start-up feedback duration relatively stable and short. Because during fault-free operation, each component of the unit, such as the control system, electrical system, and mechanical transmission system, is in a better state and can complete the start-up process more efficiently. Under good environmental conditions, such as appropriate wind speed, wind direction, temperature, and humidity, the wear and failure risks of the wind turbine are relatively low, which is conducive to extending the historical average fault-free duration. A shorter historical average start-up feedback duration helps to improve the power generation efficiency of the wind turbine. If the unit can start quickly, it can start generating electricity earlier, thus increasing the historical average daily power generation.

[0058] In a specific embodiment, by analyzing the power generation status evaluation indexes of each wind turbine, the power generation performance and reliability degree of the wind turbine in the current environment can be clarified, providing a basis for reasonably determining the power generation mode, realizing an accurate distinction between high-efficiency and low-efficiency power generation modes, improving the power generation efficiency of the entire wind farm, and at the same time, by comprehensively considering the environmental data and the data of the unit itself, better adapting to the power generation requirements under different environmental conditions, ensuring the stable operation of the wind turbine, and enhancing the operation benefit and operation management level of the wind farm.

[0059] The wind turbine power generation task allocation module is used to determine the power generation mode of each wind turbine according to the start-up correction coefficient of each wind turbine and the power generation status evaluation index of each wind turbine.

[0060] The power generation modes of the wind turbines include a low-efficiency power generation mode and a high-efficiency power generation mode.

[0061] In this embodiment, the process of determining the power generation mode of each wind turbine is as follows: According to the start-up correction coefficient of each wind turbine and the power generation status evaluation index of each wind turbine, the power generation capacity indication parameters of each wind turbine are obtained through comprehensive analysis and processing.

[0062] The power generation capacity indication parameters of each wind turbine are the numerical results obtained by quantifying the start-up correction coefficient of each wind turbine and the power generation status evaluation index of each wind turbine, and are used to characterize the start-up ability stability of each wind turbine.

[0063] In a specific embodiment, the specific acquisition method of the power generation capacity indication parameters of each wind turbine is as follows:

[0064] A i = sinh[(D i *θ i ) 2 ,

[0065] where A i is the power generation capacity indication parameter of the i-th wind turbine, D i is the power generation status evaluation index of the i-th wind turbine, θ i is the start-up correction coefficient of the i-th wind turbine, i is the number of each wind turbine, i = 1, 2,..., m, and m is the number of wind turbines.

[0066] In a specific embodiment, by analyzing the power generation capacity indication parameters of each wind turbine, the power generation mode of the wind turbine can be determined more accurately, the high-efficiency and low-efficiency power generation modes can be distinguished, so as to reasonably arrange the power generation tasks, enable the high-efficiency units to fully exert their power generation capacity, properly handle the low-efficiency units, improve the power generation efficiency of the entire wind farm, and at the same time provide an important decision-making basis for the operation management and optimization of the wind farm.

[0067] Extract the preset power generation capacity index threshold of the wind turbine from the database.

[0068] It should be understood that the power generation capacity index threshold of the wind turbine is obtained from the database and is used to reflect the critical value of the power generation capacity of the wind turbine.

[0069] In a specific embodiment, the power generation capacity index threshold of the wind turbine is preset in the database and can be obtained by various methods. For example, collect a large number of sample data of the power generation capacity parameters of wind turbines in different operating environments and different operating states, conduct detailed statistical analysis and processing on these sample data to obtain the power generation capacity evaluation values of the wind turbines, and then perform weighted average or calculation processing of specific percentile values on these evaluation values to obtain the power generation capacity index threshold of the wind turbine, or let experienced experts in the field of wind power generation set the threshold based on their long-term accumulated experience to judge the limit of the power generation capacity of the wind turbine.

[0070] If the power generation capacity indication parameter of a certain wind turbine is less than the power generation capacity index threshold of the wind turbine, the power generation mode of the wind turbine is marked as a low-efficiency power generation mode; if the power generation capacity indication parameter of a certain wind turbine is greater than or equal to the power generation capacity index threshold of the wind turbine, the power generation mode of the wind turbine is marked as a high-efficiency power generation mode.

[0071] It should be understood that if the power generation capacity indication parameter of a wind turbine is less than the power generation capacity index threshold of the wind turbine, it indicates that the wind turbine has relatively weak power generation capacity under the current operating conditions, and the overall power generation efficiency is at a low level. Therefore, its power generation mode is marked as a low-efficiency power generation mode, and its power generation task is reduced. If the power generation capacity indication parameter of a wind turbine is greater than or equal to the power generation capacity index threshold of the wind turbine, it indicates that the wind turbine has strong power generation capacity in the current operating environment and its own state, can convert wind energy into electrical energy more efficiently, performs well in aspects such as wind energy capture, generator operation, and adaptation to the environment, and the overall power generation efficiency reaches a high level. Therefore, its power generation mode is marked as a high-efficiency power generation mode, and it can be reasonably utilized and managed as a wind turbine with relatively prominent power generation efficiency in the wind farm.

[0072] It should also be noted that the generator in the low-efficiency power generation mode can reduce its power generation task through PLC remote control, such as adjusting the blade pitch angle and changing the output frequency of the frequency converter.

[0073] The wind turbine operation monitoring and adjustment module is used to obtain the operation state data of each wind turbine within a preset monitoring period, analyze the power generation state verification index of each wind turbine, and adaptively adjust the power generation tasks of each wind turbine based on the power generation state verification index of each wind turbine.

[0074] In this embodiment, analyzing the power generation state verification index of each wind turbine, the specific analysis process is as follows: The operation state data of each wind turbine includes the change amount of the generator speed, the change amount of the blade speed, the average output power of the generator, and the average voltage on the grid side of each wind turbine.

[0075] It should be noted that the change amount of the generator speed and the change amount of the blade speed refer to the difference between the maximum value and the minimum value of the generator speed and the blade speed of the wind turbine in the operating state within a preset monitoring period.

[0076] Based on the operation state data of each wind turbine, the power generation state verification index of each wind turbine is analyzed and processed.

[0077] The power generation state verification index of each wind turbine is a numerical result obtained by quantifying the operation state data of each wind turbine, and is used to characterize the power generation performance and the rationality of the operation state of each wind turbine during the working process.

[0078] In a specific embodiment, the specific acquisition method of the power generation state verification index of each wind turbine is as follows:

[0079] Extract the reference operation state data stored in the database. The reference operation state data includes the reference change amount of the generator speed, the reference change amount of the blade speed, the reference average output power of the generator, and the reference average voltage on the grid side.

[0080] It should be noted that the reference operating status data stored in the database is obtained by performing mean processing on the wind turbine operating status data before the database is established.

[0081]

[0082] Among them, B i is the power generation status verification index of the i-th wind turbine, α i is the change in the generator speed of the i-th wind turbine, α 0 is the reference change in generator speed, β i is the change in the blade speed of the i-th wind turbine, β 0 is the reference change in blade speed, γ i is the average output power of the generator of the i-th wind turbine, γ 0 is the reference average output power of the generator, δ i is the average voltage on the grid side of the i-th wind turbine, δ 0 is the reference average voltage on the grid side, z 1 is the weight of the change in generator speed, z 2 is the weight of the change in blade speed, z 3 is the weight of the average output power of the generator, z 4 is the weight of the average voltage on the grid side. i is the number of each wind turbine, i = 1, 2,..., m, where m is the number of wind turbines, and e is the natural constant.

[0083] It should be noted that the value ranges of the weight of the generator speed change amount, the weight of the blade speed change amount, the weight of the average output power of the generator, and the weight of the average grid-side voltage are all between 0 and 1. In this embodiment, the weight of the generator speed change amount is the preset weight of the generator speed change amount in the database, which represents the value of the influence degree of the generator speed change amount on the power generation state verification index of the wind turbine. The weight of the blade speed change amount is the preset weight of the blade speed change amount in the database, which represents the value of the influence degree of the blade speed change amount on the power generation state verification index of the wind turbine. The weight of the average output power of the generator is the preset weight of the average output power of the generator in the database, which represents the value of the influence degree of the average output power of the generator on the power generation state verification index of the wind turbine. The weight of the average grid-side voltage is the preset weight of the average grid-side voltage in the database, which represents the value of the influence degree of the average grid-side voltage on the power generation state verification index of the wind turbine. When in use, the preset weights of the generator speed change amount, the blade speed change amount, the average output power of the generator, and the average grid-side voltage can be directly obtained from the database, and their corresponding relationships can be pre-set mapping relationships. For example, the generator speed change amount, the blade speed change amount, the average output power of the generator, and the average grid-side voltage respectively form a mapping set with the preset weights of the generator speed change amount, the blade speed change amount, the average output power of the generator, and the average grid-side voltage in the database. The real-time generator speed change amount, blade speed change amount, average output power of the generator, and average grid-side voltage are respectively input into the mapping set to obtain the weights of the generator speed change amount, the blade speed change amount, the average output power of the generator, and the average grid-side voltage, and the mapping relationship therein is one-to-one.

[0084] It should also be noted that in this embodiment, the power generation status verification index of the wind turbine is obtained by processing the generator speed change amount, blade speed change amount, average output power of the generator, and average grid-side voltage of the wind turbine. This is because of the mutual influence among these parameters. For example, in a wind turbine, the blades are connected to the generator through components such as the hub and main shaft, forming a mechanical transmission system. When the blade speed changes, it will directly affect the generator speed through this mechanical transmission system. At the same time, the change in blade speed means a change in the amount of wind energy captured. When the blade speed increases, the captured wind energy increases, and the mechanical energy transmitted to the generator also increases, thus increasing the generator speed. According to the law of electromagnetic induction, the output voltage of the generator is related to the generator speed and magnetic field strength. When the generator speed changes, the speed at which the internal winding cuts the magnetic force lines changes, thereby affecting the output voltage. Under a certain load, according to the power formula P = UI, the voltage change will cause a change in the output power. The grid-side voltage will also limit the power output of the generator. If the grid-side voltage is too low, the generator may not be able to output at the rated power because the low voltage will limit the output current of the generator, and according to the power formula, the power output will also be affected.

[0085] In a specific embodiment, by analyzing the power generation status verification index of each wind turbine, abnormal situations during the power generation process can be detected in a timely manner, providing a basis for adaptively adjusting the power generation task, ensuring that the wind turbine always maintains a good power generation state, improving the power generation efficiency, and ensuring the stable operation of the wind farm.

[0086] In this embodiment, the power generation tasks of each wind turbine are adaptively adjusted based on the power generation status verification index of each wind turbine. The specific adjustment method is as follows: Extract the first power generation status verification index and the second power generation status verification index of the wind turbine preset in the database.

[0087] It should be understood that the first power generation status verification index and the second power generation status verification index of the wind turbine are extracted from the database and are used to reflect the critical values of the power generation status of the wind turbine.

[0088] In a specific embodiment, the first verification index of the power generation state of the wind turbine and the second verification index of the power generation state of the wind turbine are preset in the database and can be obtained through various methods. For example, a large amount of detailed operation state data of wind turbines at different operation stages can be collected. For these rich sample data collected, in-depth statistical analysis and processing are performed on the data, such as calculating statistical indicators such as the mean, median, and standard deviation of various types of data, and determining the first verification index of the power generation state of the wind turbine and the second verification index of the power generation state of the wind turbine through a specific algorithm. The method of cluster analysis can be adopted to cluster the data according to the quality of the power generation state, and the verification index corresponding to the data near different cluster boundaries is selected as the initial setting value.

[0089] It should also be noted that the first verification index of the power generation state of the wind turbine refers to the minimum value of the verification index of the power generation state of the wind turbine stored in the database, and the second verification index of the power generation state of the wind turbine refers to the maximum value of the verification index of the power generation state of the wind turbine stored in the database.

[0090] Extract the preset adjustment value of the verification index of the power generation state of the wind turbine from the database.

[0091] Analyze the power generation capacity correction parameters of each wind turbine according to the first verification index of the power generation state of the wind turbine, the second verification index of the power generation state of the wind turbine, and the adjustment value of the verification index of the power generation state of the wind turbine.

[0092] Compare the power generation capacity correction parameters of each wind turbine with the threshold of the power generation capacity index of the wind turbine, analyze and obtain the adaptive adjustment power generation mode of each wind turbine, and perform self-adaptive adjustment on each wind turbine according to the corresponding adaptive adjustment power generation mode.

[0093] In a specific embodiment, the specific acquisition method of the power generation capacity correction parameters of each wind turbine is as follows:

[0094]

[0095] Among them, A i ′ is the power generation capacity correction parameter of the i-th wind turbine, A i is the power generation capacity indication parameter of the i-th wind turbine, B i is the verification index of the power generation state of the i-th wind turbine, B 1 is the first verification index of the power generation state of the wind turbine, B 2 is the second verification index of the power generation state of the wind turbine, is the adjustment value of the verification index of the power generation state of the wind turbine, i is the number of each wind turbine, i = 1, 2,..., m, and m is the number of wind turbines.

[0096] It should be noted that if the power generation status verification index of a certain wind turbine is less than the first verification index of the wind turbine power generation status, it indicates that the power generation status of this wind turbine is at a relatively poor level, and there may be problems such as the power generation performance being significantly lower than expected and the operating status being unstable. It is necessary to adjust its power generation task. It may be necessary to reduce the power generation task to avoid greater impact on the overall power generation efficiency of the wind farm. If the power generation status verification index of a certain wind turbine is greater than the second verification index of the wind turbine power generation status, it indicates that the power generation status of this wind turbine is at a relatively good level, and it may perform excellently in both power generation performance and operating status. It is possible to appropriately consider increasing its power generation task to make full use of its power generation capacity and improve the overall power generation efficiency of the wind farm. If the power generation status verification index of a certain wind turbine is greater than or equal to the first verification index of the wind turbine power generation status and less than or equal to the second verification index of the wind turbine power generation status, it indicates that the power generation status of this wind turbine is at a normal level, and its power generation performance and operating status meet the expected range. There is no need to adjust its power generation task for the time being, and the current operating status and power generation task arrangement can be continued to maintain the stable operation and power generation efficiency of the wind farm.

[0097] In a specific embodiment, by adaptively adjusting the power generation tasks of each wind turbine based on the power generation status verification index of each wind turbine, it is possible to timely and reasonably adjust the power generation tasks according to the real-time operating status data of the wind turbines, which helps the wind turbines to always maintain the best power generation status, improve the power generation efficiency. At the same time, it can reduce manual intervention, improve the automation degree and management efficiency of the wind farm operation, and ensure the stable power generation of the wind farm.

[0098] The wind turbine operation monitoring module is used to upload the real-time power generation status information of each wind turbine to the PLC remote monitoring center to monitor the power generation process of the wind turbines in real time.

[0099] In a specific embodiment, the main control system of the wind turbine based on PLC further includes: a wind turbine fault diagnosis module, which is used to analyze the wind turbine fault information according to the power generation capacity correction parameters of each wind turbine. The specific process is as follows: extract the preset power generation capacity correction parameter threshold in the database.

[0100] It should be understood that the power generation capacity correction parameter threshold of the wind turbine is obtained by extracting from the database and is used to reflect the critical value of the power generation capacity of the wind turbine after correction.

[0101] In a specific embodiment, the threshold of the power generation capacity correction parameter of the wind turbine is preset in the database and can be obtained through various methods. For example, sample data of the power generation capacity correction parameters of many wind turbines under different working conditions (such as different environmental conditions like wind speed, temperature, humidity, etc., and different operation durations, maintenance conditions, etc.) can be collected. In-depth statistical analysis is carried out on these sample data. For instance, the distribution law of the data is analyzed, and statistical quantities such as the mean and standard deviation are calculated. According to the distribution characteristics of the data, appropriate methods can be used to determine the threshold. For example, the threshold range can be set as the mean plus or minus a certain multiple of the standard deviation, or the sample data can be divided into different intervals, and the actual operation performance of the wind turbines in each interval is observed (such as whether faults occur, the change in power generation efficiency, etc.), and the appropriate threshold is determined based on these performances, providing a key basis for the stable operation and fault judgment of the wind turbines.

[0102] If the power generation capacity correction parameter of a certain wind turbine is less than or equal to the threshold of the power generation capacity correction parameter of the wind turbine, the fault information of the wind turbine is marked as a faulty wind turbine, the fault information of the wind turbine is synchronously uploaded to the remote monitoring center, and the operation of the wind turbine is stopped through PLC control.

[0103] If the power generation capacity correction parameter of a certain wind turbine is greater than the threshold of the power generation capacity correction parameter of the wind turbine, no operation control is performed on the wind turbine.

[0104] It should be understood that if the power generation capacity correction parameter of a certain wind turbine is less than or equal to the threshold of the power generation capacity correction parameter of the wind turbine, it indicates that the power generation capacity of the wind turbine is in a poor state after correction, and there may be problems such as difficult startup of the wind turbine, low power generation efficiency, poor operation stability, or insufficient environmental adaptability. To ensure the stable power generation of the wind farm and avoid the expansion of the impact of faults, it is necessary to stop the operation of the wind turbine through PLC control and upload the fault information to the remote monitoring center. If the power generation capacity correction parameter of a certain wind turbine is greater than the threshold of the power generation capacity correction parameter of the wind turbine, it indicates that the power generation capacity of the wind turbine is at a normal level after correction and can meet the requirements of stable power generation under different working conditions. To ensure the continuous and efficient operation of the wind turbine, there is no need to make additional adjustments to its operation state temporarily.

[0105] Please refer to Figure 2 As shown, the embodiment of the present invention provides a main control method for a wind turbine based on PLC, which is characterized in that it includes the following steps: S1, monitor the startup process of each wind turbine through PLC, collect the startup data of each wind turbine, analyze the startup correction coefficient of each wind turbine, synchronously obtain the historical operation parameters of each wind turbine and the environmental data of the wind farm, and analyze the power generation state evaluation index of each wind turbine.

[0106] S2. Determine the power generation modes of each wind turbine according to the start-up correction factor of each wind turbine and the power generation status evaluation index of each wind turbine.

[0107] S3. During a preset monitoring period, obtain the operation status data of each wind turbine, analyze the power generation status verification index of each wind turbine, and perform self-adaptive adjustment on the power generation tasks of each wind turbine based on the power generation status verification index of each wind turbine.

[0108] S4. Upload the real-time power generation status information of each wind turbine to the PLC remote monitoring center to monitor the power generation process of the wind turbine in real time.

[0109] In a specific embodiment, by providing a main control system and method for a wind turbine based on a PLC, the start-up process of each wind turbine can be monitored through the PLC, the power generation status of the wind turbine can be comprehensively evaluated, a reasonable distribution of the power generation task can be achieved at the initial start-up stage, the power generation efficiency can be improved, the adjustment in the initial stage ensures the timeliness of the adjustment, and the operation loss of the wind turbine caused by an overly large actual adjustment amplitude can be avoided. At the same time, the power generation status information is uploaded to the PLC remote monitoring center for real-time monitoring to ensure the continuous and stable operation of the main control system of the wind turbine. It also includes a fault diagnosis module that can diagnose faults according to the power generation capacity correction parameters and control the operation of the unit to ensure the stable operation of the wind farm, solving the problems of incomplete monitoring of the operation process of the wind turbine, simple data processing, and inaccurate adjustment of the power generation task, facilitating maintenance personnel to quickly locate and repair faults, and reducing the time and cost of fault troubleshooting.

[0110] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device.

[0111] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art in the relevant technical field can understand and utilize the present invention well. As long as it does not deviate from the structure of the present invention or exceed the scope defined by the present invention, it should fall within the protection scope of the present invention.

Claims

1. The PLC-based wind turbine main control system is characterized by: include: The wind turbine generation capacity evaluation module is used to monitor the startup process of each wind turbine through PLC, collect the startup data of each wind turbine, analyze the startup correction coefficient of each wind turbine, synchronously obtain the historical operating parameters of each wind turbine and the wind farm environment data, and analyze the power generation status evaluation indicators of each wind turbine; The wind turbine generator set power generation task allocation module is used to determine the power generation mode of each wind turbine generator set according to the start-up correction coefficient of each wind turbine generator set and the power generation status evaluation index of each wind turbine generator set; The wind turbine operation monitoring and adjustment module is used to obtain the operation status data of each wind turbine within a preset monitoring period, analyze the power generation status verification index of each wind turbine, and adaptively adjust the power generation task of each wind turbine based on the power generation status verification index of each wind turbine; The wind turbine operation monitoring module is used to upload the real-time power generation status information of each wind turbine to the PLC remote monitoring center and monitor the power generation process of the wind turbine in real time.

2. The PLC-based wind turbine master control system according to claim 1 is characterized in that: The specific process of analyzing the startup correction coefficient of each wind turbine generator set is as follows: The start-up data of each wind turbine generator set includes the start-up feedback time of each wind turbine generator set, the generator start-up acceleration time, the blade start-up acceleration time, the generator output voltage, the generator output current and the main shaft torque; According to the startup data of each wind turbine generator set, the startup index of each wind turbine generator set is obtained by analysis and processing; Extract the wind turbine correction coefficient corresponding to each startup index interval stored in the database, and map and extract the wind turbine correction coefficient corresponding to each wind turbine startup index, and mark it as the startup correction coefficient of each wind turbine; The startup index of each wind turbine generator set is a numerical result of quantifying the startup data of each wind turbine generator set and is used to characterize the stability of the startup state of the wind turbine generator set.

3. The PLC-based wind turbine master control system according to claim 1 is characterized in that: The historical operating parameters of each wind turbine and wind farm environmental data are obtained, wherein: The historical operating parameters of each wind turbine include the historical average trouble-free time, historical average daily power generation, historical total maintenance times and historical average startup feedback time of each wind turbine; Wind farm environmental data includes the current wind speed, wind direction, temperature and humidity of the wind farm.

4. The PLC-based wind turbine master control system according to claim 1 is characterized in that: The specific process of analyzing the evaluation index of the power generation status of each wind turbine is as follows: Extracting reference historical operating parameters and reference environmental data of wind turbines stored in a database; Based on the historical operating parameters of each wind turbine, wind farm environmental data, reference historical operating parameters of the wind turbine and reference environmental data, analysis and processing are performed to obtain the power generation status evaluation index of each wind turbine. The power generation status evaluation index of each wind turbine is used to characterize the power generation performance and reliability of each wind turbine in the current environment.

5. The PLC-based wind turbine master control system according to claim 1 is characterized in that: The specific process of determining the power generation mode of each wind turbine is as follows: According to the start-up correction coefficient of each wind turbine and the power generation status evaluation index of each wind turbine, the power generation capacity index parameters of each wind turbine are obtained through comprehensive analysis and processing; Extracting the wind turbine generation capacity index threshold preset in the database; If the power generation capacity indicator parameter of a wind turbine set is less than the power generation capacity index threshold of the wind turbine set, the power generation mode of the wind turbine set is marked as a low-efficiency power generation mode; if the power generation capacity indicator parameter of a wind turbine set is greater than or equal to the power generation capacity index threshold of the wind turbine set, the power generation mode of the wind turbine set is marked as a high-efficiency power generation mode.

6. The PLC-based wind turbine master control system according to claim 1 is characterized in that: The specific analysis process of analyzing the power generation status verification index of each wind turbine is as follows: The operating status data of each wind turbine generator set includes the change in generator speed, the change in blade speed, the average generator output power and the average voltage on the grid side of each wind turbine generator set; Based on the operating status data of each wind turbine, the power generation status verification index of each wind turbine is obtained by analysis and processing; The power generation status verification index of each wind turbine generator set is a numerical result of quantifying the operating status data of each wind turbine generator set, and is used to characterize the power generation performance and rationality of the operating status of each wind turbine generator set during operation.

7. The PLC-based wind turbine master control system according to claim 6 is characterized in that: The power generation task of each wind turbine is adaptively adjusted based on the power generation status verification index of each wind turbine. The specific adjustment method is as follows: Extracting a first verification index of a wind turbine generator set power generation status and a second verification index of a wind turbine generator set power generation status preset in a database; Extracting the wind turbine generator set power generation status verification index adjustment value preset in the database; Analyze the power generation capacity correction parameters of each wind turbine according to the first verification index of the wind turbine power generation status, the second verification index of the wind turbine power generation status and the adjustment value of the wind turbine power generation status verification index; The power generation capacity correction parameters of each wind turbine are compared with the power generation capacity index threshold of the wind turbine, and the adaptive adjustment power generation mode of each wind turbine is analyzed, and each wind turbine is adaptively adjusted according to the corresponding adaptive adjustment power generation mode.

8. The PLC-based wind turbine master control system according to claim 7 is characterized in that: The specific acquisition method of the power generation capacity correction parameter of each wind turbine is as follows: Among them, A i ′ is the correction parameter of the power generation capacity of the i-th wind turbine, A i is the index parameter of the power generation capacity of the i-th wind turbine, B i is the verification index of the power generation status of the i-th wind turbine, B1 is the first verification index of the power generation status of the wind turbine, B2 is the second verification index of the power generation status of the wind turbine, is the wind turbine generator set power generation status verification index adjustment value, i is the number of each wind turbine generator set, i=1,2,...,m, m is the number of wind turbine generator sets.

9. The PLC-based wind turbine master control system according to claim 7 further comprises: The wind turbine fault diagnosis module is used to correct the parameters according to the power generation capacity of each wind turbine and analyze the fault information of the wind turbine. The specific process is as follows: Extracting the threshold value of wind turbine generating capacity correction parameter preset in the database; If the power generation capacity correction parameter of a wind turbine is less than or equal to the power generation capacity correction parameter threshold of the wind turbine, the fault information of the wind turbine is marked as a faulty wind turbine, the fault information of the wind turbine is uploaded to the remote monitoring center synchronously, and the operation of the wind turbine is stopped through PLC control.

10. A PLC-based wind turbine master control method, characterized in that: The following steps are involved: S1, monitor the startup process of each wind turbine through PLC, collect the startup data of each wind turbine, analyze the startup correction coefficient of each wind turbine, synchronously obtain the historical operating parameters of each wind turbine and wind farm environmental data, and analyze the power generation status evaluation indicators of each wind turbine; S2, determining the power generation mode of each wind turbine generator set according to the start-up correction coefficient of each wind turbine generator set and the power generation status evaluation index of each wind turbine generator set; S3, within a preset monitoring period, obtaining the operating status data of each wind turbine set, analyzing the power generation status verification index of each wind turbine set, and adaptively adjusting the power generation task of each wind turbine set based on the power generation status verification index of each wind turbine set; S4, uploading the real-time power generation status information of each wind turbine to the PLC remote monitoring center to monitor the power generation process of the wind turbine in real time.

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