A wind power generation control method and system

Abstract

The present invention discloses a wind power generation control method and system, which relates to the field of wind power generation technology, including dividing the blades of each wind turbine into multiple control groups, initializing the windward angle and speed parameters of each group of blades; dynamically adjusting the operating parameters of each wind turbine; controlling the windward angle and speed of different blade groups; regularly evaluating the overall power generation efficiency of the wind farm, optimizing and adjusting the grouped blade settings and coordinated parameters of multiple wind turbines; collecting historical data during the operation of the wind farm, analyzing the impact of blade grouping control and load distribution strategies on power generation efficiency, continuously updating control algorithms, and realizing adaptive optimization. The present invention improves the efficiency of the coordinated work of multiple wind turbines through overall evaluation and coordinated optimization of the wind farm, avoids the negative impact of local uneven wind speed on the overall power generation efficiency, and provides an efficient and reliable control solution for the wind power generation industry by improving the working efficiency of wind turbines.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a wind power generation control method and system. Background Art

[0002] As the global demand for renewable energy continues to increase, wind power generation has been widely used as an environmentally friendly and efficient form of energy. In recent years, with the continuous optimization of wind turbine design and the continuous innovation of control systems, the efficiency and stability of wind power generation have been significantly improved. At present, the research focus of wind power generation technology is mainly on the design and optimization of wind turbine blades, the rationalization of wind field layout, and the intelligent control of wind turbine operation. Wind turbine blade control is a key factor in improving wind energy capture efficiency, especially in the coordinated operation of multiple wind turbines. How to accurately adjust the windward angle and speed of the blades of each wind turbine to maximize the utilization of wind energy is one of the current hot spots in technology research and development.

[0003] Although wind power technology has made significant progress, there are still many technical difficulties in practical applications; traditional wind power control methods mostly rely on the independent operation of a single wind turbine, and lack sufficient consideration for the coordinated work of multiple wind turbines in a wind farm, which may lead to problems such as uneven load between wind turbines and low wind energy capture efficiency; secondly, due to the dynamic changes in wind speed and the complexity of the environment in the wind farm, existing control methods often find it difficult to optimize the blade control strategy of each wind turbine in real time, resulting in the wind power generation efficiency not being able to reach the optimal state in actual operation; in addition, the existing control system has a certain lag in real-time data acquisition, wind speed distribution analysis and adjustment of blade control strategies, making it difficult to quickly respond to changes in the wind farm and unable to achieve adaptive optimization, limiting the intelligence level of the wind power system. Summary of the invention

[0004] In view of the existing problems of low wind energy capture efficiency, uneven load of wind turbines, and delayed control response, a wind power generation control method and system are proposed in the present invention.

[0005] Therefore, the problem to be solved by the present invention is how to solve the problems of low wind energy capture efficiency, uneven load of wind turbines, and delayed control response.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a wind power generation control method, which includes deploying a distributed sensor network to monitor the wind speed, wind direction and environmental parameters of each area in the wind farm in real time, and transmitting the data to a central control system; according to the wind speed monitoring data, the blades of each wind turbine are divided into multiple control groups, and the windward angle and speed parameters of each group of blades are initialized to adapt to the local wind speed distribution; the central control system dynamically adjusts the operating parameters of each wind turbine based on the real-time power generation load of multiple wind turbines in the wind farm to ensure load balance and extend the life of the equipment; according to the real-time wind speed changes of each wind turbine, the windward angle and speed of different blade groups are controlled to maximize the local wind energy capture efficiency; the overall power generation efficiency of the wind farm is regularly evaluated, and the grouped blade settings and coordinated parameters of multiple wind turbines are optimized and adjusted according to the distribution trend of the wind speed in the wind farm; historical data during the operation of the wind farm is collected, the influence of blade grouping control and load distribution strategies on power generation efficiency is analyzed, and the control algorithm is continuously updated to achieve adaptive optimization.

[0008] As a preferred solution of the wind power generation control method described in the present invention, environmental monitoring equipment such as wind speed sensors, temperature sensors, and humidity sensors are arranged at various key locations in the wind farm (such as wind turbine locations at different heights and surrounding areas); these devices need to have high accuracy and stability and be able to obtain environmental parameters such as wind speed, wind direction, temperature, and humidity in real time.

[0009] All sensors transmit the real-time collected data to the central control system via a wireless network or a wired network; the transmission frequency should ensure that wind speed changes can be reflected in the system immediately, and data collection can usually be set every 5 to 10 seconds.

[0010] After receiving the real-time data, the central control system will conduct a preliminary screening of the data to remove noise data or outliers. For example, when a sensor fails, the system will make corrections by comparing the data with those of surrounding sensors. The screened data will be used for subsequent wind speed distribution analysis and control decisions.

[0011] As a preferred solution of the wind power generation control method described in the present invention, wherein: according to the screened data, the wind field area is divided into several wind speed zones, each wind speed zone has a relatively consistent wind speed level; the wind speed in each area is divided according to a preset wind speed threshold to form different wind speed groups (such as low wind speed zone, medium wind speed zone, high wind speed zone); these wind speed zones determine the division and adjustment parameters of subsequent blade groups.

[0012] The blades of each wind turbine are divided into multiple control groups according to the wind speed zones. For example, the blade group in the low wind speed zone may adopt a larger windward angle and a lower rotation speed, while the blade group in the high wind speed zone may adopt a smaller windward angle and a higher rotation speed. The blade adjustment parameters (including windward angle and rotation speed) of each control group are automatically initialized through the control system to ensure that they are compatible with the wind energy capture requirements of the wind speed zone.

[0013] As a preferred solution of the wind power generation control method of the present invention, the windward angle and rotation speed of the blades of each control group are adjusted in real time according to the real-time wind speed data changes and the dynamic distribution of wind speed in the wind farm, as follows:

[0014] ,

[0015] ,

[0016] in, represents the blade windward angle of the ith control group at time t, and Respectively represent the wind speed weight and error term of the preset i-th control group, represents the wind speed data at time t, represents the speed of the ith control group at time t, and Represents the preset speed weight and error term of the ith control group.

[0017] In areas with higher wind speeds, the windward angle is appropriately lowered to avoid overspeeding; in areas with lower wind speeds, the windward angle is increased to improve wind energy capture efficiency; the control system automatically adjusts the working status of each group of blades according to changes in wind speed to ensure that each group of blades is always in the best working condition.

[0018] Collect the operational feedback data of each control group, including power generation, wind speed and blade angle changes, and evaluate the working effects of different control groups, as follows:

[0019] ,

[0020] ,

[0021] ,

[0022] in, represents the power generation efficiency of the ith control group at time t, represents the actual power generation of the ith control group at time t, Indicates the preset maximum power generation. represents density, A represents the area covered by the blade rotation, represents the power factor, and They respectively represent the influence coefficient of windward angle on power coefficient and the influence coefficient of rotation speed on power coefficient;

[0023] Based on these feedback data, the windward angle and speed settings of each set of blades are gradually optimized to ensure that the wind turbines can operate stably and efficiently in different wind speed zones; the optimization plan is pushed to each wind turbine through the central control system to form a closed-loop control.

[0024] As a preferred solution of the wind power generation control method of the present invention, the central control system collects the power generation load data of each wind turbine in the wind farm in real time, including the power output, current, voltage and other parameters of each wind turbine. The load state of each wind turbine is evaluated through wind speed, wind direction and blade control data, as follows:

[0025] ,

[0026] in, represents the load state of the i-th control group of the j-th wind turbine at time t, represents the actual power generation of the i-th control group of the j-th wind turbine at time t, Indicates the maximum power generation of the i-th control group of the j-th wind turbine.

[0027] According to the load status of the wind turbine, the operating parameters of the wind turbine are automatically adjusted, as follows:

[0028] ,

[0029] in, represents the load deviation of the ith control group of the jth wind turbine at time t, Indicates the preset target load status.

[0030] Update the blade windward angle and speed. The update formula is:

[0031] ,

[0032] ,

[0033] ,

[0034] ,

[0035] in, represents the blade windward angle of the i-th control group of the j-th wind turbine at time t+1, represents the speed of the i-th control group of the j-th wind turbine at time t+1, represents the blade windward angle of the i-th control group of the j-th wind turbine at time t, represents the speed of the i-th control group of the j-th wind turbine at time t, Indicates the adjustment degree of the blade's windward angle. Indicates the speed adjustment degree. and They respectively represent the influencing factors of the preset blade windward angle and rotation speed.

[0036] Calculate the load deviation of the i-th control group of the j-th wind turbine at time t+1 ,like , then the adjustment is determined to be completed, where Indicates the preset deviation threshold.

[0037] Ensure that the load of each wind turbine in the wind farm is balanced; for example, if the load of a wind turbine is too high, while the load of other wind turbines around it is lower, the system will automatically reduce the load of the wind turbine, and increase the workload of other wind turbines by adjusting the speed or windward angle to maintain the overall load balance.

[0038] The system continuously monitors the operating status and environmental changes of wind turbines based on real-time load balancing results. If it is found that the load of a certain wind turbine has not been effectively optimized, or a new imbalance in load balancing occurs, the central control system will adjust its control parameters through a feedback mechanism and implement more refined dynamic adjustments. This process is a cyclic feedback optimization process that continuously improves the overall operating efficiency of the wind farm and the life of the equipment.

[0039] As a preferred solution of the wind power generation control method of the present invention, the local wind speed condition of each wind turbine is dynamically calculated based on the real-time monitored wind speed data, as follows:

[0040] ,

[0041] in, represents the local wind speed condition of the jth wind turbine, represents the wind speed data detected by the xth sensor in the area of ​​the jth wind turbine, represents the weighting factor of the x-th sensor, Represents the total number of sensors in the area described by the j-th wind turbine.

[0042] For areas with large wind speed changes, the control system automatically adjusts the windward angle and rotation speed of the blades of each wind turbine; the blades of each wind turbine are divided into multiple control groups, and each group adjusts its working state according to the wind speed changes to maintain the optimization of wind energy capture; for example, when the wind speed increases, the windward angle is reduced and the rotation speed is increased; when the wind speed decreases, the windward angle is increased and the rotation speed is reduced.

[0043] At regular intervals, the central control system evaluates the operating efficiency of each wind turbine based on the power generation data of the entire wind farm. The system identifies inefficient areas and potential optimization space in the overall wind farm by comparing the power generation, wind speed distribution and operating status of each wind turbine. Through comprehensive analysis, the system adjusts the grouped blade settings and collaborative working parameters of the wind turbines to optimize the operating status of each wind turbine to maximize the power generation efficiency of the entire wind farm.

[0044] The system combines historical wind speed data and meteorological forecast information to analyze the wind speed change trend in the wind farm and predict the possibility of future wind speed changes. On this basis, the central control system presets a long-term adjustment strategy to make adaptive adjustments in advance for possible future wind speed changes and optimize the configuration and operating parameters of the blade group. This predictive adjustment avoids the negative impact of sudden changes in wind speed and improves the long-term power generation stability of wind turbines.

[0045] As a preferred solution of the wind power generation control method described in the present invention, during the operation of the wind farm, the system continuously collects and stores all relevant historical data, including but not limited to wind speed, wind direction, blade angle, rotation speed, power generation load, equipment failure and other information; by structured processing of these data, key performance indicators are extracted, such as the average power generation efficiency of each wind turbine, load fluctuations, response time of blade group adjustment, etc.; these data provide a basis for subsequent analysis and optimization.

[0046] By analyzing the collected historical data, the specific impact of different blade grouping control and load distribution strategies on wind farm power generation efficiency is evaluated. The system uses statistical analysis methods or machine learning algorithms to quantify the effects of different control strategies; for example, it analyzes the effect of blade angle adjustment and load balancing mechanism on the overall wind farm power generation efficiency, identifies the best control strategy under different wind speed conditions, and further refines the parameter settings of blade control and load distribution.

[0047] Based on the results of historical data analysis and effect evaluation, the central control system updates the control algorithm regularly or in real time to adapt to the operating conditions and environmental changes of the wind farm; the updated algorithm will optimize blade grouping control, load distribution, wind speed adaptation and other aspects, so that the wind turbine can be in the best operating state under various conditions; the control system will automatically push the optimized algorithm to the wind turbine and implement real-time adjustments to achieve adaptive optimization.

[0048] In the second aspect, the embodiment of the present invention provides a wind power generation control system, which includes an environmental monitoring module, which is used to arrange environmental monitoring equipment (such as wind speed sensors, temperature sensors and humidity sensors), and transmit the collected data to the central control system through a wireless network. This module is responsible for real-time monitoring of environmental data in the wind farm and providing basic meteorological information for the system; a data screening module, which performs preliminary screening on the received real-time data, removes noise data or abnormal values, and ensures the accuracy and reliability of the data; a wind speed area and blade control group division module, which divides the wind farm into different wind speed areas according to the wind speed data, and divides the blades of each wind turbine into multiple control groups based on the wind speed areas, and automatically initializes the blade adjustment parameters (such as windward angle and speed) of each control group; a dynamic adjustment and load monitoring module, which dynamically adjusts the windward angle and speed of the blades of each control group of the wind turbine according to the real-time wind speed changes and the dynamic distribution of wind speed in the wind farm, and monitors and optimizes the power generation load of the wind turbine; a historical data analysis and control optimization module, which is used to collect and analyze historical data, evaluate the effects of different control strategies, and optimize the control algorithm based on the analysis results to adapt to the operating conditions and environmental changes of the wind farm.

[0049] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of a wind power generation control method as described in the first aspect of the present invention are implemented.

[0050] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of a wind power generation control method as described in the first aspect of the present invention are implemented.

[0051] The beneficial effects of the present invention are as follows: the present invention deploys a distributed sensor network to monitor the wind speed, wind direction and environmental parameters in the wind farm in real time, and transmits the data to the central control system, thereby ensuring real-time feedback of the wind farm operation status; based on the analysis of wind speed data, the method divides the wind farm into different wind speed zones, and divides the blades of each wind turbine into multiple control groups, dynamically adjusting the windward angle and rotation speed of each group of blades to maximize the capture efficiency of local wind energy; by real-time monitoring of the power generation load of the wind turbine, the central control system can automatically adjust the operating parameters according to the load conditions of each wind turbine, balance the load and improve the power generation efficiency; in addition, the system also regularly evaluates the power generation efficiency of the entire wind farm, combines historical data analysis with optimized control algorithms, and continuously and adaptively adjusts the control strategy to ensure that the wind farm always maintains efficient operation under different climatic conditions; the method effectively improves the overall efficiency of wind power generation, reduces equipment load, and extends the service life of wind turbines through precise control and intelligent optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0053] Figure 1 Schematic diagram of the steps of the wind power generation control method.

[0054] Figure 2 It is a structural diagram of the wind power generation control system. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0057] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0058] Example 1, reference Figure 1~Figure 2 , which is the first embodiment of the present invention, provides a wind power generation control method, including:

[0059] Step S1: deploy a distributed sensor network to monitor the wind speed, wind direction and environmental parameters of each area in the wind farm in real time, and transmit the data to the central control system.

[0060] Environmental monitoring equipment such as wind speed sensors, temperature sensors, and humidity sensors are placed at key locations within the wind farm (such as wind turbines at different heights and surrounding areas); these devices need to have high accuracy and stability and be able to obtain environmental parameters such as wind speed, wind direction, temperature, and humidity in real time.

[0061] All sensors transmit the real-time collected data to the central control system via a wireless network or a wired network; the transmission frequency should ensure that wind speed changes can be reflected in the system immediately, and data collection can usually be set every 5 to 10 seconds.

[0062] After receiving the real-time data, the central control system will conduct a preliminary screening of the data to remove noise data or outliers. For example, when a sensor fails, the system will make corrections by comparing the data with those of surrounding sensors. The screened data will be used for subsequent wind speed distribution analysis and control decisions.

[0063] Step S2: According to the wind speed monitoring data, the blades of each wind turbine are divided into multiple control groups, and the windward angle and speed parameters of each group of blades are initialized to adapt to the local wind speed distribution.

[0064] Based on the screened data, the wind farm area is divided into several wind speed zones, each of which has a relatively consistent wind speed level; the wind speed in each area is divided according to the preset wind speed threshold to form different wind speed groups (such as low wind speed zone, medium wind speed zone, and high wind speed zone); these wind speed zones determine the division and adjustment parameters of subsequent blade groups.

[0065] The blades of each wind turbine are divided into multiple control groups according to the wind speed zones. For example, the blade group in the low wind speed zone may adopt a larger windward angle and a lower rotation speed, while the blade group in the high wind speed zone may adopt a smaller windward angle and a higher rotation speed. The blade adjustment parameters (including windward angle and rotation speed) of each control group are automatically initialized through the control system to ensure that they are compatible with the wind energy capture requirements of the wind speed zone.

[0066] According to the real-time wind speed data changes and the dynamic distribution of wind speed in the wind farm, the blade windward angle and speed of each control group are adjusted in real time, as follows:

[0067] ,

[0068] ,

[0069] in, represents the blade windward angle of the ith control group at time t, and Respectively represent the wind speed weight and error term of the preset i-th control group, represents the wind speed data at time t, represents the speed of the ith control group at time t, and Represents the preset speed weight and error term of the ith control group.

[0070] In areas with higher wind speeds, the windward angle is appropriately lowered to avoid overspeeding; in areas with lower wind speeds, the windward angle is increased to improve wind energy capture efficiency; the control system automatically adjusts the working status of each group of blades according to changes in wind speed to ensure that each group of blades is always in the best working condition.

[0071] Collect the operational feedback data of each control group, including power generation, wind speed and blade angle changes, and evaluate the working effects of different control groups, as follows:

[0072] ,

[0073] ,

[0074] ,

[0075] in, represents the power generation efficiency of the ith control group at time t, represents the actual power generation of the ith control group at time t, Indicates the preset maximum power generation. represents density, A represents the area covered by the blade rotation, represents the power factor, and They respectively represent the influence coefficient of windward angle on the power coefficient and the influence coefficient of rotation speed on the power coefficient.

[0076] Based on these feedback data, the windward angle and speed settings of each set of blades are gradually optimized to ensure that the wind turbines can operate stably and efficiently in different wind speed zones; the optimization plan is pushed to each wind turbine through the central control system to form a closed-loop control.

[0077] Step S3: The central control system dynamically adjusts the operating parameters of each wind turbine based on the real-time power generation load of multiple wind turbines in the wind farm to ensure load balance and extend the life of the equipment.

[0078] The central control system collects the power generation load data of each wind turbine in the wind farm in real time, including the power output, current, voltage and other parameters of each wind turbine; through the wind speed, wind direction and blade control data, the load status of each wind turbine is evaluated, as follows:

[0079] ,

[0080] in, represents the load state of the i-th control group of the j-th wind turbine at time t, represents the actual power generation of the i-th control group of the j-th wind turbine at time t, Indicates the maximum power generation of the i-th control group of the j-th wind turbine.

[0081] According to the load status of the wind turbine, the operating parameters of the wind turbine are automatically adjusted, as follows:

[0082] ,

[0083] in, represents the load deviation of the ith control group of the jth wind turbine at time t, Indicates the preset target load status.

[0084] Update the blade windward angle and speed. The update formula is:

[0085] ,

[0086] ,

[0087] ,

[0088] ,

[0089] in, represents the blade windward angle of the i-th control group of the j-th wind turbine at time t+1, represents the speed of the i-th control group of the j-th wind turbine at time t+1, represents the blade windward angle of the i-th control group of the j-th wind turbine at time t, represents the speed of the i-th control group of the j-th wind turbine at time t, Indicates the adjustment degree of the blade's windward angle. Indicates the speed adjustment degree. and They respectively represent the influencing factors of the preset blade windward angle and rotation speed.

[0090] Calculate the load deviation of the i-th control group of the j-th wind turbine at time t+1 ,like , then the adjustment is determined to be completed, where Indicates the preset deviation threshold.

[0091] Ensure that the load of each wind turbine in the wind farm is balanced; for example, if the load of a wind turbine is too high, while the load of other wind turbines around it is lower, the system will automatically reduce the load of the wind turbine, and increase the workload of other wind turbines by adjusting the speed or windward angle to maintain the overall load balance.

[0092] The system continuously monitors the operating status and environmental changes of wind turbines based on real-time load balancing results. If it is found that the load of a certain wind turbine has not been effectively optimized, or a new imbalance in load balancing occurs, the central control system will adjust its control parameters through a feedback mechanism and implement more refined dynamic adjustments. This process is a cyclic feedback optimization process that continuously improves the overall operating efficiency of the wind farm and the life of the equipment.

[0093] Step S4: According to the real-time wind speed changes of each wind turbine, the windward angle and rotation speed of different blade groups are controlled to maximize the local wind energy capture efficiency; the overall power generation efficiency of the wind farm is regularly evaluated, and according to the distribution trend of wind speed in the wind farm, the grouped blade settings and coordinated parameters of multiple wind turbines are optimized and adjusted.

[0094] Based on the real-time monitored wind speed data, the local wind speed conditions of each wind turbine are dynamically calculated as follows:

[0095] ,

[0096] in, represents the local wind speed condition of the jth wind turbine, represents the wind speed data detected by the xth sensor in the area of ​​the jth wind turbine, represents the weighting factor of the x-th sensor, Represents the total number of sensors in the area described by the j-th wind turbine.

[0097] For areas with large wind speed changes, the control system automatically adjusts the windward angle and speed of each wind turbine blade; the blades of each wind turbine are divided into multiple control groups, and each group adjusts its working state according to the wind speed changes to maintain the optimization of wind energy capture. For example, when the wind speed increases, the windward angle is reduced and the speed is increased; when the wind speed decreases, the windward angle is increased and the speed is reduced.

[0098] At regular intervals, the central control system evaluates the operating efficiency of each wind turbine based on the power generation data of the entire wind farm. The system identifies inefficient areas and potential optimization space in the overall wind farm by comparing the power generation, wind speed distribution and operating status of each wind turbine. Through comprehensive analysis, the system adjusts the grouped blade settings and collaborative working parameters of the wind turbines to optimize the operating status of each wind turbine to maximize the power generation efficiency of the entire wind farm.

[0099] The system combines historical wind speed data and meteorological forecast information to analyze the wind speed change trend in the wind farm and predict the possibility of future wind speed changes. On this basis, the central control system presets a long-term adjustment strategy to make adaptive adjustments in advance for possible future wind speed changes and optimize the configuration and operating parameters of the blade group. This predictive adjustment avoids the negative impact of sudden changes in wind speed and improves the long-term power generation stability of wind turbines.

[0100] Step S5: Collect historical data during the operation of the wind farm, analyze the impact of blade grouping control and load distribution strategy on power generation efficiency, continuously update the control algorithm, and achieve adaptive optimization.

[0101] During the operation of the wind farm, the system continuously collects and stores all relevant historical data, including but not limited to wind speed, wind direction, blade angle, rotation speed, power generation load, equipment failure and other information; by structuring these data, key performance indicators are extracted, such as the average power generation efficiency of each wind turbine, load fluctuations, response time of blade group adjustment, etc.; these data provide the basis for subsequent analysis and optimization.

[0102] By analyzing the collected historical data, the specific impact of different blade grouping control and load distribution strategies on wind farm power generation efficiency is evaluated. The system uses statistical analysis methods or machine learning algorithms to quantify the effects of different control strategies; for example, it analyzes the effect of blade angle adjustment and load balancing mechanism on the overall wind farm power generation efficiency, identifies the best control strategy under different wind speed conditions, and further refines the parameter settings of blade control and load distribution.

[0103] Based on the results of historical data analysis and effect evaluation, the central control system updates the control algorithm regularly or in real time to adapt to the operating conditions and environmental changes of the wind farm; the updated algorithm will optimize blade grouping control, load distribution, wind speed adaptation and other aspects, so that the wind turbine can be in the best operating state under various conditions; the control system will automatically push the optimized algorithm to the wind turbine and implement real-time adjustments to achieve adaptive optimization.

[0104] Furthermore, the present embodiment also provides a wind power generation control system, including an environmental monitoring module, which is used to arrange environmental monitoring equipment (such as wind speed sensors, temperature sensors and humidity sensors), and transmit the collected data to the central control system through a wireless network. This module is responsible for real-time monitoring of environmental data in the wind farm and providing basic meteorological information for the system; a data screening module, which performs preliminary screening on the received real-time data, removes noise data or abnormal values, and ensures the accuracy and reliability of the data; a wind speed area and blade control group division module, which divides the wind farm into different wind speed areas according to the wind speed data, and divides the blades of each wind turbine into multiple control groups based on the wind speed areas, and automatically initializes the blade adjustment parameters (such as windward angle and speed) of each control group; a dynamic adjustment and load monitoring module, which dynamically adjusts the windward angle and speed of the blades of each control group of the wind turbine according to the real-time wind speed changes and the dynamic distribution of wind speed in the wind farm, and monitors and optimizes the power generation load of the wind turbine; a historical data analysis and control optimization module, which is used to collect and analyze historical data, evaluate the effects of different control strategies, and optimize the control algorithm based on the analysis results to adapt to the operating conditions and environmental changes of the wind farm.

[0105] This embodiment also provides a computer device, which is applicable to the case of a wind power generation control method, and includes a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to implement the wind power generation control method proposed in the above embodiment.

[0106] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0107] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the wind power generation control method proposed in the above embodiment is implemented.

[0108] In summary, by deploying a distributed sensor network to monitor the wind speed, wind direction and environmental parameters in the wind farm in real time, and transmitting the data to the central control system, real-time feedback of the wind farm operation status is ensured; based on the analysis of wind speed data, the method divides the wind farm into different wind speed zones, and divides the blades of each wind turbine into multiple control groups, dynamically adjusting the windward angle and rotation speed of each group of blades to maximize the capture efficiency of local wind energy; by real-time monitoring of the power generation load of the wind turbine, the central control system can automatically adjust the operating parameters according to the load conditions of each wind turbine, balance the load and improve the power generation efficiency; in addition, the system also regularly evaluates the power generation efficiency of the entire wind farm, combines historical data analysis with optimized control algorithms, and continuously and adaptively adjusts the control strategy to ensure that the wind farm always maintains efficient operation under different climatic conditions; this method effectively improves the overall efficiency of wind power generation, reduces equipment load, and extends the service life of wind turbines through precise control and intelligent optimization.

[0109] Embodiment 2: In this embodiment 2, a wind power generation control method is provided. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0110] The experiment selected a wind farm with multiple wind turbines. A variety of environmental monitoring sensors were deployed in the wind farm, including wind speed sensors, temperature sensors, humidity sensors, etc. The sensors covered different heights and different areas in the wind farm to obtain accurate wind speed, wind direction and environmental parameters.

[0111] Firstly, 10 wind turbines were deployed in the wind farm, and 30 wind speed sensors, 20 temperature sensors and 15 humidity sensors were installed. The sampling frequency of the sensors was set to once every 5 seconds to ensure that the wind speed changes could be captured in real time. All sensor data were transmitted to the central control system through the wireless network. The system preliminarily screened the data to remove noise data and outliers to ensure the accuracy of the data. Based on the distribution of wind speed data, the central control system divided the wind farm into low wind speed zone (wind speed less than 3 m / s), medium wind speed zone (wind speed of 3-6 m / s) and high wind speed zone (wind speed greater than 6 m / s). The parameters of the blade control group in each wind speed zone were initialized according to the actual wind speed.

[0112] During the initialization process, the wind turbine blade group in the low wind speed zone is set with a larger windward angle and a lower rotation speed; in the high wind speed zone, the blade windward angle is smaller and the rotation speed is higher; each wind turbine automatically adjusts the windward angle and rotation speed of the blades according to the real-time wind speed data to ensure that each wind turbine can achieve the best power generation efficiency under local wind speed conditions; the adjustment process uses a control algorithm to gradually optimize the working state of each wind turbine by calculating the wind speed changes and load conditions.

[0113] The experiment also evaluates the load balancing effect by real-time monitoring of the power generation load, current and voltage data of each wind turbine. If it is found that the load of a wind turbine is too high while the load of surrounding wind turbines is lower, the central control system automatically reduces the load of the wind turbine and increases the workload of other wind turbines to ensure load balance within the wind farm, thereby improving the overall power generation efficiency of the wind farm and extending the life of the equipment.

[0114] Through multiple tests and data analysis, this embodiment verifies that measures such as grouped blade control, dynamic adjustment of windward angle and rotation speed, and real-time load balancing can improve the power generation efficiency of wind turbines in wind fields with large wind speed changes and maintain long-term stable operation of the equipment.

[0115] Table 1 Data monitoring table

[0116] Wind turbine number Wind speed zone Blade windward angle (°) Blade speed (rpm) Power generation efficiency (%) Power generation (kW) Load status (kW) Wind turbine 1 Low wind speed area 20 12 75 150 180 Wind turbine 1 Medium wind speed zone 15 20 85 220 240 Wind turbine 1 High wind speed area 10 30 90 300 320 Wind turbine 2 Low wind speed area 22 11 70 140 170 Wind turbine 2 Medium wind speed zone 16 19 80 210 230 Wind turbine 2 High wind speed area 9 28 88 290 310 Wind turbine 3 Low wind speed area 18 13 72 145 175 Wind turbine 3 Medium wind speed zone 14 18 82 200 220 Wind turbine 3 High wind speed area 11 29 87 295 315 Wind turbine 4 Low wind speed area 21 10 68 130 160 Wind turbine 4 Medium wind speed zone 17 21 83 215 235 Wind turbine 4 High wind speed area 8 27 86 280 300 Wind turbine 5 Low wind speed area 19 14 74 150 180 Wind turbine 5 Medium wind speed zone 13 22 84 225 245 Wind turbine 5 High wind speed area 10 31 92 310 330

[0117] It can be seen from Table 1 that in different wind speed regions, the windward angle of the wind turbine blades, the rotation speed, the power generation efficiency and the load state show obvious regularity, which verifies the effectiveness of the dynamic regulation and load balancing proposed in the present invention.

[0118] Low wind speed area: In the low wind speed area, the wind speed is low and the wind energy capture efficiency is limited. Therefore, the wind turbine blades have a larger windward angle (20-22°) and a lower rotation speed (10-14 rpm). Despite this, due to the optimization of the blade windward angle, the wind turbine can still capture wind energy better, ensuring the power generation efficiency (70%-75%) and power generation capacity (130-150 kW).

[0119] Medium wind speed area: As the wind speed increases, the blade angle to the wind decreases moderately (14-17°) and the rotation speed increases accordingly (18-22 rpm), which further improves the power generation efficiency of the wind turbine (80%-85%) and increases the power generation capacity (200-225 kW).

[0120] High wind speed zone: In the high wind speed zone, the wind speed is higher and the potential for wind energy capture is the greatest; at this time, the wind turbine blade angle is further reduced (8-11°), the rotation speed is increased to a higher level (27-31 rpm), and the wind turbine can achieve the highest power generation efficiency (86%-92%) and generate the maximum power (280-330 kW).

[0121] In addition, the "load status" data in the table reflects the load balance of the wind turbine. In the high wind speed area, the load of the wind turbine is close to its maximum power generation, and the load balance is good, indicating that the central control system can adjust the working status of each wind turbine in time according to the change of wind speed, avoiding the overload of a wind turbine affecting the efficiency of the entire wind farm.

[0122] Compared with the prior art, the present invention significantly improves the power generation efficiency of wind turbines under different wind speed conditions by dynamically adjusting the windward angle and rotation speed of wind turbine blades. In particular, under low wind speed conditions, wind turbines can still capture wind energy well, thus improving the overall power generation efficiency. At the same time, the load balancing mechanism ensures the efficient coordination of wind turbines in the wind farm, prolongs the service life of the equipment, and avoids equipment damage caused by local overload operation.

[0123] In summary, this embodiment significantly optimizes the operating efficiency of the wind turbine and the overall power generation effect of the wind farm by comprehensively considering wind speed changes, blade control and load regulation, fully demonstrating the innovation and advantages of the present invention in the field of wind power generation.

[0124] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A wind power generation control method, characterized in that: include: Step S1: deploy a distributed sensor network to monitor wind speed, wind direction and environmental parameters in the wind farm in real time, and transmit the data to a central control system; Step S2: According to the wind speed monitoring data, the blades of each wind turbine are divided into multiple control groups, and the windward angle and speed parameters of each group of blades are initialized; according to the screened data, the wind farm area is divided into several wind speed zones, each wind speed zone has a relatively consistent wind speed level; the wind speed of each wind speed zone is divided according to a preset wind speed threshold to form different wind speed groups, and the wind speed groups include a low wind speed zone, a medium wind speed zone and a high wind speed zone; Based on the wind speed zone, the blades of each wind turbine are divided into a plurality of control groups, and the blade adjustment parameters of each control group are automatically initialized by the control system, wherein the blade adjustment parameters include windward angle and rotation speed; Step S3: The central control system dynamically adjusts the operating parameters of each wind turbine based on the real-time power generation load of multiple wind turbines in the wind farm; Step S4: according to the real-time wind speed change of each wind turbine, the windward angle and rotation speed of different blade groups are controlled to maximize the local wind energy capture efficiency; the overall power generation efficiency of the wind farm is regularly evaluated, and the grouped blade settings and coordinated parameters of multiple wind turbines are optimized and adjusted according to the distribution trend of wind speed in the wind farm; Step S5: Collect historical data during the operation of the wind farm, analyze the impact of blade grouping control and load distribution strategy on power generation efficiency, continuously update the control algorithm, and achieve adaptive optimization; The implementation process of step S1 includes: Arrange environmental monitoring equipment in the wind farm, the environmental monitoring equipment includes a wind speed sensor, a temperature sensor and a humidity sensor, and transmit the data collected by the environmental monitoring equipment to a central control system via a wireless network; After receiving the real-time data, the central control system performs a preliminary screening of the data to remove noise data or abnormal values; The implementation process of step S2 also includes: According to the real-time wind speed data changes and the dynamic distribution of wind speed in the wind farm, the blade windward angle and speed of each control group are adjusted in real time, as follows: ； ； in, represents the blade windward angle of the ith control group at time t, and Respectively represent the wind speed weight and error term of the preset i-th control group, represents the wind speed data at time t, represents the speed of the ith control group at time t, and represents the speed weight and error term of the preset i-th control group; In high wind speed areas, the windward angle is reduced; in low wind speed areas, the windward angle is increased; the control system automatically adjusts the working state of each set of blades according to the change in wind speed; collects the operation feedback data of each control group, which includes power generation, wind speed and blade angle changes, and evaluates the working effects of different control groups, as follows: ； ； ； in, represents the power generation efficiency of the ith control group at time t, represents the actual power generation of the ith control group at time t, Indicates the preset maximum power generation. represents density, A represents the area covered by the blade rotation, represents the power factor, and They respectively represent the influence coefficient of windward angle on power coefficient and the influence coefficient of rotation speed on power coefficient; Based on the operational feedback data, the windward angle and rotation speed setting of each set of blades are optimized.

2. The wind power generation control method according to claim 1, characterized in that: The implementation process of step S3 includes: The central control system collects the power generation load data of each wind turbine in the wind farm in real time. The power generation load data includes the power output, current and voltage of each wind turbine. The load status of each wind turbine is evaluated through wind speed, wind direction and blade control data, as follows: ； in, represents the load state of the i-th control group of the j-th wind turbine at time t, represents the actual power generation of the i-th control group of the j-th wind turbine at time t, represents the maximum power generation of the i-th control group of the j-th wind turbine; According to the load status of the wind turbine, the operating parameters of the wind turbine are automatically adjusted, as follows: ； in, represents the load deviation of the ith control group of the jth wind turbine at time t, Indicates the preset target load state; Update the blade windward angle and speed. The update formula is: ； ； ； ； in, represents the blade windward angle of the i-th control group of the j-th wind turbine at time t+1, represents the speed of the i-th control group of the j-th wind turbine at time t+1, represents the blade windward angle of the i-th control group of the j-th wind turbine at time t, represents the speed of the i-th control group of the j-th wind turbine at time t, Indicates the adjustment degree of the blade's windward angle. Indicates the speed adjustment degree. and Respectively represent the influencing factors of the preset blade windward angle and rotation speed; Calculate the load deviation of the i-th control group of the j-th wind turbine at time t+1 ,like , then the adjustment is determined to be completed, where Indicates the preset deviation threshold.

3. The wind power generation control method according to claim 2, characterized in that: The implementation process of step S4 includes: Based on the real-time monitored wind speed data, the local wind speed conditions of each wind turbine are dynamically calculated as follows: ； in, represents the local wind speed condition of the jth wind turbine, represents the wind speed data detected by the xth sensor in the area of ​​the jth wind turbine, represents the weighting factor of the x-th sensor, represents the total number of sensors in the area described by the j-th wind turbine; For areas with large wind speed variations, the control system automatically adjusts the windward angle and speed of each wind turbine blade; Compare the power generation, wind speed distribution and wind turbine operating status of each wind turbine, identify the inefficient areas and potential optimization space of the overall wind farm, and adjust the grouped blade settings and collaborative working parameters of the wind turbines; Combine historical wind speed data with weather forecast information to analyze the wind speed change trend within the wind farm and predict the possibility of future wind speed changes.

4. The wind power generation control method according to claim 3, characterized in that: The implementation process of step S5 includes: During the operation of the wind farm, all relevant historical data, including wind speed, wind direction, blade angle, rotation speed, power generation load and equipment failure, are continuously collected and stored, and the historical data are structured to extract key performance indicators, including the average power generation efficiency of each wind turbine, load fluctuations and response time of blade group adjustment; The historical data is analyzed to evaluate the specific impact of different blade grouping controls and load distribution strategies on the wind farm power generation efficiency, and the control algorithm is regularly updated to adapt to the operating conditions and environmental changes of the wind farm.

5. A wind power generation control system, based on the wind power generation control method according to any one of claims 1 to 4, characterized in that: include: Environmental monitoring module, used to arrange environmental monitoring equipment, transmit the collected data to the central control system via wireless network, and monitor the environmental data in the wind farm in real time; The data screening module is used to perform preliminary screening based on the received real-time data to remove noise data or outliers; The wind speed zone and blade control group division module is used to divide the wind field into different wind speed zones according to the wind speed data, and divide the blades of each wind turbine into multiple control groups based on the wind speed zones, and automatically initialize the blade adjustment parameters of each control group; Dynamic adjustment and load monitoring module, used to dynamically adjust the windward angle and speed of the blades of each control group of the wind turbine according to the real-time wind speed changes and the dynamic distribution of wind speed in the wind farm, and monitor and optimize the power generation load of the wind turbine; The historical data analysis and control optimization module is used to collect and analyze historical data, evaluate the effects of different control strategies, and optimize the control algorithm based on the analysis results to adapt to the operating conditions and environmental changes of the wind farm.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the wind power generation control method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the wind power generation control method according to any one of claims 1 to 4 are implemented.

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