Autonomous controllable wind power master control system, control method and device for wind power plant

Abstract

The invention relates to the technical field of wind power plant data acquisition control, and particularly discloses an autonomous controllable wind power master control system, a control method and a device for a wind power plant. The system can accurately analyze an abnormal fluctuation index of an environment and a working state abnormal factor of a wind motor by comprehensively acquiring environment parameters of the wind power plant and operation parameters of the wind motor; therefore, real-time monitoring and evaluation of the operation state of the wind power plant are achieved, the abnormal factors of the working state of the wind motor are corrected according to the abnormal fluctuation index of the environment, it can be ensured that the wind motor operates efficiently in the complex and changeable environment, control process parameters of the wind motor are collected and analyzed, and the calibration precision coefficient is accurately judged. The wind power autonomous control is accurately fed back and adjusted, the real-time monitoring, accurate evaluation and intelligent regulation and control of the operation state of the wind power plant are realized, the operation efficiency and operation and maintenance efficiency of the wind power plant are effectively improved, the operation and maintenance cost is reduced, and a powerful guarantee is provided for safe and stable operation of the wind power plant.

Description

Technical Field

[0001] The present invention relates to the technical field of wind farm data acquisition and control, and in particular to an autonomous and controllable wind power main control system, a control method and a device for a wind farm. Background Art

[0002] With the increasing attention paid to renewable energy around the world and the continuous advancement of wind power technology, intelligent control of wind farms has become an important development direction in the wind power field. By integrating intelligent monitoring and management functions and adopting advanced control methods, the operating status of wind turbines can be accurately adjusted, which not only helps to achieve maximum wind energy capture and improve operating efficiency, but also ensures the stable operation of wind turbines.

[0003] For example, the invention patent with announcement number CN113315174B announces a wind farm unit control method, device, wind farm control terminal and storage medium, which relates to the field of wind farm power generation technology. When the current active power limit value of the target wind farm is greater than or equal to the performance evaluation power threshold, a wind turbine set will be alternately selected from the multiple wind turbine sets included in the target wind farm as the performance evaluation set to generate wind power by maintaining the optimal blade angle, and the remaining wind turbine sets will be used as the blade angle adaptation set to ensure that the blade angle is adapted to the wind speed change to generate wind power, so that each wind turbine in the wind farm can take turns to act as a blade angle adaptation set and a performance evaluation set when the power grid is limited, so that each wind turbine in the wind farm has the opportunity to share the pressure of blade wear of the wind speed adaptation requirements and the performance evaluation requirements of the unit.

[0004] For example, the invention patent with the announcement number CN113471986B, a method for adjusting the active power of a wind farm, a control device and a controller of a wind farm, include: obtaining an active power increment that needs to be adjusted by the wind farm; determining the adjustable active power amount of the wind farm based on the adjustable active power amount of each wind turbine generator set in the wind farm; determining the active power adjustment amount of each wind turbine generator set based on the active power increment that needs to be adjusted by the wind farm and the adjustable active power amount of the wind farm, so as to adjust the active power of each wind turbine generator set; wherein the adjustable active power amount of the wind turbine generator set includes at least one of the following items: an amount of active power that can be increased by changing the pitch, an amount of active power that can be reduced by changing the pitch, an amount of active power that can be increased by the rotor kinetic energy, which is used to characterize the amount of active power that can be increased by changing the rotor kinetic energy, and an amount of active power that can be reduced by the braking resistor, which is used to characterize the amount of active power that can be reduced by consuming the braking resistor.

[0005] However, in the process of implementing the embodiments of the present application, it was found that the above-mentioned technology has at least the following technical problems: when adjusting the wind turbines in a wind farm, the prior art only analyzes and adjusts the active power. However, since the active power response has lag and frequency, only adjusting the pitch through active power may cause the wind turbine to frequently adjust the blade angle or output power, which not only increases the mechanical wear and energy consumption of the wind turbine, but may also affect the overall operating efficiency of the wind farm. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides an autonomous and controllable wind power master control system, a control method and a device for a wind farm, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: In a first aspect, the present invention provides an autonomous and controllable wind power main control system for a wind farm, including: an environmental analysis module, which is used to collect environmental parameters of the wind farm, and analyze the abnormal fluctuation index of the environment belonging to the wind farm through a data processing device; an operation evaluation module, which is used to obtain and analyze the operating parameters of the wind turbine belonging to the wind farm through a data processing device, evaluate the abnormal working state factor of the wind turbine belonging to the wind farm, and the control device determines whether to correct the abnormal working state factor of the wind turbine belonging to the wind farm according to the abnormal fluctuation index of the environment belonging to the wind farm; a control feedback module, which is used to control the wind turbine belonging to the wind farm according to the abnormal working state factor of the wind turbine belonging to the wind farm, collect and analyze the control process parameters of the wind turbine belonging to the wind farm through a data processing device, determine the calibration accuracy coefficient of the wind turbine belonging to the wind farm, and finally the control device provides feedback on the autonomous wind power control of the wind turbine belonging to the wind farm according to the calibration accuracy coefficient of the wind turbine belonging to the wind farm.

[0008] As a further solution, the determination of whether to correct the abnormal working state factor of the wind turbine belonging to the wind farm is specifically carried out as follows: the control device extracts the abnormal fluctuation threshold of the environment belonging to the wind farm from the wind turbine database, and compares the abnormal fluctuation index of the environment belonging to the wind farm with the abnormal fluctuation threshold of the environment belonging to the wind farm; if the abnormal fluctuation index of the environment belonging to the wind farm is greater than the abnormal fluctuation threshold of the environment belonging to the wind farm, it is determined that the abnormal working state factor of the wind turbine belonging to the wind farm is corrected; if the abnormal fluctuation index of the environment belonging to the wind farm is less than or equal to the abnormal fluctuation threshold of the environment belonging to the wind farm, it is determined that the abnormal working state factor of the wind turbine belonging to the wind farm is not corrected; the specific correction process of correcting the abnormal working state factor of the wind turbine belonging to the wind farm is as follows: the control device performs difference processing on the abnormal fluctuation index of the environment belonging to the wind farm and the abnormal fluctuation threshold of the environment belonging to the wind farm, obtains the over-limit value of the abnormal fluctuation index of the environment belonging to the wind farm, and matches the abnormal working state correction value from the wind turbine database, thereby correcting the abnormal working state factor of the wind turbine belonging to the wind farm.

[0009] As a further solution, the wind power autonomous control of the wind turbines belonging to the wind farm is carried out, and the specific control process is: comparing the abnormal working state factors of the wind turbines belonging to the wind farm with the first working state abnormal factor interval, the second working state abnormal factor interval and the third working state abnormal factor interval stored in the wind turbine database; if the abnormal working state factors of the wind turbines belonging to the wind farm belong to the first working state abnormal factor interval, the control device performs the autonomous wind power control through the first pitch control set corresponding to the first working state abnormal factor interval; if the abnormal working state factors of the wind turbines belonging to the wind farm belong to the second working state abnormal factor interval, the control device performs the autonomous wind power control through the second pitch control set corresponding to the second working state abnormal factor interval; if the abnormal working state factors of the wind turbines belonging to the wind farm belong to the third working state abnormal factor interval, the control device performs the autonomous wind power control through the third pitch control set corresponding to the third working state abnormal factor interval.

[0010] As a further solution, the feedback on the wind power autonomous control of the wind turbine belonging to the wind farm is performed, and the specific feedback process is: the control device extracts the calibration accuracy threshold of the wind turbine belonging to the wind farm from the wind turbine database, and compares the calibration accuracy coefficient of the wind turbine belonging to the wind farm with the calibration accuracy threshold of the wind turbine belonging to the wind farm; if the calibration accuracy coefficient of the wind turbine belonging to the wind farm is greater than the calibration accuracy threshold of the wind turbine belonging to the wind farm, then the wind power autonomous control of the wind turbine belonging to the wind farm is directly successfully fed back; if the calibration accuracy coefficient of the wind turbine belonging to the wind farm is less than or equal to the calibration accuracy threshold of the wind turbine belonging to the wind farm, then the pitch control set of the wind turbine belonging to the wind farm is updated, thereby updating the wind power autonomous control of the wind turbine belonging to the wind farm.

[0011] The second aspect of the present invention provides a control method for the autonomous and controllable wind power main control system for a wind farm, characterized in that it includes: step 1, collecting environmental parameters of the wind farm, and analyzing the abnormal fluctuation index of the environment belonging to the wind farm through a data processing device; step 2, obtaining and analyzing the operating parameters of the wind turbine belonging to the wind farm through a data processing device, evaluating the abnormal working state factor of the wind turbine belonging to the wind farm, and the control device determines whether to correct the abnormal working state factor of the wind turbine belonging to the wind farm according to the abnormal fluctuation index of the environment belonging to the wind farm; step 3, according to the abnormal working state factor of the wind turbine belonging to the wind farm, the control device performs autonomous wind power control on the wind turbine belonging to the wind farm, collects and analyzes the control process parameters of the wind turbine belonging to the wind farm through a data processing device, determines the calibration accuracy coefficient of the wind turbine belonging to the wind farm, and finally the control device provides feedback on the autonomous wind power control of the wind turbine belonging to the wind farm according to the calibration accuracy coefficient of the wind turbine belonging to the wind farm.

[0012] The third aspect of the present invention provides a device for using the autonomous and controllable wind power main control system for a wind farm as described above, characterized in that it includes: a data processing device and a control device; the data processing device is used to analyze the abnormal fluctuation index of the environment belonging to the wind farm, evaluate the abnormal fluctuation index of the environment belonging to the wind farm, and determine the calibration accuracy coefficient of the wind turbine belonging to the wind farm; the control device is used to determine whether to correct the abnormal working state factor of the wind turbine belonging to the wind farm according to the abnormal fluctuation index of the environment belonging to the wind farm, and at the same time, provide feedback on the wind power autonomous control of the wind turbine belonging to the wind farm according to the calibration accuracy coefficient of the wind turbine belonging to the wind farm.

[0013] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0014] (1) The present invention provides an autonomous and controllable wind power main control system, control method and device for a wind farm. By comprehensively collecting the environmental parameters and wind turbine operating parameters of the wind farm, the abnormal fluctuation index of the environment and the abnormal working state factor of the wind turbine can be accurately analyzed, thereby realizing real-time monitoring and evaluation of the wind farm operating state, improving the intelligent level of wind farm operation and maintenance, and correcting the abnormal working state factor of the wind turbine according to the abnormal environmental fluctuation index, so as to ensure that the wind turbine can maintain efficient operation in a complex and changeable environment. At the same time, the control process parameters of the wind turbine are collected and analyzed, and the calibration accuracy coefficient is accurately determined, so as to accurately feedback and adjust the wind power autonomous control. This autonomous and controllable wind power main control system realizes real-time monitoring, accurate evaluation and intelligent regulation of the wind farm operating state, effectively improves the operation efficiency and operation and maintenance efficiency of the wind farm, reduces the operation and maintenance cost, and provides a strong guarantee for the safe and stable operation of the wind farm.

[0015] (2) The present invention determines whether to correct the abnormal working state factor of the wind turbine belonging to the wind farm according to the abnormal fluctuation index of the environment belonging to the wind farm, thereby reducing the additional loss and unstable factors caused by frequent adjustments of the wind turbine, aiming to enable the wind turbine to better adapt to the current environmental conditions.

[0016] (3) The present invention provides feedback on the autonomous control of the wind turbines belonging to the wind farm according to the calibration accuracy coefficient of the wind turbines belonging to the wind farm. This feedback mechanism can continuously optimize the control strategy of the wind turbines, improve the operating efficiency and stability of the wind turbines, and make the autonomous control of the wind turbines more accurate and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0018] Figure 1 It is a schematic diagram of system module connection of the present invention;

[0019] Figure 2 It is a schematic diagram of the method steps of the present invention;

[0020] Figure 3 It is a wind speed variation curve of the environment of the wind farm involved in the present invention within the environment detection period.

[0021] Figure numerals: 1. Each data sample point. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0023] Reference Figure 1 As shown, the first aspect of the present invention provides an autonomous and controllable wind power master control system for a wind farm, comprising: an environmental analysis module, an operation evaluation module and a control feedback module.

[0024] A first aspect of the present invention provides an autonomous and controllable wind power main control system for a wind farm, which also includes a wind turbine database, wherein the wind turbine database is used to store terrain complexity, defined wind speed fluctuation value, defined wind direction change times, influence value corresponding to terrain complexity, abnormal fluctuation threshold of the environment belonging to the wind farm, abnormal working state correction value, weight value corresponding to abnormal fluctuation index, influence value corresponding to operating efficiency, reference blade speed, blade vibration amplitude definition value, first working state abnormal factor interval, second working state abnormal factor interval, third working state abnormal factor interval, first pitch control set, second pitch control set, third pitch control set, target average operating efficiency of wind turbines belonging to the wind farm in the end sub-period of control detection, influence value corresponding to angle adjustment deviation value, influence value corresponding to speed adjustment deviation value, influence value corresponding to operating efficiency adjustment deviation value, defined angle adjustment deviation value, defined speed adjustment deviation value, defined operating efficiency adjustment deviation value and calibration accuracy threshold of wind turbines belonging to the wind farm.

[0025] The environmental analysis module is connected to the operation evaluation module, the operation evaluation module is connected to the control feedback module, and the environmental analysis module, the operation evaluation module and the control feedback module are all connected to the wind turbine database.

[0026] The environmental analysis module is used to collect environmental parameters of the wind farm and analyze the abnormal fluctuation index of the environment of the wind farm through a data processing device.

[0027] Specifically, the abnormal fluctuation index of the environment of the wind farm is analyzed, and the specific analysis process is as follows:

[0028] The environmental parameters of the wind farm include the wind speed change curve of the environment belonging to the wind farm within the environmental detection cycle, the number of wind direction changes of the environment belonging to the wind farm within the environmental detection cycle, and the topography of the wind farm; the above-mentioned environmental detection cycle represents the time period for analyzing the environment belonging to the wind farm, and the specific duration is determined by the wind farm operation and maintenance manager; the above-mentioned wind speed change curve represents the change relationship between the wind speed and time of the wind farm within the environmental detection cycle, the wind farm's wind tower records the wind speed data in real time, and the wind speed of the wind farm's environment within the environmental detection cycle is transmitted to the matrix laboratory software for denoising, and the wind speed change curve is drawn after denoising; the above-mentioned number of wind direction changes represents the number of times the wind direction in the area where the wind farm is located changes within the environmental detection cycle, which can be extracted from the data recorded by the wind tower belonging to the wind farm; the above-mentioned topography of the wind farm represents the geomorphic features of the area where the wind farm is located, which can be extracted from the wind farm construction project report, and the geomorphic features may be flat terrain, complex terrain (such as hills, ridges, valleys, basins, etc.) or sea and land terrain, etc.

[0029] A number of data points are randomly located on the wind speed change curve of the environment of the wind farm within the environmental detection period and marked as data sample points, the wind speed of each data sample point is obtained, and standard deviation processing is performed, and the processing result is marked as the wind speed change fluctuation value of the environment of the wind farm within the environmental detection period; in an exemplary embodiment, the wind speed change curve of the environment of the wind farm within the environmental detection period is as follows Figure 3 As shown, it shows the changing relationship between the wind speed of a certain wind farm's environment and the environmental detection time point during the environmental detection cycle. The horizontal axis is the environmental detection time point, the unit is minute, and the vertical axis is the wind speed, the unit is meter per second. The wind speed of each data sample point 1 is obtained, and the standard deviation processing is performed to obtain the wind speed change fluctuation value of the wind farm's environment during the environmental detection cycle, wherein the wind speed change fluctuation value indicates the discrete degree of the wind speed of the wind farm's environment during the environmental detection cycle. The larger the wind speed change fluctuation value, the more drastic the change of the wind speed during the environmental detection cycle. The above-mentioned random positioning of several data points can use a random number generation algorithm to randomly select several data points on the time axis of the wind speed change curve, and these data points are the randomly positioned data sample points.

[0030] According to the terrain of the wind farm, the terrain complexity of the terrain of the wind farm is matched from the wind turbine database and marked as the terrain complexity of the wind farm; the terrain complexity corresponding to each terrain is stored in the wind turbine database, and the terrain complexity corresponding to the terrain of the wind farm can be obtained by directly querying the terrain of the wind farm in the wind turbine database, wherein the terrain complexity is an indicator used to quantify the complexity of the terrain characteristics of the wind farm. The landform features such as hills and valleys in the complex terrain will have a significant impact on the wind speed. For example, hills may accelerate the passing airflow, while valleys may slow down the wind speed. This acceleration and deceleration effect of the wind speed will cause large fluctuations in the wind speed inside the wind farm, thereby affecting the operating efficiency and power generation of the wind turbine. For another example, terrain obstacles (such as trees, buildings, etc.) in the complex terrain may cause the wind direction to deflect. This deflection increases the number of wind direction changes, which will not only affect the overall wind energy capture efficiency of the wind farm, but also may increase the lateral wind force and torque borne by the wind turbine.

[0031] The data processing device comprehensively analyzes the wind speed change fluctuation value of the wind farm's environment within the environmental detection period, the number of wind direction changes in the wind farm's environment within the environmental detection period, and the terrain complexity of the wind farm, and obtains the abnormal fluctuation index of the wind farm's environment. In this embodiment, the abnormal fluctuation index of the wind farm's environment is a numerical value indicating the degree of fluctuation of the wind farm's environment.

[0032] The abnormal fluctuation index of the environment of the wind farm is analyzed by the following specific method:

[0033]

[0034] Where AFW is the abnormal fluctuation index of the wind farm environment, EDE is the wind speed fluctuation value of the wind farm environment during the environmental detection period, CW is the number of wind direction changes of the wind farm environment during the environmental detection period, TC is the terrain complexity of the wind farm, and EDE is ΔJ is the wind speed fluctuation value preset in the wind turbine database, CW ΔJ is the number of wind direction changes preset in the wind turbine database, FJ is the impact value corresponding to the terrain complexity preset in the wind turbine database, and e is a natural constant.

[0035] The above-defined wind speed change fluctuation value indicates the maximum allowable value of the wind speed change fluctuation value of the environment of the wind farm within the environmental detection period; the above-defined number of wind direction changes indicates the maximum allowable number of wind direction changes of the environment of the wind farm within the environmental detection period; the above-defined impact value corresponding to the terrain complexity indicates the degree of influence of the unit value of the terrain complexity of the wind farm on the abnormal fluctuation index of the environment of the wind farm. The wind turbine database stores the correspondence between the terrain complexity and its corresponding impact value. For example, the terrain complexity of the wind farm is input into the wind turbine database, and the wind turbine database can match the impact value corresponding to the terrain complexity, and the value range is between 0 and 1.

[0036] It needs to be explained that changes in wind direction are also accompanied by fluctuations in wind speed. The pressure gradient force is the main driving force of wind speed. It is generated by the pressure difference between two locations and determines the direction of the wind. When the pressure gradient force changes, for example, due to the movement of a weather system or the influence of terrain, the wind direction will change accordingly. At the same time, the magnitude of the pressure gradient force also directly affects the wind speed. The greater the pressure difference, the greater the wind. Therefore, when the wind direction changes, it is often accompanied by changes in the pressure gradient force, which leads to fluctuations in wind speed. Therefore, when the number of wind direction changes is at a large level, the degree of fluctuation in wind speed will also increase. This drastic fluctuation in wind speed is also closely related to the complexity of the terrain, because complex terrain conditions (such as steep slopes, deep valleys, and obstacles) will disrupt the flow of airflow, resulting in uneven distribution of wind speed in space and time. This complexity and uncertainty will further aggravate the abnormal fluctuations in the operating environment of the wind farm. Therefore, by analyzing the above parameters, the degree of abnormal fluctuations in the environment of the wind farm can be accurately identified.

[0037] The operation evaluation module is used to obtain and analyze the operation parameters of the wind turbines belonging to the wind farm through a data processing device, and evaluate the abnormal working state factors of the wind turbines belonging to the wind farm. The control device determines whether to correct the abnormal working state factors of the wind turbines belonging to the wind farm according to the abnormal fluctuation index of the environment belonging to the wind farm.

[0038] In a specific embodiment, the present invention determines whether to correct the abnormal working state factor of the wind turbine belonging to the wind farm according to the abnormal fluctuation index of the environment belonging to the wind farm, thereby reducing the additional losses and unstable factors caused by frequent adjustments of the wind turbine, aiming to enable the wind turbine to better adapt to current environmental conditions.

[0039] Specifically, the wind farm belongs to the abnormal working state factor of the wind turbine, and the specific evaluation process is:

[0040] The operating parameters of the wind turbines belonging to the wind farm include the real-time output power of the wind turbines belonging to the wind farm during the first monitoring period, the real-time blade speed of the wind turbines belonging to the wind farm during the first monitoring period, and the real-time blade vibration amplitude of each detection position point of the wind turbines belonging to the wind farm during the first monitoring period; the above-mentioned first monitoring period represents the time period for analyzing the operating status of the wind turbines belonging to the wind farm, and the specific duration is determined by the wind farm operation and maintenance manager; the above-mentioned real-time output power represents the actual electric power generated by the wind turbine at each moment during the first monitoring period, which can be measured by the built-in power sensor of the wind turbine; the above-mentioned real-time blade speed represents the speed of the blades of the wind turbine at each moment during the first monitoring period, which can be measured by the built-in speed sensor of the wind turbine; the above-mentioned real-time blade vibration amplitude represents the vibration amplitude of each detection position point of the wind turbine belonging to the wind farm at each moment during the first monitoring period, which can be measured by the vibration sensor at each detection position point, wherein each detection position point refers to a position point of the wind turbine where a vibration sensor has been pre-installed.

[0041] The rated output power of the wind turbine belonging to the wind farm is obtained, and the real-time output power of the wind turbine belonging to the wind farm during the first monitoring period is ratio-processed with the rated output power of the wind turbine belonging to the wind farm, and the processing result is marked as the real-time operating efficiency of the wind turbine belonging to the wind farm during the first monitoring period; the rated output power of the wind turbine belonging to the wind farm indicates the maximum power that the wind turbine can stably output under specific conditions (such as rated wind speed, air density, atmospheric pressure and other standard operating conditions), which can be extracted from the manufacturing specification manual of the wind turbine belonging to the wind farm; the real-time operating efficiency is an indicator to measure the ability of the wind turbine to convert wind energy into electrical energy at each moment during the first monitoring period, and is usually expressed as the ratio of the actual output power of the wind turbine to the theoretical rated output power.

[0042] The real-time blade vibration amplitudes of each detection position point of the wind turbine belonging to the wind farm within the first monitoring period are averaged, and the processing result is marked as the real-time blade vibration amplitude of the wind turbine belonging to the wind farm within the first monitoring period, which represents the average level of the blade vibration amplitudes of each detection position point of the wind turbine at each moment in the first monitoring period.

[0043] The data processing device comprehensively analyzes the abnormal fluctuation index of the environment belonging to the wind farm, the real-time operating efficiency of the wind turbine belonging to the wind farm during the first monitoring period, the real-time blade speed of the wind turbine belonging to the wind farm during the first monitoring period, the real-time blade vibration amplitude of the wind turbine belonging to the wind farm during the first monitoring period, the abnormal fluctuation index of the environment belonging to the wind farm, the abnormal working state correction value, the weight value corresponding to the abnormal fluctuation index, the influence value corresponding to the operating efficiency, the reference blade speed, the blade vibration amplitude limit value and the abnormal fluctuation threshold of the environment belonging to the wind farm; in this embodiment, the real-time working state abnormality factor of the wind turbine belonging to the wind farm during the first monitoring period represents the numerical value of the abnormal degree of the working state of the wind turbine belonging to the wind farm at each moment in the first monitoring period.

[0044] The maximum value is located from the real-time abnormal working state factors of the wind turbines belonging to the wind farm in the first monitoring period, and is marked as the abnormal working state factor HUFM of the wind turbines belonging to the wind farm, that is, HUFM=MAX(HUF(zt)); It should be explained that the maximum value is located from the real-time abnormal working state factors of the wind turbines belonging to the wind farm in the first monitoring period, and is marked as the abnormal working state factor of the wind turbines belonging to the wind farm. This step lays the foundation for adjusting the most significant abnormalities, which can reduce the frequent response of the wind turbine to various subtle abnormalities and reduce the mechanical stress caused by frequent responses, thereby increasing the service life of the wind turbine. At the same time, the first monitoring period is short, which means that the changes in the operating state of the wind turbine can be captured in time, and adjustments are made according to the maximum value of the real-time abnormal working state factors. Over-adjustment caused by small anomalies accumulated over a long period of time can be avoided. Such over-adjustment may cause new mechanical stress or damage. Adjustment based on the maximum value can more accurately find the root cause of the problem and avoid unnecessary adjustments. By locating the maximum value, misjudgment and omission of subtle anomalies can be avoided. Misjudgment may lead to unnecessary operation and maintenance costs and time consumption, while omission may cause more serious failures. Adjustment based on the maximum value can ensure the accuracy and effectiveness of operation and maintenance work and reduce the risk of misjudgment and omission. Frequent adjustments may have a negative impact on the operating stability of wind turbines, while adjustment based on the maximum value can effectively solve the most significant abnormal problems while ensuring system stability. This adjustment method can reduce the fluctuations and instability factors in the operation of wind turbines caused by frequent adjustments.

[0045] Specifically, the real-time abnormal working state factor of the wind turbine in the wind farm in the first monitoring cycle is evaluated by the following specific method:

[0046]

[0047] Wherein, HUF(zt) is the abnormal working state factor of the wind turbine belonging to the wind farm at the time zt in the first monitoring period, FG is the abnormal working state correction value, AFW is the abnormal fluctuation index of the environment belonging to the wind farm, SR is the weight value corresponding to the abnormal fluctuation index preset in the wind turbine database, SWT(zt) is the operating efficiency of the wind turbine belonging to the wind farm at the time zt in the first monitoring period, RS(zt) is the blade speed of the wind turbine belonging to the wind farm at the time zt in the first monitoring period, WT(zt) is the blade vibration amplitude of the wind turbine belonging to the wind farm at the time zt in the first monitoring period, qr is the influence value corresponding to the operating efficiency preset in the wind turbine database, ΔRS is the reference blade speed preset in the wind turbine database, WT ΔJ is the blade vibration amplitude limit value preset in the wind turbine database, e is a natural constant, AFW Y is the abnormal fluctuation threshold of the environment of the wind farm, zt is any time point in the first monitoring period, zt∈[zt1,zt2], zt1 is the starting time point of the first monitoring period, and zt2 is the ending time point of the second monitoring period.

[0048] The weight value corresponding to the above abnormal fluctuation index represents the proportion of the abnormal fluctuation index of the environment of the wind farm to the abnormal factor of the working state. The corresponding relationship between the abnormal fluctuation index and its corresponding weight value is stored in the wind turbine database. For example, the abnormal fluctuation index of the environment of the wind farm is input into the wind turbine database, and the wind turbine database can match the weight value corresponding to the abnormal fluctuation index, and the value range is between 0 and 1; the influence value corresponding to the above operating efficiency represents the influence degree of the unit value of the operating efficiency on the abnormal factor of the working state. The corresponding relationship between the operating efficiency and its corresponding influence value is stored in the wind turbine database. For example, the operating efficiency is input into the wind turbine database, and the wind turbine database can match the operating efficiency. The influence value corresponding to the rate ranges from 0 to 1; the above-mentioned reference blade speed represents the reference value of the real-time blade speed of the wind turbine belonging to the wind farm within the first monitoring period. It should be explained that the reference blade speed is obtained by matching the abnormal fluctuation index of the environment belonging to the wind farm. The reference blade speeds corresponding to each abnormal fluctuation index interval are stored in the wind turbine database. The abnormal fluctuation index interval to which the abnormal fluctuation index of the environment belonging to the wind farm belongs is queried, and the reference blade speed corresponding to the abnormal fluctuation index interval is the reference blade speed in this embodiment; the above-mentioned blade vibration amplitude limit value represents the maximum allowable value of the real-time blade vibration amplitude of the wind turbine belonging to the wind farm within the first monitoring period.

[0049] It needs to be explained that the abnormal fluctuation index of the environment reflects the stability and degree of change of the environmental conditions of the wind farm. When the abnormal fluctuation index is high, it means that the environmental conditions are relatively unstable, which may lead to increased fluctuations in the wind turbine blade speed and operating efficiency. Specifically, drastic fluctuations in wind speed and direction will directly affect the wind turbine blade speed, and thus affect the operating efficiency. When the blade speed deviates significantly from the reference blade speed, the operating efficiency may decrease due to mechanical wear or aerodynamic effects. Long-term speed abnormalities may also aggravate the mechanical wear of the wind turbine, because components such as blades and bearings need to constantly adapt to such changes, which in turn causes the blade vibration amplitude to be at a higher level. Therefore, by monitoring the above parameters, the degree of adaptability of the wind turbine to the environment and abnormal working conditions can be identified, reducing the one-sidedness of single-dimensional analysis.

[0050] Specifically, the determination of whether to correct the abnormal working state factor of the wind turbine belonging to the wind farm is carried out in the following specific determination process:

[0051] The control device extracts the abnormal fluctuation threshold of the wind farm environment from the wind turbine database, and compares the abnormal fluctuation index of the wind farm environment with the abnormal fluctuation threshold of the wind farm environment; the abnormal fluctuation threshold of the wind farm environment represents the maximum value of the reasonable range of the abnormal fluctuation index of the wind farm environment.

[0052] If the abnormal fluctuation index of the environment belonging to the wind farm is greater than the abnormal fluctuation threshold of the environment belonging to the wind farm, it is determined that the abnormal working state factor of the wind turbine belonging to the wind farm is corrected; if the abnormal fluctuation index of the environment belonging to the wind farm is less than or equal to the abnormal fluctuation threshold of the environment belonging to the wind farm, it is determined that the abnormal working state factor of the wind turbine belonging to the wind farm is not corrected; the abnormal working state factor of the wind turbine belonging to the wind farm is corrected, and the specific correction process is: the control device performs difference processing on the abnormal fluctuation index of the environment belonging to the wind farm and the abnormal fluctuation threshold of the environment belonging to the wind farm, and obtains the over-limit value of the abnormal fluctuation index of the environment belonging to the wind farm, and matches the abnormal working state correction value from the wind turbine database, thereby correcting the abnormal working state factor of the wind turbine belonging to the wind farm. correction; the above-mentioned abnormal fluctuation index exceeding the limit value of the environment belonging to the wind farm indicates the difference between the abnormal fluctuation index of the environment belonging to the wind farm and the abnormal fluctuation threshold value of the environment belonging to the wind farm; the above-mentioned working state abnormal correction value indicates the numerical value of the correction degree of the real-time working state abnormal factor of the wind turbine belonging to the wind farm within the first monitoring period, and the value range is between 0 and 1, wherein the working state abnormal correction value, the specific matching process is: the working state abnormal correction value corresponding to each abnormal fluctuation index exceeding the limit value interval is stored in the wind turbine database, and the abnormal fluctuation index exceeding the limit value interval of the abnormal fluctuation index of the environment belonging to the wind farm is queried, and the working state abnormal correction value corresponding to the interval is the working state abnormal correction value matched by the abnormal fluctuation index exceeding the limit value of the environment belonging to the wind farm.

[0053] It needs to be explained that when the abnormal fluctuation index of the environment belonging to the wind farm is greater than the preset abnormal fluctuation threshold, this usually means that the current environmental conditions have changed significantly, and these changes may directly or indirectly affect the working state of the wind turbine. In this case, the abnormal working state factor of the wind turbine will often show a large value, reflecting the difficulties or shortcomings that the wind turbine may have in coping with such environmental changes. However, when adjusting the abnormal working state factor of the wind turbine, this embodiment is based on the maximum value of the real-time abnormal working state factor. Although this approach can quickly locate and solve the most significant problems, it also has certain risks. If the maximum value is too large, directly adjusting according to this value may cause the adjusted parameters to exceed the working capacity of the wind turbine, thereby causing damage to the wind turbine or affecting its normal operation. In order to avoid this situation, it is necessary to correct the abnormal working state factor of the wind turbine belonging to the wind farm so that the control process will not cause too much burden on the wind turbine.

[0054] The control feedback module is used for controlling the wind turbines belonging to the wind farm to perform autonomous wind power control on the wind turbines belonging to the wind farm according to the abnormal working state factors of the wind turbines belonging to the wind farm, collecting and analyzing the control process parameters of the wind turbines belonging to the wind farm through the data processing device, determining the calibration accuracy coefficient of the wind turbines belonging to the wind farm, and finally the control device provides feedback on the autonomous wind power control of the wind turbines belonging to the wind farm according to the calibration accuracy coefficient of the wind turbines belonging to the wind farm.

[0055] Furthermore, the autonomous wind power control of the wind turbines belonging to the wind farm is specifically carried out in the following process: comparing the abnormal working state factors of the wind turbines belonging to the wind farm with the first abnormal working state factor interval, the second abnormal working state factor interval and the third abnormal working state factor interval stored in the wind turbine database; if the abnormal working state factors of the wind turbines belonging to the wind farm belong to the first abnormal working state factor interval, the control device performs autonomous wind power control through the first pitch control set corresponding to the first abnormal working state factor interval; if the abnormal working state factors of the wind turbines belonging to the wind farm belong to the second abnormal working state factor interval, the control device performs autonomous wind power control through the second pitch control set corresponding to the second abnormal working state factor interval; if the abnormal working state factors of the wind turbines belonging to the wind farm belong to the third abnormal working state factor interval, the control device performs autonomous wind power control through the third abnormal working state factor interval. The third pitch control set corresponding to the abnormal working state factor interval performs autonomous wind power control; in an example embodiment, assuming that the abnormal working state factor of the wind turbine belonging to the wind farm is H, which belongs to the second abnormal working state factor interval [H-16%, H+24%], the second pitch control set corresponding to the interval includes: performing difference processing on the rated operating efficiency of the wind turbine (extracted from the manufacturing specification manual of the wind turbine belonging to the wind farm) and the operating efficiency corresponding to the abnormal working state factor of the wind turbine belonging to the wind farm, performing ratio processing on the processing result and the rated operating efficiency of the wind turbine, and finally obtaining the operating efficiency deviation ratio of the wind turbine belonging to the wind farm. Assuming that the operating efficiency deviation ratio of the wind turbine belonging to the wind farm in this example embodiment is 23%, the target blade angle of the wind turbine belonging to the wind farm at the end time point of the control detection cycle is adjusted to the angle below the end time point of the first monitoring cycle. times, and control the target average speed of the wind turbines in the wind farm at the end of the control detection period to the current speed. times, whereby the control device performs autonomous wind power control through the second pitch control set.

[0056] In a specific embodiment, the present invention provides feedback on the autonomous control of wind turbines belonging to the wind farm according to the calibration accuracy coefficient of the wind turbines belonging to the wind farm. This feedback mechanism can continuously optimize the control strategy of the wind turbines, improve the operating efficiency and stability of the wind turbines, and make the autonomous control of the wind turbines more accurate and efficient.

[0057] Specifically, the wind power autonomous control of the wind turbines in the wind farm is fed back, and the specific feedback process is as follows:

[0058] The control device extracts the calibration accuracy threshold of the wind turbine belonging to the wind farm from the wind turbine database, and compares the calibration accuracy coefficient of the wind turbine belonging to the wind farm with the calibration accuracy threshold of the wind turbine belonging to the wind farm. If the calibration accuracy coefficient of the wind turbine belonging to the wind farm is greater than the calibration accuracy threshold of the wind turbine belonging to the wind farm, the wind power autonomous control of the wind turbine belonging to the wind farm is directly fed back successfully.

[0059] If the calibration accuracy coefficient of the wind turbine belonging to the wind farm is less than or equal to the calibration accuracy threshold of the wind turbine belonging to the wind farm, the pitch control set of the wind turbine belonging to the wind farm is updated, thereby updating and feeding back the wind power autonomous control of the wind turbine belonging to the wind farm; the calibration accuracy threshold of the wind turbine belonging to the wind farm represents the minimum value of the reasonable range of the calibration accuracy coefficient of the wind turbine belonging to the wind farm; the direct successful feedback of the wind power autonomous control of the wind turbine belonging to the wind farm refers to generating a control success log feedback and sending it to the wind farm operation and maintenance administrator in the form of email or message; in an example embodiment, the updating of the pitch control set of the wind turbine belonging to the wind farm, thereby updating and feeding back the wind power autonomous control of the wind turbine belonging to the wind farm, the specific process is: assuming that the calibration accuracy threshold of the wind turbine belonging to the wind farm is subtracted from the calibration accuracy of the wind turbine belonging to the wind farm The difference in the degree coefficient is 85%. The second pitch control set before updating: Assuming that the abnormal working state factor of the wind turbine belonging to the wind farm is H, which belongs to the second abnormal working state factor interval [H-16%, H+24%], the second pitch control set corresponding to this interval includes: performing difference processing on the rated operating efficiency of the wind turbine (extracted from the manufacturing specification manual of the wind turbine belonging to the wind farm) and the operating efficiency corresponding to the abnormal working state factor of the wind turbine belonging to the wind farm, performing ratio processing on the processing result and the rated operating efficiency of the wind turbine, and finally obtaining the operating efficiency deviation ratio of the wind turbine belonging to the wind farm. Assuming that the operating efficiency deviation ratio of the wind turbine belonging to the wind farm in this example embodiment is 23%, the target blade angle of the wind turbine belonging to the wind farm at the end time point of the control detection cycle is adjusted to the angle below the end time point of the first monitoring cycle. times, and control the target average speed of the wind turbines in the wind farm at the end of the control detection period to the current speed. times, whereby the control device performs autonomous wind power control through the second pitch control set; the updated second pitch control set: the rated operating efficiency of the wind turbine (extracted from the manufacturing specification manual of the wind turbine belonging to the wind farm) and the operating efficiency corresponding to the abnormal working state factor of the wind turbine belonging to the wind farm are subjected to difference processing, and the processing result is subjected to ratio processing with the rated operating efficiency of the wind turbine, and finally the operating efficiency deviation ratio of the wind turbine belonging to the wind farm is obtained. Assuming that the operating efficiency deviation ratio of the wind turbine belonging to the wind farm in this example embodiment is 23%, the target blade angle of the wind turbine belonging to the wind farm at the end time point of the control detection cycle is adjusted to the angle at the end time point of the first monitoring cycle. times, and control the target average speed of the wind turbines in the wind farm at the end of the control detection period to the current speed. times, and stores the updated second pitch control set in the wind turbine database and feeds it back to the wind farm operation and maintenance administrator in the form of a log.

[0060] Furthermore, the calibration accuracy coefficient of the wind turbine belonging to the wind farm is determined, and the specific determination process is: the abnormal fluctuation index of the environment belonging to the wind farm and the abnormal working state factor of the wind turbine belonging to the wind farm are accumulated, and the accumulated result is marked as the abnormal value of the wind turbine belonging to the wind farm, which represents the comprehensive quantitative value of the abnormal degree of the environment belonging to the wind farm and the abnormal degree of the working state of the wind turbine belonging to the wind farm. The abnormal fluctuation index of the environment belonging to the wind farm and the abnormal working state factor of the wind turbine belonging to the wind farm are both percentages, and therefore can be directly added.

[0061] The control process parameters of the wind turbines belonging to the wind farm include the blade angle of the wind turbines belonging to the wind farm at the end time of the control detection cycle, the average speed of the wind turbines belonging to the wind farm in the control detection end sub-cycle, and the average operating efficiency of the wind turbines belonging to the wind farm in the control detection end sub-cycle; the above-mentioned control detection cycle represents the time period for analyzing the wind power autonomous control results of the wind turbines belonging to the wind farm, and the above-mentioned control detection end sub-cycle represents the end time period of the control detection cycle, and the specific duration is determined by the wind farm operation and maintenance manager; the above-mentioned blade angle represents the angle between the blades of the wind turbine relative to the wind direction, which can be measured by an angle sensor; the above-mentioned average speed represents the average level of the wind rotor rotation speed of the wind turbines belonging to the wind farm in the control detection end sub-cycle, which can be measured by a speed sensor; the above-mentioned average operating efficiency of the wind turbines belonging to the wind farm in the control detection end sub-cycle represents the average level of the real-time operating efficiency of the wind turbines belonging to the wind farm in the control detection end sub-cycle, wherein the real-time operating efficiency of the wind turbines belonging to the wind farm in the control detection end sub-cycle is obtained in the same manner as the real-time operating efficiency of the wind turbines belonging to the wind farm in the first monitoring cycle.

[0062] The target blade angle of the wind turbine belonging to the wind farm at the end time point of the control detection cycle is obtained, and the difference and absolute value processing are performed in sequence with the blade angle of the wind turbine belonging to the wind farm at the end time point of the control detection cycle, and the processing result is marked as the angle adjustment deviation value of the wind turbine belonging to the wind farm; the target blade angle of the wind turbine belonging to the wind farm at the end time point of the control detection cycle can be extracted from the pitch control set, which represents the desired blade angle of the wind turbine belonging to the wind farm at the end time point of the control detection cycle. For example, the control device performs autonomous wind power control through the first pitch control set corresponding to the first working state abnormal factor interval, and the target blade angle of the wind turbine belonging to the wind farm at the end time point of the control detection cycle is extracted from the first pitch control set; the angle adjustment deviation value of the wind turbine belonging to the wind farm represents the difference between the target blade angle of the wind turbine belonging to the wind farm at the end time point of the control detection cycle and the blade angle of the wind turbine belonging to the wind farm at the end time point of the control detection cycle.

[0063] The target average speed of the wind turbines belonging to the wind farm in the control detection end sub-period is obtained, and the difference and absolute value processing are performed in sequence with the average speed of the wind turbines belonging to the wind farm in the control detection end sub-period, and the processing result is marked as the speed adjustment deviation value of the wind turbines belonging to the wind farm; the target average speed of the wind turbines belonging to the wind farm in the control detection end sub-period can be extracted from the pitch control set, which represents the expected average speed of the wind turbines belonging to the wind farm in the control detection end sub-period. For example, the control device performs autonomous wind power control through the first pitch control set corresponding to the first working state abnormal factor interval, then the target average speed of the wind turbines belonging to the wind farm at this location in the control detection end sub-period is extracted from the first pitch control set; the speed adjustment deviation value of the wind turbines belonging to the wind farm represents the difference between the target average speed of the wind turbines belonging to the wind farm in the control detection end sub-period and the average speed of the wind turbines belonging to the wind farm in the control detection end sub-period.

[0064] According to the abnormal values ​​of the wind turbines belonging to the wind farm, the target average operating efficiency of the wind turbines belonging to the wind farm in the sub-period at the end of the control detection is matched from the wind turbine database, and the difference processing is performed with the average operating efficiency of the wind turbines belonging to the wind farm in the sub-period at the end of the control detection, and the processing result is marked as the operating efficiency adjustment deviation value of the wind turbines belonging to the wind farm; the above-mentioned target average operating efficiency of the wind turbines belonging to the wind farm in the sub-period at the end of the control detection represents the expected average operating efficiency of the wind turbines belonging to the wind farm in the sub-period at the end of the control detection, and the specific matching rule is: the target average operating efficiency corresponding to each abnormal value interval is stored in the wind farm database, and the abnormal value interval to which the abnormal value of the wind turbines belonging to the wind farm belongs is queried, and the target average operating efficiency corresponding to the abnormal value interval is the target average operating efficiency of the wind turbines belonging to the wind farm in the sub-period at the end of the control detection.

[0065] The data processing device comprehensively analyzes the angle adjustment deviation value of the wind turbine belonging to the wind farm, the speed adjustment deviation value of the wind turbine belonging to the wind farm, and the operating efficiency adjustment deviation value of the wind turbine belonging to the wind farm, and obtains the calibration accuracy coefficient of the wind turbine belonging to the wind farm. In this embodiment, the calibration accuracy coefficient of the wind turbine belonging to the wind farm represents a numerical value of the control accuracy of the wind turbine belonging to the wind farm.

[0066] The wind farm belongs to the wind turbine calibration accuracy coefficient, the specific determination method is:

[0067]

[0068] Where, YZS is the calibration accuracy coefficient of the wind turbine belonging to the wind farm, ADB is the angle adjustment deviation value of the wind turbine belonging to the wind farm, RSA is the speed adjustment deviation value of the wind turbine belonging to the wind farm, PGE is the operation efficiency adjustment deviation value of the wind turbine belonging to the wind farm, jx1 is the influence value corresponding to the angle adjustment deviation value preset in the wind turbine database, jx2 is the influence value corresponding to the speed adjustment deviation value preset in the wind turbine database, jx3 is the influence value corresponding to the operation efficiency adjustment deviation value preset in the wind turbine database, π is a natural constant, generally taken as 3.1, e is a natural constant, generally taken as 2.7, ADB ΔJ Adjust the deviation value for the defined angle preset in the wind turbine database, RSA ΔJ RSA is the speed adjustment deviation value preset in the wind turbine database. ΔJ Adjust the deviation value for the defined operating efficiency preset in the wind turbine database.

[0069] The angle adjustment deviation value defined above indicates the maximum allowable value of the angle adjustment deviation value of the wind turbine belonging to the wind farm; the influence value corresponding to the speed adjustment deviation value indicates the maximum allowable value of the speed adjustment deviation value of the wind turbine belonging to the wind farm; the operating efficiency adjustment deviation value indicates the maximum allowable value of the operating efficiency adjustment deviation value of the wind turbine belonging to the wind farm; the influence value corresponding to the angle adjustment deviation value indicates the numerical value of the influence of the unit value of the angle adjustment deviation value of the wind turbine belonging to the wind farm on the calibration accuracy coefficient of the wind turbine belonging to the wind farm; the wind turbine database stores the corresponding relationship between the angle adjustment deviation value and its corresponding influence value. For example, the angle adjustment deviation value of the wind turbine belonging to the wind farm is input into the wind turbine database, and the wind turbine database can match the influence value corresponding to the angle adjustment deviation value, and the value range is between 0 and 1; the influence value corresponding to the speed adjustment deviation value indicates that the wind turbine belonging to the wind farm The numerical value of the influence of the unit value of the motor speed adjustment deviation value on the calibration accuracy coefficient of the wind turbine belonging to the wind farm, and the correspondence between the speed adjustment deviation value and its corresponding influence value is stored in the wind turbine database. For example, the speed adjustment deviation value of the wind turbine belonging to the wind farm is input into the wind turbine database, and the wind turbine database can match the influence value corresponding to the speed adjustment deviation value, and the value range is between 0 and 1; the influence value corresponding to the above-mentioned operating efficiency adjustment deviation value represents the numerical value of the influence of the unit value of the operating efficiency adjustment deviation value of the wind turbine belonging to the wind farm on the calibration accuracy coefficient of the wind turbine belonging to the wind farm. The wind turbine database stores the correspondence between the operating efficiency adjustment deviation value and its corresponding influence value. For example, the operating efficiency adjustment deviation value of the wind turbine belonging to the wind farm is input into the wind turbine database, and the wind turbine database can match the influence value corresponding to the operating efficiency adjustment deviation value, and the value range is between 0 and 1.

[0070] It needs to be explained that when the angle adjustment deviation of the wind turbine is large, this directly reveals that the pitch control set is insufficient in accurately adjusting the operation of the wind turbine, which may be due to the performance degradation of the wind turbine itself or the unreasonable setting of the pitch control set parameters. This significant deviation will profoundly affect the ability of the wind turbine to capture wind energy. Specifically, the adjustment deviation of the blade angle will significantly change the angle between the wind rotor and the wind direction. This change directly determines the wind force and torque generated by the wind turbine. The torque fluctuation will then be reflected in the speed of the wind turbine, causing the speed adjustment deviation to increase accordingly. The speed is a key indicator of the operating status of the wind turbine. It not only reflects the efficiency of the wind turbine in capturing wind energy, but also directly affects the stability of the output power. Therefore, when the speed adjustment deviation increases, the fluctuation of the wind turbine output power will also intensify, thereby significantly reducing the operating efficiency of the wind turbine and reducing the operating efficiency. The decrease in the efficiency, that is, the increase in the operating efficiency adjustment deviation value, is the result of the combined effect of the angle adjustment deviation and the speed adjustment deviation. It shows that during the actual operation of the wind turbine, there is a large gap between its energy conversion efficiency and the design expectation. This efficiency loss will not only reduce the overall power generation of the wind farm, but also increase the operation and maintenance cost, and have an adverse impact on the economic benefits of the wind farm. In summary, there is a close causal relationship between the angle adjustment deviation value, the speed adjustment deviation value and the operating efficiency adjustment deviation value of the wind turbine. The increase in the angle adjustment deviation directly leads to the fluctuation of the torque and speed, which in turn causes a significant decrease in the operating efficiency. Therefore, in order to maintain the efficient and stable operation of the wind turbine, it is necessary to strengthen the maintenance and calibration of the pitch control system to ensure that the blade angle can be accurately adjusted, thereby reducing the angle adjustment deviation value, thereby reducing the deviation of the speed and operating efficiency, and improving the overall operating efficiency of the wind turbine.

[0071] In a specific embodiment, the present invention provides an autonomous and controllable wind power main control system for a wind farm. By comprehensively collecting the environmental parameters and wind turbine operating parameters of the wind farm, the abnormal fluctuation index of the environment and the abnormal working state factor of the wind turbine can be accurately analyzed, thereby realizing real-time monitoring and evaluation of the operating state of the wind farm, improving the intelligence level of wind farm operation and maintenance, and correcting the abnormal working state factor of the wind turbine according to the abnormal environmental fluctuation index, so as to ensure that the wind turbine can maintain efficient operation in a complex and changeable environment. At the same time, the control process parameters of the wind turbine are collected and analyzed, and the calibration accuracy coefficient is accurately determined, so as to accurately feedback and adjust the autonomous control of wind power. This autonomous and controllable wind power main control system realizes real-time monitoring, accurate evaluation and intelligent regulation of the operating state of the wind farm, effectively improves the operating efficiency and operation and maintenance efficiency of the wind farm, reduces the operation and maintenance cost, and provides a strong guarantee for the safe and stable operation of the wind farm.

[0072] The second aspect of the present invention provides a control method for the autonomous and controllable wind power main control system for a wind farm, characterized in that it includes: step 1, collecting environmental parameters of the wind farm, and analyzing the abnormal fluctuation index of the environment belonging to the wind farm through a data processing device; step 2, obtaining and analyzing the operating parameters of the wind turbine belonging to the wind farm through a data processing device, evaluating the abnormal working state factor of the wind turbine belonging to the wind farm, and the control device determines whether to correct the abnormal working state factor of the wind turbine belonging to the wind farm according to the abnormal fluctuation index of the environment belonging to the wind farm; step 3, according to the abnormal working state factor of the wind turbine belonging to the wind farm, the control device performs autonomous wind power control on the wind turbine belonging to the wind farm, collects and analyzes the control process parameters of the wind turbine belonging to the wind farm through a data processing device, determines the calibration accuracy coefficient of the wind turbine belonging to the wind farm, and finally the control device provides feedback on the autonomous wind power control of the wind turbine belonging to the wind farm according to the calibration accuracy coefficient of the wind turbine belonging to the wind farm.

[0073] The third aspect of the present invention provides a device for using the autonomous and controllable wind power main control system for a wind farm as described above, characterized in that it includes: a data processing device and a control device; the data processing device is used to analyze the abnormal fluctuation index of the environment belonging to the wind farm, evaluate the abnormal fluctuation index of the environment belonging to the wind farm, and determine the calibration accuracy coefficient of the wind turbine belonging to the wind farm; the control device is used to determine whether to correct the abnormal working state factor of the wind turbine belonging to the wind farm according to the abnormal fluctuation index of the environment belonging to the wind farm, and at the same time, provide feedback on the wind power autonomous control of the wind turbine belonging to the wind farm according to the calibration accuracy coefficient of the wind turbine belonging to the wind farm.

[0074] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. An autonomous and controllable wind power master control system for a wind farm, characterized in that: include: The environmental analysis module is used to collect environmental parameters of the wind farm and analyze the abnormal fluctuation index of the environment of the wind farm through the data processing device; The operation evaluation module is used to obtain and analyze the operation parameters of the wind turbines belonging to the wind farm through the data processing device, evaluate the abnormal working state factors of the wind turbines belonging to the wind farm, and the control device determines whether to correct the abnormal working state factors of the wind turbines belonging to the wind farm according to the abnormal fluctuation index of the environment belonging to the wind farm; The control feedback module is used to control the wind turbines belonging to the wind farm according to the abnormal working state factors of the wind turbines belonging to the wind farm, collect and analyze the control process parameters of the wind turbines belonging to the wind farm through the data processing device, determine the calibration accuracy coefficient of the wind turbines belonging to the wind farm, and finally the control device provides feedback on the wind turbine autonomous control of the wind turbines belonging to the wind farm according to the calibration accuracy coefficient of the wind turbines belonging to the wind farm.

2. The autonomous and controllable wind power master control system for a wind farm according to claim 1, characterized in that: The abnormal fluctuation index of the environment of the wind farm is analyzed, and the specific analysis process is as follows: The environmental parameters of the wind farm include a wind speed change curve of the environment of the wind farm during an environmental detection period, the number of wind direction changes of the environment of the wind farm during an environmental detection period, and the terrain of the wind farm; Randomly locate a number of data points on the wind speed change curve of the environment of the wind farm within the environmental detection period, and mark them as data sample points, obtain the wind speed of each data sample point, and perform standard deviation processing, and the processing result is marked as the wind speed change fluctuation value of the environment of the wind farm within the environmental detection period; According to the terrain to which the wind farm belongs, the terrain complexity of the terrain to which the wind farm belongs is matched from the wind turbine database, and marked as the terrain complexity of the wind farm; The data processing device comprehensively analyzes the wind speed fluctuation value of the wind farm environment during the environmental detection period, the number of wind direction changes in the wind farm environment during the environmental detection period, and the terrain complexity of the wind farm, and obtains the abnormal fluctuation index of the wind farm environment.

3. The autonomous and controllable wind power master control system for a wind farm according to claim 1 is characterized in that: The specific determination process of whether to correct the abnormal working state factor of the wind turbine in the wind farm is as follows: The control device extracts the abnormal fluctuation threshold of the environment belonging to the wind farm from the wind turbine database, and compares the abnormal fluctuation index of the environment belonging to the wind farm with the abnormal fluctuation threshold of the environment belonging to the wind farm; If the abnormal fluctuation index of the environment belonging to the wind farm is greater than the abnormal fluctuation threshold of the environment belonging to the wind farm, it is determined that the abnormal factor of the working state of the wind turbine belonging to the wind farm is corrected; if the abnormal fluctuation index of the environment belonging to the wind farm is less than or equal to the abnormal fluctuation threshold of the environment belonging to the wind farm, it is determined that the abnormal factor of the working state of the wind turbine belonging to the wind farm is not corrected; The abnormal working state factor of the wind turbine belonging to the wind farm is corrected, and the specific correction process is: the control device performs difference processing on the abnormal fluctuation index of the environment belonging to the wind farm and the abnormal fluctuation threshold of the environment belonging to the wind farm, and obtains the over-limit value of the abnormal fluctuation index of the environment belonging to the wind farm, and matches the abnormal working state correction value from the wind turbine database, thereby correcting the abnormal working state factor of the wind turbine belonging to the wind farm.

4. The autonomous and controllable wind power master control system for a wind farm according to claim 3 is characterized in that: The wind farm belongs to the abnormal working state factor of the wind turbine, and the specific evaluation process is as follows: The operating parameters of the wind turbines belonging to the wind farm include the real-time output power of the wind turbines belonging to the wind farm in the first monitoring period, the real-time blade speed of the wind turbines belonging to the wind farm in the first monitoring period, and the real-time blade vibration amplitude of each detection position point of the wind turbines belonging to the wind farm in the first monitoring period; The rated output power of the wind turbine belonging to the wind farm is obtained, and the real-time output power of the wind turbine belonging to the wind farm in the first monitoring period is processed by ratio with the rated output power of the wind turbine belonging to the wind farm, and the processing result is marked as the real-time operating efficiency of the wind turbine belonging to the wind farm in the first monitoring period; Performing mean processing on the real-time blade vibration amplitudes of each detection position point of the wind turbine belonging to the wind farm within the first monitoring period, and marking the processing result as the real-time blade vibration amplitudes of the wind turbine belonging to the wind farm within the first monitoring period; The data processing device comprehensively analyzes the abnormal fluctuation index of the environment belonging to the wind farm, the real-time operating efficiency of the wind turbine belonging to the wind farm in the first monitoring period, the real-time blade speed of the wind turbine belonging to the wind farm in the first monitoring period, the real-time blade vibration amplitude of the wind turbine belonging to the wind farm in the first monitoring period, the abnormal fluctuation index of the environment belonging to the wind farm, the abnormal working state correction value, the weight value corresponding to the abnormal fluctuation index, the influence value corresponding to the operating efficiency, the reference blade speed, the blade vibration amplitude limit value and the abnormal fluctuation threshold of the environment belonging to the wind farm, and obtains the real-time working state abnormality factor of the wind turbine belonging to the wind farm in the first monitoring period; A maximum value is located from the real-time abnormal working state factors of the wind turbines belonging to the wind farm in the first monitoring period, and is marked as the abnormal working state factor of the wind turbines belonging to the wind farm.

5. The autonomous and controllable wind power master control system for a wind farm according to claim 4 is characterized in that: The wind farm belongs to the abnormal factor of the real-time working state of the wind turbine in the first monitoring cycle, and the specific evaluation method is: Wherein, HUF(zt) is the abnormal working state factor of the wind turbine belonging to the wind farm at the time zt in the first monitoring period, FG is the abnormal working state correction value, AFW is the abnormal fluctuation index of the environment belonging to the wind farm, SR is the weight value corresponding to the abnormal fluctuation index preset in the wind turbine database, SWT(zt) is the operating efficiency of the wind turbine belonging to the wind farm at the time zt in the first monitoring period, RS(zt) is the blade speed of the wind turbine belonging to the wind farm at the time zt in the first monitoring period, WT(zt) is the blade vibration amplitude of the wind turbine belonging to the wind farm at the time zt in the first monitoring period, qr is the influence value corresponding to the operating efficiency preset in the wind turbine database, ΔRS is the reference blade speed preset in the wind turbine database, WT ΔJ is the blade vibration amplitude limit value preset in the wind turbine database, e is a natural constant, AFW Y is the abnormal fluctuation threshold of the environment of the wind farm, zt is any time point in the first monitoring period, zt∈ [ zt1, zt 2] , zt1 is the start time point of the first monitoring cycle, and zt2 is the end time point of the second monitoring cycle.

6. The autonomous and controllable wind power master control system for a wind farm according to claim 1, characterized in that: The specific control process of autonomously controlling the wind turbines in the wind farm is as follows: Compare the abnormal working state factors of the wind turbines belonging to the wind farm with the first abnormal working state factor interval, the second abnormal working state factor interval and the third abnormal working state factor interval stored in the wind turbine database; If the abnormal working state factor of the wind turbine belonging to the wind farm belongs to the first abnormal working state factor interval, the control device performs autonomous wind power control through the first pitch control set corresponding to the first abnormal working state factor interval; If the abnormal working state factor of the wind turbine belonging to the wind farm belongs to the second abnormal working state factor interval, the control device performs autonomous wind power control through the second pitch control set corresponding to the second abnormal working state factor interval; If the abnormal working state factor of the wind turbine belonging to the wind farm belongs to the third abnormal working state factor interval, the control device performs autonomous wind power control through the third pitch control set corresponding to the third abnormal working state factor interval.

7. The autonomous and controllable wind power master control system for a wind farm according to claim 1, characterized in that: The calibration accuracy coefficient of the wind turbine belonging to the wind farm is determined by the following specific determination process: The abnormal fluctuation index of the environment belonging to the wind farm and the abnormal working state factor of the wind turbine belonging to the wind farm are accumulated, and the accumulated result is marked as the abnormal value of the wind turbine belonging to the wind farm; The control process parameters of the wind turbines belonging to the wind farm include the blade angle of the wind turbines belonging to the wind farm at the end time of the control detection cycle, the average speed of the wind turbines belonging to the wind farm at the end sub-cycle of the control detection, and the average operating efficiency of the wind turbines belonging to the wind farm at the end sub-cycle of the control detection; Obtain the target blade angle of the wind turbine belonging to the wind farm at the end time of the control detection cycle, and perform difference and absolute value processing on the blade angle of the wind turbine belonging to the wind farm at the end time of the control detection cycle in sequence, and mark the processing result as the angle adjustment deviation value of the wind turbine belonging to the wind farm; Obtain the target average speed of the wind turbine belonging to the wind farm in the sub-period at the end of the control detection, and perform difference and absolute value processing on the average speed of the wind turbine belonging to the wind farm in the sub-period at the end of the control detection in sequence, and mark the processing result as the speed adjustment deviation value of the wind turbine belonging to the wind farm; According to the abnormal value of the wind turbine belonging to the wind farm, the target average operating efficiency of the wind turbine belonging to the wind farm in the sub-period at the end of the control detection is matched from the wind turbine database, and the difference processing is performed with the average operating efficiency of the wind turbine belonging to the wind farm in the sub-period at the end of the control detection, and the processing result is marked as the operating efficiency adjustment deviation value of the wind turbine belonging to the wind farm; The data processing device comprehensively analyzes the angle adjustment deviation value of the wind turbine belonging to the wind farm, the speed adjustment deviation value of the wind turbine belonging to the wind farm, and the operation efficiency adjustment deviation value of the wind turbine belonging to the wind farm, and obtains the calibration accuracy coefficient of the wind turbine belonging to the wind farm.

8. The autonomous and controllable wind power master control system for a wind farm according to claim 1, characterized in that: The wind power autonomous control of the wind turbines in the wind farm is fed back, and the specific feedback process is as follows: The control device extracts the calibration accuracy threshold of the wind turbine belonging to the wind farm from the wind turbine database, and compares the calibration accuracy coefficient of the wind turbine belonging to the wind farm with the calibration accuracy threshold of the wind turbine belonging to the wind farm. If the calibration accuracy coefficient of the wind turbine belonging to the wind farm is greater than the calibration accuracy threshold of the wind turbine belonging to the wind farm, the wind power autonomous control of the wind turbine belonging to the wind farm is directly successfully fed back; If the calibration accuracy coefficient of the wind turbine belonging to the wind farm is less than or equal to the calibration accuracy threshold of the wind turbine belonging to the wind farm, the pitch control set of the wind turbine belonging to the wind farm is updated, thereby updating the wind power autonomous control of the wind turbine belonging to the wind farm.

9. A control method for the autonomous and controllable wind power master control system for a wind farm as claimed in any one of claims 1 to 8, characterized in that: include: Step 1: Collect environmental parameters of the wind farm and analyze the abnormal fluctuation index of the environment of the wind farm through a data processing device; Step 2: Obtain and analyze the operating parameters of the wind turbines belonging to the wind farm through a data processing device, evaluate the abnormal working state factors of the wind turbines belonging to the wind farm, and the control device determines whether to correct the abnormal working state factors of the wind turbines belonging to the wind farm according to the abnormal fluctuation index of the environment belonging to the wind farm; Step three, according to the abnormal working status factors of the wind turbines belonging to the wind farm, the control device performs autonomous wind power control on the wind turbines belonging to the wind farm, collects and analyzes the control process parameters of the wind turbines belonging to the wind farm through the data processing device, and determines the calibration accuracy coefficient of the wind turbines belonging to the wind farm. Finally, the control device provides feedback on the autonomous wind power control of the wind turbines belonging to the wind farm according to the calibration accuracy coefficient of the wind turbines belonging to the wind farm.

10. A device using the autonomous and controllable wind power master control system for a wind farm as claimed in any one of claims 1 to 8, characterized in that: include: Data processing device and control device; The data processing device is used to analyze the abnormal fluctuation index of the environment belonging to the wind farm, evaluate the abnormal fluctuation index of the environment belonging to the wind farm, and determine the calibration accuracy coefficient of the wind turbine belonging to the wind farm; The control device is used to determine whether to correct the abnormal working state factor of the wind turbine belonging to the wind farm according to the abnormal fluctuation index of the environment belonging to the wind farm, and at the same time provide feedback on the wind power autonomous control of the wind turbine belonging to the wind farm according to the calibration accuracy coefficient of the wind turbine belonging to the wind farm.

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