Wind power main control system based on a secure and trustworthy software and hardware platform

Abstract

The present invention discloses a wind power main control system based on a secure and trustworthy software and hardware platform, belonging to the technical field of wind power generation, including: a data acquisition module, a data analysis module, a monitoring result judgment module, an abnormal unit optimization control module, and a data backup module. The present invention collects the operation monitoring parameters of the wind turbine unit in real time through the data acquisition module, and obtains the operation monitoring evaluation value through the processing of the data analysis module, so as to accurately judge the operation state of the wind turbine unit, and further realizes the precise monitoring and timely early warning of the wind turbine unit, effectively solving the problems of insufficient safety performance and reliability in the existing wind power main control system, and improving the safe operation level and fault response ability of the wind farm.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and particularly to a wind power main control system based on a secure and trusted software and hardware platform. Background Art

[0002] With the rapid development of wind energy power generation, the requirements for the safety, reliability and intelligent level of the main control system of wind farms are increasing day by day. The wind power main control system refers to the core system responsible for monitoring and controlling the operation of wind turbines in the field of wind power.

[0003] The existing wind power main control system determines the operation mode of the wind turbine by monitoring the grid state (especially the grid frequency), and adjusts the output of the wind turbine accordingly to achieve active support for the grid or by optimizing the communication and data interaction between the internal controllers of the wind turbine.

[0004] For example, a control method and main control system for a voltage source type wind turbine disclosed in the invention patent announcement with the publication number of CN111594384B includes: determining the operation mode of the wind turbine based on whether the grid is in a large frequency disturbance condition; when the wind turbine operates in the current source operation mode, determining the generator torque output instruction sent to the converter and the operation mode of the wind turbine based on the generator torque instruction output by the variable speed and variable pitch control loop and the generator torque instruction required for the grid frequency active support control loop to output the support frequency; when the wind turbine operates in the voltage source operation mode, determining the generator torque output instruction sent to the converter and the operation mode of the wind turbine based on the generator torque instruction output by the variable speed and variable pitch control loop and the generator torque required for virtual synchronization.

[0005] For example, a control system for the integrated linkage of the central control of a wind farm and the main control of a wind turbine disclosed in the invention patent announcement with the publication number of CN114233572B includes: a tower base controller, a nacelle controller, a yaw frequency converter, a pitch frequency converter, a power converter, an anemometer and wind vane, a control panel, an internal optical fiber communication bus and a data acquisition server; the tower base controller and the nacelle controller achieve integrated control of the tower base controller and the nacelle controller through the internal optical fiber communication bus, and at the same time, the nacelle controller serves as a coupler to achieve multi-interface and protocol data interaction of the yaw frequency converter, the pitch frequency converter, the power converter and the anemometer and wind vane.

[0006] However, in the process of implementing the technical solutions of the present invention in the embodiments of the present application, it is found that the above technologies have at least the following technical problems:

[0007] The management and data storage methods of wind power main control systems in the prior art are generally single, failing to effectively classify and distinguish between normal data and abnormal data, ignoring the differences in data nature, and making it difficult to efficiently identify and handle potential risks and abnormal conditions. Therefore, the existing wind power main control systems have problems with insufficient safety performance and reliability. Summary of the Invention

[0008] By providing a wind power main control system based on a secure and trustworthy software and hardware platform, the embodiments of the present application solve the problems of insufficient safety performance and reliability in the existing wind power main control systems, and achieve precise monitoring and timely warning of wind turbines.

[0009] The embodiments of the present application provide a wind power main control system based on a secure and trustworthy software and hardware platform, including: a data acquisition module, configured to collect the operation monitoring parameters of a wind turbine in real time through an edge intelligent main control sensing hub connected by the secure and trustworthy software and hardware platform; a data analysis module, configured to analyze the operation monitoring parameters of the wind turbine to obtain an evaluation index of the deviation state of the wind turbine, and thereby process to obtain an operation monitoring category, where the operation monitoring category is: normal wind turbine, abnormal wind turbine, and faulty wind turbine; a monitoring result judgment module, configured to, when the operation monitoring category is a normal wind turbine, save the operation monitoring parameters of the wind turbine to the main control cloud platform, when the operation monitoring category is an abnormal wind turbine, record the wind turbine as an abnormal unit and perform optimization control, and when the operation monitoring category is a faulty wind turbine, give a warning and control the wind turbine to enter a safety protection mode; an abnormal unit optimization control module, configured to track and monitor the abnormal unit, analyze to obtain the optimization control execution parameters of the abnormal unit, and perform optimization control on the abnormal unit through the main control cloud platform; a data backup module, configured to count the operation monitoring parameters of the abnormal unit, obtain the parameters of each available backup storage location, thereby screen to obtain a suitable backup storage location, and transmit the operation monitoring parameters of the abnormal unit to the suitable backup storage location for backup storage.

[0010] Further, the operation monitoring parameters of the wind turbine are collected in real time through the edge intelligent main control sensing hub connected by the secure and trustworthy software and hardware platform; the operation monitoring parameters specifically include: the blade deviation angle, output power, main shaft speed, main shaft inclination, generator temperature, yaw angle, power factor, and oil level height of the wind turbine.

[0011] Further, the method for analyzing the operation monitoring parameters of the wind turbine to obtain the deviation state evaluation index of the wind turbine is as follows: Obtain the operation monitoring target set of the wind turbine, where the operation monitoring target set of the wind turbine includes: blade deviation angle target value, output power target value, main shaft rotation speed target value, main shaft inclination target value, generator temperature target value, yaw angle target value, power factor target value, and oil level height target value; Analyze the operation monitoring target set of the wind turbine and the operation monitoring parameters of the wind turbine to obtain the deviation state evaluation index of the wind turbine; The deviation state evaluation index of the wind turbine is used to characterize the degree of abnormal operation of the wind turbine.

[0012] Further, the method for obtaining the deviation state evaluation index of the wind turbine is as follows:

[0013]

[0014] In the formula, R represents the deviation state evaluation index of the wind turbine, e represents the natural constant, R1 represents the structural control evaluation value of the wind turbine, R2 represents the performance control evaluation value of the wind turbine, μ1 represents the influence weight of the structural control evaluation value, and μ2 represents the influence weight of the performance control evaluation value.

[0015] Further, the method for analyzing the operation monitoring parameters of the wind turbine to obtain the deviation state evaluation index of the wind turbine, and thus obtaining the operation monitoring category, is as follows: Obtain the first deviation state evaluation threshold of the wind turbine and the second deviation state evaluation threshold of the wind turbine preset in the database; Based on the deviation state evaluation index of the wind turbine, compare it with the first deviation state evaluation threshold of the wind turbine and the second deviation state evaluation threshold of the wind turbine. If the deviation state evaluation index of the wind turbine is above the first deviation state evaluation threshold of the wind turbine, the operation monitoring category is a wind turbine failure; If the deviation state evaluation index of the wind turbine is less than the first deviation state evaluation threshold of the wind turbine and above the second deviation state evaluation threshold of the wind turbine, the operation monitoring category is a wind turbine anomaly; If the deviation state evaluation index of the wind turbine is less than the second deviation state evaluation threshold of the wind turbine, the operation monitoring category is a normal wind turbine.

[0016] Furthermore, the tracking and monitoring of abnormal wind turbine units specifically include: obtaining real-time external environment parameters through the edge intelligent master control sensing hub connected by a secure and trusted software and hardware platform; obtaining the preset external environment target set in the database, where the external environment target set includes: the target value of external environment wind speed, the target value of external atmospheric pressure, the target value of external environment temperature, and the target value of external environment humidity; obtaining the environmental matching degree by comparing the real-time external environment parameters with the external environment target set; screening the deviation status evaluation index of abnormal wind turbine units based on the deviation status evaluation index of wind turbine units; obtaining the preset environmental target matching degree in the database, and performing fusion analysis on the environmental matching degree, the environmental target matching degree, the deviation status evaluation index of abnormal wind turbine units, and the first threshold of the deviation status of wind turbine units to obtain the tracking control reference index of abnormal wind turbine units; the real-time external environment parameters include: real-time external environment wind speed, real-time external atmospheric pressure, real-time external environment temperature, and real-time external environment humidity; the environmental matching degree is used to characterize the matching degree of the current operating environment of abnormal wind turbine units; the tracking control reference index of abnormal wind turbine units is used to characterize the execution complexity of optimizing the control of abnormal wind turbine units.

[0017] Furthermore, the analysis to obtain the optimized control execution parameters of abnormal wind turbine units specifically includes: obtaining the preset tracking control reference index intervals and the corresponding operation adjustment parameters in the database for each tracking control reference index interval, matching the tracking control reference index of abnormal wind turbine units with each tracking control reference index interval, and if the tracking control reference index of abnormal wind turbine units is within a certain tracking control reference index interval, obtaining the operation adjustment parameter corresponding to that tracking control reference index interval as the optimized control execution parameter of abnormal wind turbine units.

[0018] Furthermore, the parameters of each currently available backup storage location specifically include: the remaining space capacity, the remaining space capacity ratio, the disk read / write speed, the average time between disk failures, and the average disk failure repair time of each backup storage location.

[0019] Furthermore, the screening to obtain a suitable backup storage location and transmitting the operation monitoring parameters of abnormal wind turbine units to the suitable backup storage location for backup storage specifically includes: processing the parameters of each backup storage location to obtain the backup storage effect evaluation value of each backup storage location, and thus screening to obtain the best backup storage location; the backup storage effect evaluation value of each backup storage location is used to characterize the backup storage effect of each. Based on the backup storage effect evaluation values of each backup storage location, obtaining the backup storage location corresponding to the maximum backup storage effect evaluation value as the suitable backup storage location, and backing up the operation monitoring parameters of abnormal wind turbine units in the suitable backup storage location.

[0020] Further, it further includes a data security and encryption module, which is used to use an encryption algorithm to perform data transmission on the operation monitoring parameters of the wind turbine stored in the main control cloud platform and the operation monitoring parameters of the abnormal units in the data backup module, and store them separately in the main control cloud platform and the data backup module, and set access permission management.

[0021] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0022] 1. The wind power main control system based on a secure and trusted software and hardware platform provided by the present invention can collect the operation monitoring parameters of the wind turbine in real time through the data acquisition module, and obtain the operation monitoring evaluation value through the data analysis module, so as to accurately judge the operation state of the wind turbine, and then realize the precise monitoring and timely warning of the wind turbine, effectively solving the problems of insufficient safety performance and reliability in the existing wind power main control system, and improving the safe operation level and fault response ability of the wind farm.

[0023] 2. The present invention uses the abnormal unit optimization control module to track and monitor the abnormal unit, and based on the real-time external environment parameters and the preset external environment target set, analyzes and obtains the abnormal unit optimization control execution parameters, so as to realize the intelligent optimization control of the abnormal unit, and improve the power generation efficiency and energy utilization rate of the wind farm.

[0024] 3. The present invention sets up a data security and encryption module to perform data encryption transmission on the operation monitoring parameters of the wind turbine stored in the main control cloud platform and the data backup module, and set access permission management, so as to ensure the security and confidentiality of the data, and then prevent the risk of data leakage and illegal access, and further provide a reliable guarantee for the data security of the wind farm. Description of the Drawings

[0025] Figure 1 It is a schematic structural diagram of the wind power main control system based on a secure and trusted software and hardware platform provided by the embodiments of the present application. Detailed Embodiments

[0026] Embodiments of the present application provide a wind power main control system based on a secure and trusted software and hardware platform, which solves the problem of insufficient safety performance and reliability in the existing wind power main control system. The system includes a data acquisition module for real-time collecting the operation monitoring parameters of a wind turbine through an edge intelligent main control sensing hub connected by a secure and trusted software and hardware platform; a data analysis module for analyzing the operation monitoring parameters of the wind turbine to obtain an evaluation index of the deviation state of the wind turbine, and thus obtaining an operation monitoring category, where the operation monitoring category includes: normal operation of the wind turbine, abnormal operation of the wind turbine, and failure of the wind turbine; a monitoring result judgment module for saving the operation monitoring parameters of the wind turbine to the main control cloud platform when the operation monitoring category is normal operation of the wind turbine, recording the wind turbine as an abnormal unit and performing optimization control when the operation monitoring category is abnormal operation of the wind turbine, giving an early warning and controlling the wind turbine to enter a safety protection mode when the operation monitoring category is failure of the wind turbine; an abnormal unit optimization control module for tracking and monitoring the abnormal unit, analyzing to obtain the execution parameters for optimizing the control of the abnormal unit, and performing optimization control on the abnormal unit through the main control cloud platform; and a data backup module for counting the operation monitoring parameters of the abnormal unit, obtaining the parameters of each available backup storage location, screening to obtain a suitable backup storage location, and transmitting the operation monitoring parameters of the abnormal unit to the suitable backup storage location for backup storage, achieving precise monitoring and timely early warning of the wind turbine.

[0027] The technical solution in the embodiments of the present application is to solve the problem of insufficient safety performance and reliability in the above-mentioned wind power main control system. The general idea is as follows: The system includes a data acquisition module for real-time collecting the operation monitoring parameters of a wind turbine through an edge intelligent main control sensing hub connected by a secure and trusted software and hardware platform; a data analysis module for analyzing the operation monitoring parameters of the wind turbine to obtain an evaluation index of the deviation state of the wind turbine, and thus obtaining an operation monitoring category, where the operation monitoring category includes: normal operation of the wind turbine, abnormal operation of the wind turbine, and failure of the wind turbine; a monitoring result judgment module for saving the operation monitoring parameters of the wind turbine to the main control cloud platform when the operation monitoring category is normal operation of the wind turbine, recording the wind turbine as an abnormal unit and performing optimization control when the operation monitoring category is abnormal operation of the wind turbine, giving an early warning and controlling the wind turbine to enter a safety protection mode when the operation monitoring category is failure of the wind turbine; an abnormal unit optimization control module for tracking and monitoring the abnormal unit, analyzing to obtain the execution parameters for optimizing the control of the abnormal unit, and performing optimization control on the abnormal unit through the main control cloud platform; and a data backup module for counting the operation monitoring parameters of the abnormal unit, obtaining the parameters of each available backup storage location, screening to obtain a suitable backup storage location, and transmitting the operation monitoring parameters of the abnormal unit to the suitable backup storage location for backup storage, achieving precise monitoring and timely early warning of the wind turbine.

[0028] To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0029] As Figure 1 shown, it is a schematic structural diagram of a wind power main control system based on a secure and trustworthy software and hardware platform provided by an embodiment of the present application. The wind power main control system based on a secure and trustworthy software and hardware platform provided by an embodiment of the present application includes: a data acquisition module, configured to collect operation monitoring parameters of a wind turbine in real time through an edge intelligent main control sensing hub connected by a secure and trustworthy software and hardware platform; a data analysis module, configured to analyze the operation monitoring parameters of the wind turbine to obtain a deviation state evaluation index of the wind turbine, and thereby process to obtain an operation monitoring category, and the operation monitoring category is: the wind turbine is normal, the wind turbine is abnormal, and the wind turbine has a fault; a monitoring result judgment module, configured to, when the operation monitoring category is that the wind turbine is normal, save the operation monitoring parameters of the wind turbine to the main control cloud platform, when the operation monitoring category is that the wind turbine is abnormal, record the wind turbine as an abnormal unit and perform optimization control, and when the operation monitoring category is that the wind turbine has a fault, give an early warning and control the wind turbine to enter a safety protection mode; an abnormal unit optimization control module, configured to perform tracking monitoring on the abnormal unit, analyze to obtain abnormal unit optimization control execution parameters, and perform optimization control on the abnormal unit through the main control cloud platform; a data backup module, configured to count the operation monitoring parameters of the abnormal unit, obtain parameters of each currently available backup storage location, thereby perform screening to obtain a suitable backup storage location, and transmit the operation monitoring parameters of the abnormal unit to the suitable backup storage location for backup storage.

[0030] In this embodiment, through a secure and trustworthy software and hardware platform, it is closely connected to the edge intelligent main control sensing hub, and can collect various operation monitoring parameters of the wind turbine in real time and accurately. By deeply analyzing the collected operation monitoring parameters, evaluating the operation state of the unit, and classifying the unit into three categories of normal, abnormal, or faulty accordingly, it is beneficial to timely discover potential problems and provide strong support for subsequent monitoring and control. Through the monitoring result judgment module, through intelligent monitoring and judgment, it can ensure that the unit operates in a safe and stable state and effectively reduce the probability of accidents. Through the optimization control by the abnormal unit optimization control module, it can maximize the operation efficiency and reliability of the unit and reduce the maintenance cost. The data backup module provides strong guarantee for data security, and can ensure the integrity and availability of data even in extreme cases.

[0031] It should be noted that the safety protection mode is an important emergency response mechanism. When serious faults occur in the wind turbine generator set, a series of preset protection measures are automatically taken to prevent the further deterioration of the faults, ensure personnel safety, reduce equipment damage, and maintain the overall stable operation of the wind farm. These measures include: stopping the operation of all rotating components (such as blades, generators, etc.) of the wind turbine generator set, automatically isolating the faulty unit from the power grid connection to prevent the fault current from impacting the power grid, and at the same time protecting the faulty unit from the influence of abnormal power grid conditions, providing a safe electrical environment for subsequent maintenance work, locking the remote and local operation interfaces of the wind turbine generator set, only allowing maintenance personnel with specific permissions to intervene through specific safety channels, and detailed recording of various parameter changes before and after the fault occurs, including fault time, fault code, relevant monitoring parameters, etc. Automatically sending fault alarm information to the preset maintenance team or management personnel, including fault type and location.

[0032] Furthermore, the edge intelligent master control sensing hub connected through a secure and trustworthy software and hardware platform continuously collects the operation monitoring parameters of the wind turbine generator set; the operation monitoring parameters specifically include: the blade deviation angle, output power, main shaft speed, main shaft inclination, generator temperature, yaw angle, power factor, and oil level height of the wind turbine generator set.

[0033] In this embodiment, the edge intelligent master control sensing hub connected through a secure and trustworthy software and hardware platform continuously collects the operation monitoring parameters of the wind turbine generator set. The edge intelligent master control sensing hub includes connections to devices such as angle sensors, power meters, speed sensors, inclination sensors, acceleration sensors, gyroscopes, temperature sensors, yaw motors, oil level sensors, and power quality analyzers, etc. The angle sensor installed on the blade of the wind turbine generator set is used to continuously monitor the blade deviation angle in real time, the output power is obtained through power measurement instruments (such as power meters or power sensors), the main shaft speed is obtained through the speed sensor, the main shaft inclination is obtained through the inclination sensor (such as an acceleration sensor or a gyroscope), the generator temperature is obtained through the temperature sensor, the yaw angle of the wind turbine generator set is obtained through the yaw system (such as a yaw motor), the power factor is obtained through power monitoring devices (such as power quality analyzers), and the oil level height is obtained through the oil level sensor.

[0034] Furthermore, the deviation state evaluation index of the wind turbine is obtained by analyzing the operation monitoring parameters of the wind turbine set, and the specific method is: obtaining the operation monitoring target set of the wind turbine set, the operation monitoring target set of the wind turbine set including: blade deviation angle target value, output power target value, main shaft speed target value, main shaft inclination target value, generator temperature target value, yaw angle target value, power factor target value and oil level height target value; analyzing the operation monitoring target set of the wind turbine set with the operation monitoring parameters of the wind turbine set to obtain the deviation state evaluation index of the wind turbine set; the deviation state evaluation index of the wind turbine set is used to characterize the degree of abnormal operation of the wind turbine set.

[0035] In this embodiment, the deviation state evaluation index of the wind turbine is obtained by obtaining the operation monitoring parameters of the wind turbine and analyzing them, taking into account the mutual influence between these parameters, for example: the blade deviation angle directly affects the ability of the wind wheel to capture wind energy. If the deviation angle is too large, the blades may block each other, resulting in energy loss; if the deviation angle is too small, the wind energy may not be fully captured. The output power is proportional to the main shaft speed and is regulated by the blade deviation angle. At the same time, the increase in output power will cause the temperature of the generator to rise, and equipment failure will also cause the temperature to rise, resulting in abnormal output power. The main shaft speed will lead to an increase in output power, but it may also cause the temperature of the generator to rise and mechanical wear to increase. The main shaft inclination will affect the stability and safety of the wind turbine. Excessive inclination will cause vibration and imbalance of the unit, and excessive generator temperature may cause equipment failure and performance degradation. The yaw angle determines the degree to which the wind turbine is facing the wind head-on, affecting the efficiency of wind energy capture. The optimized yaw angle can increase the output power and main shaft speed. The change in output power will cause a change in power factor. Too low an oil level will aggravate the increase in generator temperature and the wear of the main shaft. Too high an oil level will limit the heat dissipation effect of the oil and cause the equipment to overheat.

[0036] Furthermore, the deviation state evaluation index of the wind turbine is obtained, and the specific method is as follows:

[0037]

[0038] Where R represents the deviation state evaluation index of the wind turbine, e represents the natural constant, R1 represents the structural control evaluation value of the wind turbine, R2 represents the performance control evaluation value of the wind turbine, μ1 represents the influence weight of the structural control evaluation value, and μ2 represents the influence weight of the performance control evaluation value.

[0039] In this embodiment, the structural control evaluation value of the wind turbine generator set is obtained by:

[0040]

[0041] Wherein, R1 represents the structural control evaluation value of the wind turbine, θ1 represents the blade deviation angle of the wind turbine, Δθ1 represents the target value of the blade deviation angle, θ2 represents the spindle inclination of the wind turbine, Δθ2 represents the target value of the spindle inclination, θ3 represents the yaw angle of the wind turbine, Δθ3 represents the target value of the yaw angle, H1 represents the oil level height of the wind turbine, and ΔH1 represents the target value of the oil level height.

[0042] It should be noted that the blade deviation angle of the wind turbine refers to the angular deviation between the blade and the rotating shaft. If the blade deviation angle is too large, the blades may block each other, resulting in energy loss. Conversely, if the angle is too small, the wind turbine may not be able to fully capture wind energy. The yaw angle of the wind turbine refers to the angle between the axis of rotation of the wind turbine (or the plane of the wind wheel) and the wind direction. When the yaw angle is too large, the wind wheel cannot fully capture wind energy, resulting in a decrease in power generation efficiency. An overly large yaw angle will also increase the torque and stress on the wind wheel, affecting the operating stability and lifespan of the wind wheel.

[0043] The performance control evaluation value of the wind turbine is obtained. The specific method is as follows:

[0044]

[0045] Wherein, R2 represents the performance control evaluation value of the wind turbine, e represents the natural constant, P r represents the output power processing value, V r represents the spindle speed processing value, T r represents the generator temperature processing value, W r represents the power factor processing value.

[0046] It should be noted that the power factor processing value refers to the ratio of the active power to the apparent power in an AC circuit, and its value range is from 0 to 1. The generator temperature is not negative during operation. Wherein, P r represents the output power processing value, P1 represents the output power of the wind turbine, and ΔP1 represents the target value of the output power. Wherein, V r is the spindle speed processing value, V1 represents the spindle speed of the wind turbine, and ΔV1 represents the target value of the spindle speed. Wherein, T r represents the generator temperature processing value, T1 represents the generator temperature of the wind turbine, and ΔT1 represents the target value of the generator temperature. Wherein, W r represents the power factor processing value, W1 represents the power factor of the wind turbine, and ΔW1 represents the target value of the power factor.

[0047] It should be noted that the influence weight of the structural control evaluation value represents the value of the influence degree of the structural control evaluation value on the deviation state evaluation index of the wind turbine. When used, the influence weight corresponding to the structural control evaluation value can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the structural control evaluation value and the influence weight of the structural control evaluation value form a mapping set, and the real-time structural control evaluation value is input into the mapping set to obtain the influence weight corresponding to the structural control evaluation value, where the mapping relationship can be a one-to-one or many-to-one relationship. The influence weight of the performance control evaluation value represents the value of the influence degree of the performance control evaluation value on the deviation state evaluation index of the wind turbine. When used, the influence weight corresponding to the performance control evaluation value can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the performance control evaluation value and the influence weight of the performance control evaluation value form a mapping set, and the real-time performance control evaluation value is input into the mapping set to obtain the influence weight corresponding to the performance control evaluation value, where the mapping relationship can be a one-to-one or many-to-one relationship.

[0048] Furthermore, the deviation state evaluation index of the wind turbine is obtained by analyzing the operation monitoring parameters of the wind turbine, and the operation monitoring category is obtained through such processing. The specific process is as follows: Obtain the first deviation state evaluation threshold of the wind turbine and the second deviation state evaluation threshold of the wind turbine preset in the database; Based on the deviation state evaluation index of the wind turbine, compare it with the first deviation state evaluation threshold of the wind turbine and the second deviation state evaluation threshold of the wind turbine. If the deviation state evaluation index of the wind turbine is above the first deviation state evaluation threshold of the wind turbine, the operation monitoring category is wind turbine failure; If the deviation state evaluation index of the wind turbine is less than the first deviation state evaluation threshold of the wind turbine and above the second deviation state evaluation threshold of the wind turbine, the operation monitoring category is wind turbine abnormality; If the deviation state evaluation index of the wind turbine is less than the second deviation state evaluation threshold of the wind turbine, the operation monitoring category is wind turbine normal.

[0049] Further, track and monitor abnormal wind turbine units, specifically including: obtaining real-time external environmental parameters through the edge intelligent main control sensing hub connected by a secure and trusted software and hardware platform; obtaining the preset external environmental target set in the database, where the external environmental target set includes: the target value of external environmental wind speed, the target value of external atmospheric pressure, the target value of external environmental temperature, and the target value of external environmental humidity; obtaining the environmental matching degree by comparing the real-time external environmental parameters with the external environmental target set; screening the deviation state evaluation index of abnormal wind turbine units based on the deviation state evaluation index of wind turbine units; obtaining the preset environmental target matching degree in the database, and performing fusion analysis on the environmental matching degree, the environmental target matching degree, the deviation state evaluation index of abnormal wind turbine units, and the first threshold of the deviation state of wind turbine units to obtain the tracking control reference index of abnormal wind turbine units; the real-time external environmental parameters include: real-time external environmental wind speed, real-time external atmospheric pressure, real-time external environmental temperature, and real-time external environmental humidity; the environmental matching degree is used to characterize the matching degree of the current operating environment of abnormal wind turbine units; the tracking control reference index of abnormal wind turbine units is used to characterize the execution complexity of optimizing control of abnormal wind turbine units.

[0050] In this embodiment, the method for obtaining the environmental matching degree is as follows:

[0051]

[0052] In the formula, E represents the environmental matching degree, e represents the natural constant, S v represents the real-time external environmental wind speed, ΔS v represents the target value of environmental wind speed, W f represents the real-time external atmospheric pressure, ΔW f represents the target value of external atmospheric pressure, W v represents the real-time external environmental temperature, ΔW v represents the target value of external environmental temperature, H v represents the real-time external environmental humidity, ΔH v represents the target value of external environmental humidity, β1 represents the influence weight of real-time external environmental wind speed, β2 represents the influence weight of real-time external environmental temperature, β3 represents the influence weight of real-time external atmospheric pressure, and β4 represents the influence weight of real-time external environmental humidity.

[0053] It should be noted that the real-time external environmental wind speed can be obtained through real-time monitoring by high-precision wind speed sensors in the wind farm, the real-time external atmospheric pressure can be obtained through real-time monitoring of the meteorological tower by barometric sensors, the real-time external environmental temperature can be obtained through real-time detection of the temperature at the location of the meteorological tower by temperature sensors, and the real-time external environmental humidity can be obtained through real-time detection of the humidity at the location of the meteorological tower by humidity sensors.

[0054] The environmental matching degree obtained by analyzing the real-time external environmental wind speed, real-time external atmospheric pressure, real-time external environmental temperature, and real-time external environmental humidity takes into account the mutual influence among these parameters. For example, the change in wind speed may affect the distribution of atmospheric pressure. In strong wind weather, an increase in wind speed will lead to a decrease in local atmospheric pressure. At the same time, the change in atmospheric pressure will also affect the magnitude and direction of wind speed. In the case of a large wind speed, air flow will accelerate the dissipation of heat, thereby reducing the environmental temperature. On the contrary, in the case of a small wind speed, air flow is slow, heat is not easily dissipated, and the environmental temperature will be relatively high. In the case of a large wind speed, air flow will accelerate the diffusion and evaporation of water vapor, thereby reducing the environmental humidity. On the contrary, in the case of a small wind speed, air flow is slow, water vapor is not easily diffused, and the environmental humidity will be relatively high. As the temperature rises, the movement of gas molecules in the atmosphere intensifies, resulting in a decrease in atmospheric pressure. The change in atmospheric pressure will affect the content and distribution of water vapor in the air; on the other hand, the change in environmental humidity may also have an impact on atmospheric pressure. As the temperature rises, the content of water vapor in the air will increase, leading to an increase in environmental humidity. When the temperature reaches a certain level, the water vapor in the air reaches a saturated state and begins to condense into water droplets or dew, thereby reducing the environmental humidity. In addition, the change in environmental humidity will also affect the perception and distribution of environmental temperature.

[0055] The real-time external environment wind speed influence weight represents the numerical value of the influence degree of the real-time external environment wind speed on the environment matching degree. When in use, the influence weight corresponding to the real-time external environment wind speed can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the real-time external environment wind speed and the real-time external environment wind speed influence weight form a mapping set. Inputting the real-time external environment wind speed into the mapping set can obtain the influence weight corresponding to the real-time external environment wind speed, and the mapping relationship therein can be a one-to-one or many-to-one relationship. The real-time external environment temperature influence weight represents the numerical value of the influence degree of the real-time external environment temperature on the environment matching degree. When in use, the influence weight corresponding to the real-time external environment temperature can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the real-time external environment temperature and the real-time external environment temperature influence weight form a mapping set. Inputting the real-time external environment temperature into the mapping set can obtain the influence weight corresponding to the real-time external environment temperature, and the mapping relationship therein can be a one-to-one or many-to-one relationship. The real-time external atmospheric pressure influence weight represents the numerical value of the influence degree of the real-time external atmospheric pressure on the environment matching degree. When in use, the influence weight corresponding to the real-time external atmospheric pressure can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the real-time external atmospheric pressure and the real-time external atmospheric pressure influence weight form a mapping set. Inputting the real-time external atmospheric pressure into the mapping set can obtain the influence weight corresponding to the real-time external atmospheric pressure, and the mapping relationship therein can be a one-to-one or many-to-one relationship. The real-time external environment humidity influence weight represents the numerical value of the influence degree of the real-time external environment humidity on the environment matching degree. When in use, the influence weight corresponding to the real-time external environment humidity can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the real-time external environment humidity and the real-time external environment humidity influence weight form a mapping set. Inputting the real-time external environment humidity into the mapping set can obtain the influence weight corresponding to the real-time external environment humidity, and the mapping relationship therein can be a one-to-one or many-to-one relationship.

[0056] Obtain the tracking control reference index of the abnormal unit. The method is as follows:

[0057]

[0058] In the formula, L represents the tracking control reference index of the abnormal unit, E represents the environment matching degree, ΔE represents the environment target matching degree, R represents the deviation state evaluation index of the wind turbine unit, and Y r represents the first threshold of the deviation state evaluation of the wind turbine unit.

[0059] Further, the optimized control execution parameters of the abnormal unit are analyzed, specifically including: obtaining the preset tracking control reference index intervals in the database and the corresponding operation adjustment parameters for each tracking control reference index interval, matching the tracking control reference index of the abnormal unit with each tracking control reference index interval, and if the tracking control reference index of the abnormal unit is within a certain tracking control reference index interval, obtaining the operation adjustment parameters corresponding to the tracking control reference index interval as the optimized control execution parameters of the abnormal unit.

[0060] In this embodiment, it should be noted that the optimized control execution parameters of the abnormal unit include: the wind turbine speed adjustment value, the generator speed adjustment value, the pitch angle adjustment value, the generator temperature adjustment value, and the yaw angle adjustment value. By obtaining the preset tracking control reference index intervals and their corresponding operation adjustment parameters in the database, and then precisely matching the tracking control reference index of the abnormal unit with each tracking control reference index interval to determine the operation adjustment parameters most suitable for the current abnormal situation as the optimized control execution parameters, it can greatly save resources. In the large-scale operation of a wind farm, if all units are subjected to undifferentiated comprehensive detection, it will not only consume a large amount of time, manpower, and material resources, but also increase equipment wear and energy consumption due to frequent operations. By implementing precise measures only for the units with problems, unnecessary resource waste is avoided. Once an abnormal unit is identified, the corresponding optimized control execution parameters can be quickly matched and the adjustment can be immediately implemented, so as to restore the normal operation state of the unit in the shortest time. This not only reduces the power generation loss caused by fault shutdown, but also improves the overall operation efficiency and reliability of the wind farm.

[0061] Further, the parameters of each currently available backup storage location specifically include: the remaining space capacity, the remaining space capacity ratio, the disk read and write speed, the average time between disk failures, and the average disk failure repair time of each backup storage location.

[0062] In this embodiment, the remaining space capacity can be obtained by using a management tool or a command-line tool to obtain the remaining space capacity of the storage device. The remaining space capacity ratio can be obtained by calculating the ratio of the remaining space capacity to the total capacity, or the above file system management tool or command-line tool can be used to directly view this ratio. The disk read and write speed can be measured using disk performance testing software (such as CrystalDiskMark hard disk detection tool, HD Tune hard disk detection tool, etc.). The average time between disk failures can be obtained by looking up the historical actual operation records, and the average disk failure repair time can be obtained by looking up the historical actual operation records.

[0063] It should be noted that the evaluation value of the backup storage effect of each backup storage location is obtained by analyzing the parameters of each currently available backup storage location (the remaining space capacity, the remaining space capacity ratio, the disk read and write speed, the average trouble-free time of the disk, and the average fault repair time of the disk). This takes into account the mutual influence between these parameters. For example, when the remaining space capacity is small, the disk read and write speed will be affected. Maintaining a small remaining space capacity for a long time will have a negative impact on the disk life because the disk needs more write operations to manage the limited space, which increases the disk wear and failure risk. A faster read and write speed can shorten the data access time, improve the system response speed and throughput. A longer average trouble-free time of the disk means that the disk is less likely to fail, thus improving the storage reliability. A shorter average fault repair time of the disk can reduce the fault downtime and improve the availability.

[0064] Furthermore, screening is performed to obtain the suitable backup storage locations, and the operation monitoring parameters of the abnormal units are transmitted to the suitable backup storage locations for backup storage. Specifically, it includes: based on the parameters of each backup storage location, the evaluation value of the backup storage effect of each backup storage location is processed, and thus the best backup storage location is obtained through screening; the evaluation value of the backup storage effect of each backup storage location is used to represent the backup storage effect of each. Based on the evaluation value of the backup storage effect of each backup storage location, the backup storage location corresponding to the maximum evaluation value of the backup storage effect is obtained as the suitable backup storage location, and the operation monitoring parameters of the abnormal units are backup stored in the suitable backup storage location.

[0065] In this embodiment, the method for obtaining the evaluation value of the backup storage effect of each backup storage location is as follows:

[0066]

[0067] In the formula, Z represents the evaluation value of the backup storage effect of each backup storage location, C i represents the remaining space capacity of the i-th backup storage location, i represents the number of the backup storage location, i = 1, 2,..., I, and I represents the total number of backup storage locations. represents the average remaining space capacity of the backup storage location, B i represents the remaining space capacity ratio of the i-th backup storage location. represents the average remaining space capacity ratio of the backup storage location, V i represents the disk read and write speed of the i-th backup storage location. represents the average disk read and write speed of the backup storage location, T i represents the average trouble-free time of the disk of the i-th backup storage location. represents the average average trouble-free time of the disk of the backup storage location. Indicates the average disk failure repair time of the backup storage location, Q i Indicates the average disk failure repair time of the i-th backup storage location, γ1 represents the influence weight of the average remaining space capacity, γ2 represents the influence weight of the average remaining space capacity ratio, γ3 represents the influence weight of the average disk read / write speed, γ4 represents the influence weight of the average disk failure-free time, and γ5 represents the influence weight of the average disk failure repair time.

[0068]

[0069] It should be noted that the influence weight of the average remaining space capacity represents the value of the influence degree of the average remaining space capacity on the evaluation value of the backup storage effect of each backup storage location. When in use, the influence weight corresponding to the average remaining space capacity can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the average remaining space capacity and the influence weight of the average remaining space capacity form a mapping set, and the real-time average remaining space capacity is input into the mapping set to obtain the influence weight corresponding to the average remaining space capacity, where the mapping relationship can be a one-to-one or many-to-one relationship. The influence weight of the average remaining space capacity ratio represents the value of the influence degree of the average remaining space capacity ratio on the evaluation value of the backup storage effect of each backup storage location. When in use, the influence weight corresponding to the average remaining space capacity ratio can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the average remaining space capacity ratio and the influence weight of the average remaining space capacity ratio form a mapping set, and the real-time average remaining space capacity ratio is input into the mapping set to obtain the influence weight corresponding to the average remaining space capacity ratio, where the mapping relationship can be a one-to-one or many-to-one relationship. The influence weight of the average disk read / write speed represents the value of the influence degree of the average disk read / write speed on the evaluation value of the backup storage effect of each backup storage location. When in use, the influence weight corresponding to the average disk read / write speed can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the average disk read / write speed and the influence weight of the average disk read / write speed form a mapping set, and the real-time average disk read / write speed is input into the mapping set to obtain the influence weight corresponding to the average disk read / write speed, where the mapping relationship can be a one-to-one or many-to-one relationship. The influence weight of the average disk failure repair duration represents the value of the influence degree of the average disk failure repair duration on the evaluation value of the backup storage effect of each backup storage location. When in use, the influence weight corresponding to the average disk failure repair duration can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the average disk failure repair duration and the influence weight of the average disk failure repair duration form a mapping set, and the real-time average disk failure repair duration is input into the mapping set to obtain the influence weight corresponding to the average disk failure repair duration, where the mapping relationship can be a one-to-one or many-to-one relationship. The influence weight of the average disk failure-free duration represents the value of the influence degree of the influence weight of the average disk failure-free duration on the evaluation value of the backup storage effect of each backup storage location. When in use, the influence weight corresponding to the influence weight of the average disk failure-free duration can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the influence weight of the average disk failure-free duration and the influence weight of the influence weight of the average disk failure-free duration form a mapping set, and the real-time influence weight of the average disk failure-free duration is input into the mapping set to obtain the influence weight corresponding to the influence weight of the average disk failure-free duration, where the mapping relationship can be a one-to-one or many-to-one relationship.

[0070] Furthermore, it also includes a data security and encryption module, which is used to use an encryption algorithm to transmit and save the operation monitoring parameters of the wind turbine stored in the main control cloud platform and the operation monitoring parameters of the abnormal units in the data backup module respectively in the main control cloud platform and the data backup module, and set access permission management.

[0071] In this embodiment, the operation monitoring parameters of the wind turbine stored in the main control cloud platform and the data backup module are stored in the initial buffer area of the appropriate backup storage location in plain text, and are recorded as temporary data; the temporary data is encrypted using a symmetric encryption algorithm to generate ciphertext data and a symmetric encryption key; in the wind power main control system, an asymmetric encryption algorithm is used to generate an asymmetric key pair, and the asymmetric key pair includes a public key and a private key; the public key pair is used for encryption to obtain encrypted key data, and the encrypted key data is transmitted to the data backup module. When the data backup module receives the encrypted key data, the private key is used to decrypt the encrypted key data to obtain the symmetric encryption key, and the symmetric encryption key is stored in the backup module in an encrypted form; the encrypted ciphertext data is stored in the main control cloud platform and the data backup module respectively, and an access permission management mechanism is set to perform different levels of access control on the operation monitoring parameters of the wind turbine according to the user permission level. When a user requests access to the encrypted data, the wind power main control system verifies the user's access permission. If the access permission verification passes, the stored symmetric encryption key is called to decrypt the ciphertext data, and the decrypted data is displayed to the user. If the access permission verification fails, it is not displayed. By providing multi-level data encryption and secure communication, the integrity and confidentiality of data during transmission and storage are ensured, and the energy conversion efficiency and operation stability are improved through real-time optimization control of the wind turbine.

[0072] It should be noted that storing the operation monitoring parameters of the wind turbine in the main control cloud platform without uploading them to the data backup module can reduce unnecessary data transmission. Storing the operation monitoring parameters of the unit in the data backup module facilitates quickly locating and analyzing the cause of the fault for timely processing. Through targeted backup and optimizing the data processing flow, resource waste is reduced.

[0073] In summary, in this embodiment, the operation monitoring parameters of the wind turbine are collected in real time through the data collection module, and the operation monitoring evaluation value is obtained through the data analysis module, so as to accurately judge the operation state of the wind turbine, and then realize the precise monitoring and timely warning of the wind turbine, effectively solving the problems of insufficient safety performance and reliability in the existing wind power main control system, and improving the safe operation level and fault response ability of the wind farm.

[0074] Those skilled in the art will appreciate that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0075] The present invention is described with reference to the flowcharts and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0076] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0077] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0078] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0079] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. A wind power master control system based on a secure and reliable software and hardware platform, characterized by: include: The data acquisition module is used to collect the operation monitoring parameters of the wind turbine in real time through the edge intelligent master control sensor hub connected to the secure and trusted hardware and software platform; A data analysis module is used to analyze the wind turbine generator set operation monitoring parameters to obtain the deviation state evaluation index of the wind turbine generator set, thereby obtaining the operation monitoring category, wherein the operation monitoring category is: normal wind turbine generator set, abnormal wind turbine generator set and faulty wind turbine generator set; The monitoring result judgment module is used to save the operation monitoring parameters of the wind turbine set to the main control cloud platform when the operation monitoring category is that the wind turbine set is normal; when the operation monitoring category is that the wind turbine set is abnormal, the wind turbine set is recorded as an abnormal unit and optimized control is performed; when the operation monitoring category is that the wind turbine set is faulty, an early warning is performed and the wind turbine set is controlled to enter a safety protection mode; The abnormal unit optimization control module is used to track and monitor the abnormal units, analyze and obtain the optimization control execution parameters of the abnormal units, and optimize the abnormal units through the main control cloud platform; The tracking and monitoring of abnormal units specifically includes: Acquire real-time external environment parameters through the edge intelligent master control sensor hub connected by a secure and trusted hardware and software platform; Obtaining an external environment target set preset in a database, wherein the external environment target set includes: an external environment wind speed target value, an external atmospheric pressure target value, an external environment temperature target value, and an external environment humidity target value; The environmental matching degree is obtained by comparing the real-time external environmental parameters with the external environmental target set; Based on the deviation state evaluation index of the wind turbine generator set, the deviation state evaluation index of the abnormal unit is screened and obtained; Obtain the environmental target matching degree preset in the database, and fuse and analyze the environmental matching degree, environmental target matching degree, deviation state evaluation index of abnormal units and the first threshold value of deviation state evaluation of wind turbine units to obtain the tracking control reference index of abnormal units; The real-time external environment parameters include: real-time external environment wind speed, real-time external atmospheric pressure, real-time external environment temperature and real-time external environment humidity; The environmental matching degree is used to characterize the matching degree of the current operating environment of the abnormal unit; The tracking control reference index of the abnormal unit is used to characterize the complexity of the execution of the optimization control of the abnormal unit; The data backup module is used to count the operation monitoring parameters of abnormal units and obtain the parameters of each currently available backup storage location, thereby screening to obtain the adapted backup storage location, and transmitting the operation monitoring parameters of the abnormal units to the adapted backup storage location for backup storage.

2. The wind power master control system based on a secure and reliable software and hardware platform as claimed in claim 1, characterized in that: The edge intelligent master control sensor hub connected through the secure and trusted hardware and software platform collects the operation monitoring parameters of the wind turbine in real time; The operation monitoring parameters specifically include: blade deviation angle, output power, main shaft speed, main shaft inclination, generator temperature, yaw angle, power factor and oil level of the wind turbine.

3. The wind power master control system based on a secure and reliable software and hardware platform as claimed in claim 1, characterized in that: The specific method for analyzing the wind turbine generator set's operating monitoring parameters to obtain the wind turbine generator set's deviation state assessment index is as follows: Acquire an operation monitoring target set of a wind turbine generator set, wherein the operation monitoring target set of the wind turbine generator set includes: a blade deviation angle target value, an output power target value, a main shaft speed target value, a main shaft inclination target value, a generator temperature target value, a yaw angle target value, a power factor target value, and an oil level height target value; The wind turbine operation monitoring target set and the wind turbine operation monitoring parameters are analyzed to obtain the wind turbine deviation state evaluation index; The deviation state evaluation index of the wind turbine generator set is used to characterize the degree of abnormal operation of the wind turbine generator set.

4. The wind power master control system based on a secure and reliable software and hardware platform as claimed in claim 1, characterized in that: The deviation state evaluation index of the wind turbine is obtained. The specific method is as follows: Where R represents the deviation state evaluation index of the wind turbine, e represents the natural constant, R1 represents the structural control evaluation value of the wind turbine, R2 represents the performance control evaluation value of the wind turbine, μ1 represents the influence weight of the structural control evaluation value, and μ2 represents the influence weight of the performance control evaluation value.

5. The wind power master control system based on a secure and reliable software and hardware platform as claimed in claim 1, characterized in that: The deviation state evaluation index of the wind turbine generator set is obtained by analyzing the operation monitoring parameters of the wind turbine generator set, and the operation monitoring category is obtained by processing. The specific process is as follows: Obtaining a first threshold value for evaluating a deviation state of a wind turbine set and a second threshold value for evaluating a deviation state of a wind turbine set preset in a database; Based on the deviation state assessment index of the wind turbine generator set, and compared with the first deviation state assessment threshold of the wind turbine generator set and the second deviation state assessment threshold of the wind turbine generator set, if the deviation state assessment index of the wind turbine generator set is above the first deviation state assessment threshold of the wind turbine generator set, then the operation monitoring category is a wind turbine generator fault; If the deviation state assessment index of the wind turbine generator set is less than the first deviation state assessment threshold of the wind turbine generator set and is above the second deviation state assessment threshold of the wind turbine generator set, the operation monitoring category is abnormality of the wind turbine generator set; If the deviation state assessment index of the wind turbine generator set is less than the second deviation state assessment threshold of the wind turbine generator set, the operation monitoring category is that the wind turbine generator set is normal.

6. The wind power master control system based on a secure and reliable software and hardware platform as claimed in claim 1, characterized in that: The analysis obtains the abnormal unit optimization control execution parameters, which specifically include: Obtain each tracking control reference index interval preset in the database and the operating adjustment parameters corresponding to each tracking control reference index interval, match the tracking control reference index of the abnormal unit with each tracking control reference index interval; if the tracking control reference index of the abnormal unit is within a certain tracking control reference index interval, obtain the operating adjustment parameters corresponding to the tracking control reference index interval as the abnormal unit optimization control execution parameters.

7. The wind power master control system based on a secure and reliable software and hardware platform as claimed in claim 6, characterized in that: The currently available parameters of each backup storage location specifically include: the remaining space capacity of each backup storage location, the remaining space capacity ratio, the disk read and write speed, the disk average failure-free time, and the disk average failure repair time.

8. The wind power master control system based on a secure and reliable software and hardware platform as claimed in claim 7, characterized in that: The screening to obtain an adapted backup storage location and transmitting the operation monitoring parameters of the abnormal unit to the adapted backup storage location for backup storage specifically includes: According to the parameters of each backup storage location, the backup storage effect evaluation value of each backup storage location is processed to obtain the best backup storage location through screening; The backup storage effect evaluation value of each backup storage location is used to characterize the backup storage effect; Based on the backup storage effect evaluation values ​​of each backup storage location, the backup storage location corresponding to the maximum backup storage effect evaluation value is obtained as the adaptive backup storage location, and the operation monitoring parameters of the abnormal unit are backed up and stored in the adaptive backup storage location.

9. The wind power master control system based on a secure and reliable software and hardware platform as claimed in claim 1, characterized in that: It also includes a data security and encryption module, which is used to use encryption algorithms to transmit the operation monitoring parameters of the wind turbine sets stored in the main control cloud platform and the operation monitoring parameters of the abnormal units in the data backup module, and save them in the main control cloud platform and the data backup module respectively, and set access permission management.

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