A wind power cluster multi-station reactive power voltage coordinated control device and method

Abstract

The present invention relates to the field of voltage control technology, and specifically to a wind power cluster multi-station reactive voltage collaborative control device and method, the device comprising: an AVC master station, responsible for the overall voltage optimization control of the system; an AVC cooperative control substation, located between the AVC master station and the AVC substation, receiving the voltage control instructions sent by the AVC master station, converting the voltage control instructions into reactive instructions, and sending them to each AVC substation for execution; the AVC substation, receiving the reactive instructions sent by the AVC cooperative control substation, controlling the reactive sources in their respective areas, and performing closed-loop control of the voltage target value; a parameter control system, which uses two sets of control parameters to respectively apply the control requirements of the same application scenario and switch the control parameters. Through the present invention, the problems of voltage oscillation and control difficulty caused by the large discreteness of the response time in the prior art are effectively solved, the response speed of the system and the stability of voltage control are improved, and it is adapted to different wind farm layouts and control requirements.

Description

Technical Field

[0001] The present invention relates to the field of voltage control technology, and in particular to a device and method for coordinated reactive voltage control of multiple stations in a wind power cluster. Background Art

[0002] Wind power generation, as a renewable energy source, has been widely used around the world. Wind farms convert wind energy into electrical energy through wind turbines and transmit it to the power grid. However, due to the instability and unpredictability of wind energy, the connection of wind farms to the power grid will bring challenges to the voltage stability and reactive power balance of the power grid. To address these problems, existing technologies generally adopt automatic voltage control (AVC) systems.

[0003] Currently, voltage control for wind farm clusters primarily utilizes a two-tier control architecture consisting of an AVC master station and AVC substations. In this architecture, the AVC master station coordinates voltage control for the entire wind farm cluster and sends voltage control instructions to each AVC substation. Each substation, in turn, controls the reactive power within its area of ​​responsibility based on these instructions to achieve the target voltage. However, in scenarios involving wind farm clusters with relatively close electrical distances, the response times of different substation systems vary significantly, which can easily lead to severe voltage fluctuations and create the risk of non-convergence.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present disclosure and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0005] The present invention provides a wind power cluster multi-station reactive power and voltage coordinated control device and system, thereby effectively solving the problems pointed out in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A wind power cluster multi-station reactive power and voltage coordinated control device, comprising:

[0008] AVC master station, responsible for the overall voltage optimization control of the system;

[0009] The AVC auxiliary control substation is located between the AVC master station and the AVC substations, receives voltage control instructions sent by the AVC master station, converts the voltage control instructions into reactive power instructions, and sends them to each AVC substation for execution;

[0010] The AVC substation receives the reactive power command issued by the AVC cooperative control substation, controls the reactive power source in its respective area, and performs closed-loop control of the voltage target value;

[0011] The parameter control system is connected to the AVC master station, AVC co-control substation and AVC substation through data communication. Through two sets of control parameters, it is respectively applicable to the control requirements of different application scenarios and switches the control parameters to affect the voltage optimization control strategy of the AVC master station, adjust the way in which the AVC co-control substation generates reactive power instructions, and change the control strategy of the AVC substation for the reactive power source.

[0012] Furthermore, it also includes: an adaptive learning system, which optimizes control parameters and adjustment strategies by analyzing historical data. The adaptive learning system is connected to the AVC master station, AVC cooperative control substation, AVC substation and parameter control system, and adaptively adjusts control parameters based on historical operation data collected from each of the AVC substations, and issues optimized control instructions to the AVC master station through the parameter control system, and adjusts the generation method of reactive instructions through the AVC cooperative control substation.

[0013] Furthermore, by analyzing historical data, control parameters and adjustment strategies are optimized, including:

[0014] Obtain voltage, reactive power and related control parameters during system operation and establish a historical database;

[0015] Performing system stability influence identification and pattern recognition on the historical data in the historical database to obtain historical data analysis results;

[0016] Obtaining a prediction model based on the historical data analysis results and a machine learning algorithm;

[0017] According to the prediction model, voltage fluctuations and reactive power requirements in the future operating environment are predicted, and the control strategy is adjusted in advance.

[0018] Furthermore, the machine learning algorithm is a gradient boosting decision tree.

[0019] Furthermore, the AVC co-control substation includes:

[0020] A parsing module, configured to receive a control target calculated by the AVC master station and parse the control target into a voltage target value;

[0021] The signal module outputs a voltage increase blocking signal, a voltage decrease blocking signal or a bidirectional voltage blocking signal according to the current real-time voltage value and the upper and lower voltage limits;

[0022] An acquisition module, for acquiring the real-time voltage of the controlled bus and the reactive power;

[0023] Mode selection module, enabling corresponding control mode according to control word setting;

[0024] A fixed value algorithm module is used to set control parameters and calculate the reactive power control target value based on the input voltage target value and actual voltage conditions;

[0025] The instruction distribution module decomposes reactive power instructions according to the calculated reactive power control target value and sends the instructions to each AVC substation for execution.

[0026] Furthermore, the fixed value algorithm module includes:

[0027] The fixed value unit includes the voltage regulation step, cycle, dead zone and reactive power regulation step, cycle, dead zone of the wind farm grid connection point;

[0028] The algorithm unit calculates the reactive power target value according to the voltage target value output by the analysis module, the bus voltage value and reactive power output by the acquisition module, and the parameters of the setting unit.

[0029] Furthermore, the AVC assistance substation also includes: a redundant control module, which automatically takes over all functions of the main control module when the main control module fails. The main control module is composed of an analysis module, a signal module, an acquisition module, a mode selection module, a constant algorithm module and an instruction allocation module.

[0030] Furthermore, the redundant control module includes:

[0031] Redundant parsing unit, receiving and parsing the control target from the AVC master station. When the parsing module fails, the redundant parsing unit can automatically take over the parsing task and correctly convert the control target;

[0032] The redundant signal processing unit is responsible for processing the current real-time voltage value and voltage upper and lower limit information. When the signal module fails, it generates a voltage increase lock signal, a voltage decrease lock signal, or a bidirectional voltage lock signal;

[0033] Redundant mode selection unit, used to enable the corresponding control mode according to the control word setting, and can automatically activate and switch and execute the control mode when the mode selection module fails;

[0034] A redundant fixed value algorithm unit, when a fixed value algorithm module fails, calculates a reactive power target value based on the voltage target value output by the analysis module, the bus voltage value and reactive power output by the acquisition module, and the control parameters preset in the fixed value algorithm module, and sends the calculation result to the instruction distribution module;

[0035] The redundant instruction distribution unit decomposes the reactive power instructions according to the reactive power target value output by the redundant algorithm unit when the instruction distribution module fails, and sends the instructions to each AVC substation for execution.

[0036] A method for coordinated reactive power and voltage control of multiple stations in a wind power cluster is provided, which adopts the above-mentioned device for coordinated reactive power and voltage control of multiple stations in a wind power cluster, and comprises:

[0037] The AVC master station calculates the overall optimization target and sends voltage control instructions;

[0038] The auxiliary control substation receives the voltage control instruction and resolves the voltage control instruction into a specific reactive power control instruction;

[0039] Collect bus voltage and reactive power data in real time, and calculate reactive power target value based on voltage target and real-time data;

[0040] Each of the AVC substations receives and executes a reactive power target value, and performs closed-loop control of a voltage target value.

[0041] Furthermore, the method further includes: optimizing control parameters and adjustment strategies by analyzing historical data.

[0042] The technical solution of the present invention can achieve the following technical effects:

[0043] The system solves the problems of voltage oscillation and control difficulty caused by large discrete response time in the existing technology, improves the response speed of the system and the stability of voltage control, and adapts to different wind farm layouts and control requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 This is a framework diagram of a wind power cluster multi-station reactive power and voltage coordinated control device;

[0046] Figure 2 Flowchart for optimizing control parameters and adjustment strategies;

[0047] Figure 3 The figure is a flow chart of a method for coordinated reactive power and voltage control of multiple stations in a wind power cluster; DETAILED DESCRIPTION

[0048] 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.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0050] Example 1

[0051] like Figure 1 As shown, the present invention provides a wind power cluster multi-station reactive power and voltage coordinated control device, the device comprising:

[0052] AVC master station, responsible for the overall voltage optimization control of the system;

[0053] The AVC auxiliary control substation is located between the AVC master station and the AVC substations. It receives voltage control instructions from the AVC master station, converts the voltage control instructions into reactive power instructions, and sends them to each AVC substation for execution.

[0054] The AVC substation receives reactive power instructions from the AVC cooperative control substation, controls the reactive power sources in its respective area, and performs closed-loop control of the voltage target value;

[0055] The parameter control system is connected to the AVC master station, AVC co-control substation and AVC substation through data communication. Through two sets of control parameters, it is suitable for the control requirements of different application scenarios and switches the control parameters to affect the voltage optimization control strategy of the AVC master station, adjust the way the AVC co-control substation generates reactive power instructions, and change the control strategy of the AVC substation for the reactive power source.

[0056] Specifically, the AVC master station calculates the voltage demand of the power grid, generates voltage control instructions, and sends these instructions to the AVC co-control substation to coordinate the reactive voltage regulation of the wind farm group. The AVC master station uses optimization algorithms to analyze the real-time status of the power grid and determine the voltage target value of each wind farm to ensure the stable operation of the entire power grid. Based on the optimization calculation results, the AVC master station transmits the voltage control instructions to the AVC co-control substation, providing a basis for subsequent reactive power control.

[0057] The AVC cooperative control substation is located between the AVC master station and the AVC substation. It receives the voltage control instructions sent by the AVC master station and parses these instructions into specific reactive power control instructions. The cooperative control substation selects appropriate control parameters and algorithms according to the grid operation mode (such as two-layer control mode or three-layer control mode). The parsed reactive power control instructions will be sent to each AVC substation to ensure that each wind farm can coordinate and coordinate reactive power voltage regulation.

[0058] The AVC substation receives reactive power control instructions from the AVC cooperative control substation and controls the reactive power sources (such as capacitor banks, sensors, etc.) in its respective area according to these instructions to achieve closed-loop control of the voltage target value. During the reactive power control process, the AVC substation monitors and feeds back the voltage and reactive power data in the area in real time to ensure that the control effect meets the expected results.

[0059] The parameter control system sets two sets of control parameters, which are suitable for different application scenarios, such as two-layer control mode and three-layer control mode. Under different operating modes, the parameter control system can dynamically switch control parameters to adapt to different grid operation requirements. By optimizing and adjusting the control parameters, the parameter control system can improve the system's response speed and stability, and ensure the accuracy and reliability of reactive voltage regulation.

[0060] The present invention effectively solves the problems of voltage oscillation and control difficulty caused by large discrete response time in the prior art, improves the response speed of the system and the stability of voltage control, and adapts to different wind farm layouts and control requirements.

[0061] As a preferred embodiment of the above embodiment, it also includes: an adaptive learning system, which optimizes control parameters and adjustment strategies by analyzing historical data. The adaptive learning system is connected to the AVC master station, AVC cooperative control substation, AVC substation and parameter control system, and adaptively adjusts control parameters based on historical operation data collected from each of the AVC substations, and issues optimized control instructions to the AVC master station through the parameter control system, and adjusts the generation method of reactive instructions through the AVC cooperative control substation.

[0062] Specifically, the adaptive learning system uses machine learning algorithms based on historical operating data and real-time feedback to continuously adjust and optimize control parameters, ensuring optimal system operation under varying operating conditions. By training and analyzing data from diverse operating conditions, the adaptive learning system can predict future operating trends and proactively adjust control strategies, thereby improving system response speed and stability and further reducing voltage fluctuations and reactive power losses.

[0063] As a preferred embodiment of the above, Figure 2 As shown, by analyzing historical data, the control parameters and adjustment strategies are optimized, including:

[0064] A10: Obtain voltage, reactive power and related control parameters during system operation and establish a historical database;

[0065] A20: Perform system stability influence identification and pattern recognition on historical data in the historical database to obtain historical data analysis results;

[0066] A30: Obtain a prediction model based on historical data analysis results and machine learning algorithms;

[0067] A40: Based on the prediction model, predict voltage fluctuations and reactive power demand in the future operating environment and adjust the control strategy in advance.

[0068] Specifically, first, during the system operation, data on voltage, reactive power and related control parameters are collected in real time, and these data are continuously stored in the historical database to form a comprehensive historical operation data record. These data provide detailed information about the system under different operating conditions.

[0069] Next, the data stored in the historical database is analyzed, including identification of influences on system stability and pattern recognition. By analyzing the impact of different factors on system stability in the historical data, we can identify which parameter changes have a significant impact on voltage stability and reactive power control. Simultaneously, using pattern recognition techniques, we identify the operating modes and characteristics in the historical data and identify typical system behaviors under different operating conditions.

[0070] Based on the above analysis results, a prediction model was built using a machine learning algorithm. First, the historical data was cleaned and normalized to remove noise and standardize the data format. The machine learning model was then trained using this preprocessed data to identify the impact of different parameter changes on system operation. During the training process, the model's accuracy was verified using a portion of the historical data, and model parameters were adjusted to improve prediction accuracy.

[0071] Using the trained prediction model, the currently collected voltage, reactive power and related control parameters are input in real time to predict voltage fluctuations and reactive power demand in the future operating environment. The model calculates the future voltage change trend and reactive power demand based on the input data, and adjusts the control strategy in advance according to the prediction results. For example, if it is predicted that the voltage will fluctuate in the future, the system can adjust the reactive power control parameters in advance to stabilize the voltage change.

[0072] As a preference of the above embodiment, the machine learning algorithm is a gradient boosting decision tree.

[0073] This solution uses the gradient boosted decision tree (GBDT) as the machine learning algorithm for the following reasons:

[0074] Adapting to complex voltage control requirements: In the coordinated reactive power and voltage control of multiple sites in a wind farm cluster, changes in voltage and reactive power are affected by multiple factors, including the layout of the wind farm, real-time weather conditions, load fluctuations, etc. There are complex nonlinear relationships between these factors. GBDT can effectively capture these complex nonlinear relationships and provide an accurate prediction model to ensure that the system can identify voltage fluctuations in advance and make adjustments.

[0075] High-dimensional data processing capabilities: This solution requires processing a large amount of real-time monitoring data and historical operating data. These data include multiple features such as voltage, reactive power, and control parameters. GBDT excels in processing high-dimensional data and can automatically select and combine the most important features, improving the model's prediction accuracy and efficiency.

[0076] Robustness and stability: Wind farms operate in a highly uncertain environment, and data may contain noise and missing values. GBDT is highly robust to these conditions, enabling reliable predictions even under imperfect data conditions. By continuously optimizing control parameters and strategies, GBDT can improve system stability and reduce voltage fluctuations and reactive power losses.

[0077] Model interpretability: In this solution, the AVC master station and AVC cooperative substation need to adjust their control strategies based on the prediction results. The GBDT results are highly interpretable, helping engineers understand the impact of various features on voltage control and optimize control strategies. For example, by analyzing the importance of each feature in the model, it is possible to identify the factors that have the greatest impact on voltage fluctuations, allowing targeted adjustments to be made.

[0078] As a preferred embodiment of the above, the AVC coordinating substation includes:

[0079] A parsing module is used to receive the control target calculated by the AVC master station and parse the control target into a voltage target value;

[0080] The signal module outputs a voltage increase blocking signal, a voltage decrease blocking signal or a bidirectional voltage blocking signal according to the current real-time voltage value and the upper and lower voltage limits;

[0081] Acquisition module, collecting the real-time voltage and reactive power of the controlled bus;

[0082] Mode selection module, enabling corresponding control mode according to control word setting;

[0083] The fixed value algorithm module is used to set the control parameters and calculate the reactive power control target value based on the input voltage target value and the actual voltage situation;

[0084] The instruction distribution module decomposes the reactive power instructions according to the calculated reactive power control target value and sends the instructions to each AVC substation for execution.

[0085] Specifically, the analysis module receives the voltage control instruction from the AVC master station, and converts the control target into a specific voltage target value through a preset analysis algorithm. These voltage target values ​​are used to guide subsequent reactive power control operations; the signal module monitors the current voltage value in real time, and when the voltage exceeds the set upper and lower limits, it outputs a corresponding blocking signal to protect the safe operation of the system. For example, when the voltage is too high, it outputs a voltage increase blocking signal, and when the voltage is too low, it outputs a voltage reduction blocking signal. In some cases, it outputs a bidirectional voltage blocking signal; the acquisition module collects the voltage and reactive power data of the controlled bus in real time through sensors and monitoring equipment, and feeds this data back to the analysis module and the constant value algorithm module for further calculation and control; the mode selection module selects the voltage and reactive power data of the controlled bus in real time according to the operation requirements of the power grid. and control word settings, select to enable double-layer control mode or three-layer control mode. The double-layer control mode is suitable for simpler grid structures and has a faster response speed. The three-layer control mode is suitable for complex grid structures and has a higher control accuracy. The fixed value algorithm module sets the corresponding control parameters, and calculates the reactive power control target value using a preset algorithm (such as PID control algorithm) based on the input voltage target value and the real-time collected voltage data. These target values ​​will be used to guide the reactive power control operation of the AVC substation. The instruction distribution module decomposes the reactive power control target values ​​calculated by the fixed value algorithm module into specific reactive power control instructions, and distributes them to each AVC substation for execution, ensuring that each wind farm can coordinate and coordinate reactive power voltage regulation to achieve the overall voltage control target of the system.

[0086] As a preferred embodiment of the above, the fixed value algorithm module includes:

[0087] The constant value unit is used to set and store a series of basic control parameters, including voltage regulation step, cycle, dead zone, and reactive power regulation step, cycle, dead zone;

[0088] The algorithm unit calculates the reactive power target value according to the voltage target value output by the analysis module, the bus voltage value and reactive power output by the acquisition module, and the parameters of the setting unit.

[0089] Specifically, the constant value algorithm module includes a constant value unit and an algorithm unit. The constant value unit is used to set and store a series of basic control parameters, including voltage regulation step size, cycle, dead zone, and reactive power regulation step size, cycle, and dead zone. These parameters are set by the system during initialization and stored in the constant value unit for use during the control process. The algorithm unit is responsible for calculating the reactive power target value based on the voltage target value output by the analysis module, the bus voltage value and reactive power output by the acquisition module, and the parameters of the constant value unit. The algorithm unit combines the voltage target value with the actual collected voltage and reactive power data, and uses a preset control algorithm, such as the PID control algorithm, to dynamically calculate the reactive power control target value, taking into account parameters such as the regulation step size, cycle, and dead zone, to ensure that the voltage operates stably within the target range. Through the collaborative work of the constant value unit and the algorithm unit, the constant value algorithm module can achieve accurate parameter setting and reactive power target value calculation, ensuring the stability and response speed of the system under different operating conditions.

[0090] As a preferred embodiment of the above embodiment, the AVC assisting substation also includes: a redundant control module, which automatically takes over all functions of the main control module when the main control module fails. The main control module includes an analysis module, a signal module, an acquisition module, a mode selection module, a constant algorithm module and an instruction allocation module.

[0091] As a preferred embodiment of the above, the redundant control module includes:

[0092] The redundant parsing unit receives and parses the control targets from the AVC master station. When the parsing module fails, the redundant parsing unit can automatically take over the parsing task and correctly switch the control targets.

[0093] The redundant signal processing unit is responsible for processing the current real-time voltage value and voltage upper and lower limit information. When the signal module fails, it generates a voltage increase lock signal, a voltage decrease lock signal, or a bidirectional voltage lock signal;

[0094] Redundant mode selection unit, used to enable the corresponding control mode according to the control word setting, and can automatically activate and switch and execute the control mode when the mode selection module fails;

[0095] A redundant fixed value algorithm unit, when a fixed value algorithm module fails, calculates a reactive power target value based on the voltage target value output by the analysis module, the bus voltage value and reactive power output by the acquisition module, and the control parameters preset in the fixed value algorithm module, and sends the calculation result to the instruction distribution module;

[0096] The redundant instruction distribution unit decomposes the reactive power instructions according to the reactive power target value output by the redundant algorithm unit when the instruction distribution module fails, and sends the instructions to each AVC substation for execution.

[0097] Specifically, the redundant analysis unit is responsible for receiving and analyzing the control targets from the AVC master station. When the analysis module fails, the redundant analysis unit can automatically take over the analysis task, correctly convert the control targets, and ensure that the generation of voltage target values ​​is uninterrupted; the redundant signal processing unit is responsible for processing the current real-time voltage value and voltage upper and lower limit information. In the event of a signal module failure, the redundant signal processing unit generates a voltage increase, voltage decrease, or bidirectional voltage block signal, ensuring safe system operation under voltage anomalies. The redundant mode selection unit automatically activates the corresponding control mode according to the control word settings. In the event of a mode selection module failure, the redundant mode selection unit automatically activates, switches, and executes the control mode, ensuring the correct selection of the system operating mode. In the event of a failure in the fixed value algorithm module, the redundant constant value algorithm unit calculates the reactive power target value based on the voltage target value output by the analysis module, the bus voltage and reactive power output by the acquisition module, and the control parameters preset in the fixed value algorithm module. The calculation result is sent to the instruction distribution module, ensuring accurate calculation of the reactive power control target value. In the event of a failure in the instruction distribution module, the redundant instruction distribution unit decomposes reactive power commands based on the reactive power target value output by the redundant constant value algorithm unit and sends the commands to each AVC substation for execution, ensuring coordinated reactive power and voltage regulation across all wind farms. Through the configuration of redundant control modules, even if the main control module fails, the AVC cooperative control substation can maintain normal operation, ensuring system stability and reliability.

[0098] Example 2

[0099] like Figure 3 As shown, the present application provides a method for coordinated reactive power and voltage control of multiple stations in a wind power cluster, which adopts the above-mentioned device for coordinated reactive power and voltage control of multiple stations in a wind power cluster, including:

[0100] S1: The AVC master station calculates the overall optimization target and sends voltage control instructions;

[0101] S2: The auxiliary control substation receives the voltage control instruction and interprets it into a specific reactive power control instruction;

[0102] S3: Collect bus voltage and reactive power data in real time, and calculate reactive power target value based on voltage target and real-time data;

[0103] S4: Each AVC substation receives and executes the reactive power target value and performs closed-loop control of the voltage target value.

[0104] As a preferred embodiment of the above embodiment, the method further includes: optimizing control parameters and adjustment strategies by analyzing historical data.

[0105] The technical effects achieved by this embodiment are the same as those of the first embodiment and will not be described again here.

[0106] Although the present application has been described with reference to specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and drawings are merely illustrative of the present application as defined herein and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, the present application is intended to include such modifications and variations as fall within the scope of the present application and its equivalents.

Claims

1. A wind power cluster multi-station reactive power and voltage coordinated control device, characterized in that: include: AVC master station, responsible for the overall voltage optimization control of the system; The AVC auxiliary control substation is located between the AVC master station and the AVC substations, receives voltage control instructions sent by the AVC master station, converts the voltage control instructions into reactive power instructions, and sends them to each AVC substation for execution; The AVC substation receives the reactive power command issued by the AVC cooperative control substation, controls the reactive power source in its respective area, and performs closed-loop control of the voltage target value; A parameter control system is connected to the AVC master station, the AVC co-control substation, and the AVC substation via data communication. Two sets of control parameters are used to adapt to the control requirements of different application scenarios. The control parameters are switched to affect the voltage optimization control strategy of the AVC master station, adjust the way the AVC co-control substation generates reactive power instructions, and change the control strategy of the AVC substation for reactive power sources. An adaptive learning system that analyzes historical data to optimize control parameters and adjustment strategies. The adaptive learning system is connected to the AVC master station, AVC cooperative control substation, AVC substation, and parameter control system. Based on historical operating data collected from each AVC substation, the adaptive learning system adaptively adjusts control parameters, issues optimized control instructions to the AVC master station via the parameter control system, and adjusts the generation method of reactive power instructions via the AVC cooperative control substation. By analyzing historical data, we can optimize control parameters and adjustment strategies, including: Obtain voltage, reactive power and related control parameters during system operation and establish a historical database; Performing system stability influence identification and pattern recognition on the historical data in the historical database to find out the typical behavior of the system under different operating conditions and obtain historical data analysis results; Obtaining a prediction model based on the historical data analysis results and a machine learning algorithm, wherein the machine learning algorithm is a gradient boosting decision tree; According to the prediction model, the currently collected voltage, reactive power and related control parameters are input in real time to predict the voltage fluctuation and reactive power demand in the future operating environment, and adjust the control strategy in advance.

2. The wind power cluster multi-station reactive power and voltage coordinated control device according to claim 1, characterized in that: The AVC cooperative control substation includes: A parsing module, configured to receive a control target calculated by the AVC master station and parse the control target into a voltage target value; The signal module outputs a voltage increase blocking signal, a voltage decrease blocking signal or a bidirectional voltage blocking signal according to the current real-time voltage value and the upper and lower voltage limits; An acquisition module, for acquiring the real-time voltage of the controlled bus and the reactive power; A mode selection module is configured to enable a corresponding control mode according to the control word setting, wherein the control mode affects the calculation method of the reactive power control target value and the instruction distribution strategy; a fixed value algorithm module for setting control parameters and calculating a reactive power control target value based on the input voltage target value and actual voltage conditions, wherein the actual voltage conditions include the blocking signal output by the signal module and the real-time voltage and reactive power collected by the collection module; The instruction distribution module decomposes reactive power instructions according to the calculated reactive power control target value and sends the instructions to each AVC substation for execution.

3. The wind power cluster multi-station reactive power and voltage coordinated control device according to claim 2, characterized in that: The fixed value algorithm module includes: The constant value unit is used to set and store a series of basic control parameters, including voltage regulation step, cycle, dead zone, and reactive power regulation step, cycle, dead zone; The algorithm unit calculates the reactive power target value according to the voltage target value output by the analysis module, the bus voltage value and reactive power output by the acquisition module, and the parameters of the setting unit.

4. A method for coordinated reactive power and voltage control of multiple wind power stations, using the device for coordinated reactive power and voltage control of multiple wind power stations according to any one of claims 1 to 3, characterized in that: include: The AVC master station calculates the overall optimization target and sends voltage control instructions; The auxiliary control substation receives the voltage control instruction and parses the voltage control instruction into a specific reactive power control instruction; Collect bus voltage and reactive power data in real time, and calculate reactive power target value based on voltage target and real-time data; Each of the AVC substations receives and executes a reactive power target value, and performs closed-loop control of a voltage target value.

5. The method for coordinated reactive power and voltage control of multiple wind power stations in a wind power cluster according to claim 4, characterized in that: The method further includes optimizing control parameters and adjustment strategies by analyzing historical data.

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