Anti-gust distributed cooperative control method and system for multiple wind turbine generators in regional wind field

Abstract

The invention discloses an anti-gust distributed cooperative control method and system for multiple wind turbine generators in a regional wind field, and relates to the technical field of wind power generation control, and the method comprises the steps: sensing gust disturbance information according to the operation state data of each generator in the wind field and meteorological sensor data; generating a cooperative anti-interference demand signal through a preset communication rule based on the topological relation between the wind turbine generators and the gust disturbance information; according to the safe operation constraint and the cooperative demand signal of each unit, obtaining the preliminary adjustment amount of an anti-interference control parameter; based on the wind field overall power scheduling target and the control parameter adjustment amount, an optimization control instruction giving consideration to disturbance rejection and power generation is obtained through a distributed optimization algorithm; and adjusting an execution mechanism according to the optimization control instruction to realize collaborative anti-gust control. By means of the mode, efficient distributed cooperation among wind field units can be achieved, gust impact can be effectively resisted, and meanwhile stable output of the overall power generation power of a wind field is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind power generation control, in particular to a regional wind farm multi-wind turbine gust resistance distributed collaborative control method and system. BACKGROUND

[0002] In the existing wind farm control technology, a centralized architecture with a central controller as the core or a scheme of independent control strategy of each wind turbine is generally adopted. The centralized control relies on the central controller to collect the whole field data and uniformly calculate the control instruction, which has problems such as large communication bandwidth pressure, heavy calculation burden, poor system expansibility, etc., resulting in serious lag in response to sudden gusts, and it is difficult to achieve fast and accurate control. Although the independent control strategy of each wind turbine avoids the central bottleneck, it lacks information interaction and collaborative mechanism among wind turbines, and each wind turbine only reacts according to the local wind conditions perceived by itself, which cannot predict and cooperatively respond to the wake disturbance from the upstream wind turbine, resulting in the conflict or disconnection of the anti-disturbance actions among wind turbines. Not only can it not effectively smooth the power and load fluctuations of the whole wind farm, but also it may amplify the negative impact of gusts due to asynchronous control, ultimately leading to challenges in the safety and stability of the operation of the wind farm.

[0003] The above content is only used to assist in understanding the technical solutions of the present application, and does not represent the acknowledgement of the above content as prior art. SUMMARY

[0004] The main purpose of the present application is to provide a regional wind farm multi-wind turbine gust resistance distributed collaborative control method and system, aiming at solving the technical problems of gust response lag and uncoordinated actions among wind turbines in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides a regional wind farm multi-wind turbine gust resistance distributed collaborative control method, which comprises: According to the operation state data and meteorological sensor data of each wind turbine in the wind farm, the gust disturbance information perceived by each wind turbine in real time is obtained; Based on the topological relationship among the wind turbines and the gust disturbance information, through a preset communication rule, a collaborative anti-disturbance demand signal between adjacent wind turbines is generated; According to the safety operation constraints of each wind turbine and the collaborative anti-disturbance demand signal, a preliminary adjustment amount of the anti-disturbance control parameter of each wind turbine is obtained; Based on the overall power scheduling target of the wind farm and the preliminary adjustment amount of the anti-disturbance control parameter, an optimized control instruction of each wind turbine considering anti-disturbance and power generation is obtained; According to the optimized control instruction, the actuator of the corresponding wind turbine is adjusted to realize the collaborative gust resistance control of the regional wind farm.

[0006] In an embodiment, the step of obtaining the gust disturbance information perceived by each wind turbine in real time according to the operating state data and the meteorological sensor data of each wind turbine in the wind farm comprises: obtaining the operating state data reflecting the current aerodynamic load state of the wind turbine according to the rotor speed, generator torque and pitch angle data collected by the main controller of the wind turbine in the wind farm; obtaining the meteorological sensor data containing the wind speed mutation component and the structural vibration component based on the ultrasonic anemometer and accelerometer data installed on the nacelle or tower; obtaining the gust disturbance information perceived by each wind turbine in real time according to the operating state data and the meteorological sensor data through time series correlation analysis, wherein the gust disturbance information represents the gust intensity, direction and duration.

[0007] In an embodiment, the step of obtaining the gust disturbance information perceived by each wind turbine in real time according to the operating state data and the meteorological sensor data through time series correlation analysis comprises: calculating the cross-correlation coefficient and the autocorrelation function of the operating state data and the meteorological sensor data in a preset time window, respectively; determining the peak value and phase information corresponding to the cross-correlation coefficient and the autocorrelation function, respectively, and determining the propagation path and sequence of the gust disturbance according to the peak value and the phase information; based on the propagation path and the sequence, performing weighted fusion and time series correction on the meteorological sensor data through time series correlation analysis to obtain the gust disturbance information perceived by each wind turbine in real time.

[0008] In an embodiment, the step of generating the collaborative anti-disturbance demand signal between adjacent wind turbines through a preset communication rule based on the topological relationship between the wind turbines and the gust disturbance information comprises: constructing a dynamic wind turbine neighbor relationship network aiming to minimize the wake effect based on the topological relationship between the wind turbines and the historical data of the main wind direction; obtaining the disturbance state estimation value of each wind turbine in the dynamic wind turbine neighbor relationship network based on the gust disturbance information and the dynamic wind turbine neighbor relationship network; determining the difference value between the disturbance state information corresponding to each wind turbine and the disturbance state estimation value, performing proportional-integral adjustment based on the difference value, and generating the collaborative anti-disturbance demand signal between adjacent wind turbines through a preset communication rule, wherein the disturbance state information is the actual disturbance state information of the wind turbine.

[0009] In one embodiment, the step of obtaining the disturbance state estimate of each wind turbine within the dynamic wind turbine neighbor relationship network based on the gust disturbance information and the dynamic wind turbine neighbor relationship network includes: The gust disturbance information corresponding to each wind turbine is broadcast to all neighboring wind turbines according to the dynamic wind turbine neighbor relationship network. The estimated value is calculated by weighting the gust disturbance information received by each wind turbine from all neighboring wind turbines. When the estimated values ​​of the disturbance state of all wind turbine units reach the preset convergence tolerance range, the weighted average estimated value is used as the estimated value of the disturbance state of each wind turbine unit in the dynamic wind turbine neighbor relationship network.

[0010] In one embodiment, the step of obtaining the preliminary adjustment amount of the disturbance rejection control parameters of each wind turbine based on the safety operation constraints of each wind turbine and the cooperative disturbance rejection requirement signal includes: The cooperative disturbance rejection demand signal is mapped to the safety operation constraints of each wind turbine to obtain the target torque response speed and target pitch angle change rate of the generator in each wind turbine. The current operating parameters are determined, and based on the difference between the target torque response speed and the target pitch angle change rate and the current operating parameters, the preliminary adjustment amount of the disturbance rejection control parameters of each wind turbine is obtained.

[0011] In one embodiment, the step of obtaining optimized control commands for each wind turbine that balance disturbance rejection and power generation based on the overall power scheduling target of the wind farm and the preliminary adjustment amount of the disturbance rejection control parameters includes: Receive the overall power scheduling target of the wind farm within the current scheduling cycle from the wind farm central controller; Based on the preliminary adjustment of the disturbance rejection control parameters of each wind turbine, the predicted power output of each wind turbine during the disturbance rejection operation is predicted. Based on the predicted power output and the total power command of the wind farm, with the optimization objectives of minimizing the total power deviation and the load fluctuation, the power reference values ​​of each wind turbine are redistributed through a distributed optimization algorithm. Based on the power reference value, optimized control commands are obtained for each wind turbine unit, taking into account both disturbance rejection and power generation.

[0012] In one embodiment, the step of reallocating the power reference values ​​of each wind turbine unit based on the predicted power output and the total power command of the wind farm, with the optimization objectives of minimizing the total power deviation and the load fluctuation, includes: Based on the predicted power output of each wind turbine and the current power reference value, optimization variables and a local optimization objective function including power deviation penalty term and load fluctuation penalty term are constructed, wherein the optimization variables are represented by local power adjustment amount; The gradient information and dual variable information of the power adjustment amount are determined based on the total power command of the wind farm and the local optimization objective function. Based on the optimization variables, the gradient information and the dual variable information are optimized using the alternating direction multiplier distributed optimization algorithm. With the goal of minimizing the total power deviation and the load fluctuation, the power reference values ​​of each wind turbine are redistributed.

[0013] In one embodiment, the regional wind farm multi-wind turbine distributed collaborative control method for resisting gusts further includes: Record the operating data, control commands, and load response data of each wind turbine unit during each gust event; Based on the operational data, the control commands, and the load response data of the key components, the anti-disturbance effect and power generation efficiency of different control strategies are analyzed using a machine learning model to obtain the analysis results. Based on the analysis results, adaptively adjust communication rules and / or security operation constraints.

[0014] Furthermore, to achieve the above objectives, this application also proposes a distributed collaborative control system for multiple wind turbine units in a regional wind farm to resist gusts. The distributed collaborative control system for multiple wind turbine units in a regional wind farm to resist gusts includes: The data sensing module is used to obtain real-time gust disturbance information of each wind turbine based on the operating status data of each wind turbine in the wind farm and meteorological sensor data. The distributed collaborative computing module is used to generate collaborative anti-interference demand signals between adjacent wind turbines based on the topological relationship between wind turbines and the gust disturbance information, through preset communication rules. The local optimization module is used to obtain the preliminary adjustment amount of the anti-disturbance control parameters of each wind turbine based on the safety operation constraints of each wind turbine and the cooperative anti-disturbance requirement signal. The global coordination module is used to obtain optimized control commands for each wind turbine unit that take into account both disturbance rejection and power generation, based on the overall power scheduling target of the wind farm and the preliminary adjustment amount of the disturbance rejection control parameters. The execution control module is used to adjust the actuator of the corresponding wind turbine according to the optimized control command, so as to realize the coordinated anti-gust control of the regional wind field.

[0015] Furthermore, to achieve the above objectives, this application also proposes a distributed collaborative control device for multiple wind turbine units in a regional wind farm to resist gusts. The distributed collaborative control device for multiple wind turbine units in a regional wind farm to resist gusts includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the distributed collaborative control method for multiple wind turbine units in a regional wind farm to resist gusts as described above.

[0016] In addition, to achieve the above objectives, the present invention also proposes a storage medium, which is a computer-readable storage medium, and stores a computer program on the storage medium. When the computer program is executed by a processor, it implements the steps of the regional wind farm multi-wind turbine anti-gust distributed collaborative control method described above.

[0017] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the regional wind farm multi-wind turbine anti-gust distributed collaborative control method described above.

[0018] This application provides a distributed collaborative control method for multiple wind turbines in a regional wind farm to resist gusts. Based on the operating status data of each turbine within the wind farm and meteorological sensor data, gust disturbance information is sensed. Based on the topological relationship between wind turbines and the gust disturbance information, a collaborative disturbance resistance demand signal is generated through preset communication rules. Based on the safety operation constraints of each turbine and the collaborative demand signal, preliminary adjustment amounts of the disturbance resistance control parameters are obtained. Based on the overall power scheduling target of the wind farm and the control parameter adjustment amounts, an optimized control command that balances disturbance resistance and power generation is obtained through a distributed optimization algorithm. The actuators are adjusted according to the optimized control command to achieve collaborative gust resistance control. Through this method, efficient distributed collaboration among wind farm turbines can be achieved, effectively resisting gust impacts while ensuring the stable output of the overall power generation of the wind farm. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating an embodiment of the distributed collaborative control method for multiple wind turbine units in a regional wind farm to resist gusts in this application. Figure 2This is a schematic diagram illustrating the iterative update of an embodiment of the distributed collaborative control method for multiple wind turbine units in a regional wind farm to resist gusts, as described in this application. Figure 3 This is a schematic diagram of the module structure of the distributed collaborative control system for multiple wind turbine units in a regional wind farm to resist gusts, as described in an embodiment of this application. Figure 4 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the distributed collaborative control method for multiple wind turbines in a regional wind farm to resist gusts in the embodiments of this application.

[0022] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0023] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0024] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0025] The main solution of this application embodiment is: to obtain the real-time gust disturbance information of each wind turbine based on the operating status data of each wind turbine in the wind farm and the meteorological sensor data; Based on the topological relationship between wind turbine units and the gust disturbance information, a cooperative anti-interference requirement signal between adjacent wind turbine units is generated through preset communication rules. Based on the safety operation constraints of each wind turbine and the cooperative disturbance rejection requirement signal, the preliminary adjustment amount of the disturbance rejection control parameters of each wind turbine is obtained; Based on the overall power scheduling target of the wind farm and the preliminary adjustment of the disturbance rejection control parameters, the optimized control command for each wind turbine is obtained, which takes into account both disturbance rejection and power generation. According to the optimized control command, the actuators of the corresponding wind turbine units are adjusted to achieve coordinated anti-gust control of the regional wind farm.

[0026] Currently, existing wind farm control technologies generally employ either a centralized architecture centered on a central controller or independent control strategies for each wind turbine. Centralized control relies on a central controller to collect data from the entire field and uniformly calculate control commands. This results in problems such as high communication bandwidth pressure, heavy computational burden, and poor system scalability, leading to a significant lag in response to sudden gusts and making it difficult to achieve rapid and accurate control. While the independent turbine control strategy avoids the central bottleneck, it lacks information exchange and coordination mechanisms between turbines. Each turbine reacts only based on its own perceived local wind conditions, unable to predict and coordinate responses to wake disturbances from upstream turbines. This causes conflicting or disconnected anti-disturbance actions between turbines, failing to effectively mitigate overall power and load fluctuations in the wind farm. In fact, the asynchronous control may amplify the negative impact of gusts, ultimately posing challenges to the safety and stability of wind farm operation.

[0027] This application provides a solution that senses gust disturbance information based on the operating status data of each wind turbine in a wind farm and meteorological sensor data; generates a collaborative disturbance rejection signal based on the topological relationship between wind turbines and the gust disturbance information through preset communication rules; obtains preliminary adjustment amounts for disturbance rejection control parameters based on the safety operation constraints of each turbine and the collaborative rejection signal; and obtains optimized control commands that balance disturbance rejection and power generation through a distributed optimization algorithm based on the overall power scheduling target of the wind farm and the control parameter adjustment amounts. The actuators are adjusted according to the optimized control commands to achieve collaborative gust control. Through this method, efficient distributed collaboration among wind farm turbines can be achieved, effectively resisting gust impacts while ensuring the stable output of the overall power generation of the wind farm.

[0028] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a regional wind farm multi-wind turbine anti-gust distributed collaborative control device, etc. This embodiment does not specifically limit it. The following uses a regional wind farm multi-wind turbine anti-gust distributed collaborative control device as an example to describe this embodiment and the following embodiments.

[0029] All actions involving the acquisition of signals, information, or data in this application are carried out in accordance with the relevant data protection laws and policies of the country where the application is located, and with the authorization of the owner of the relevant device.

[0030] This application provides a distributed collaborative control method for multiple wind turbine units in a regional wind farm to resist gusts, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the distributed collaborative control method for multiple wind turbine units in a regional wind farm to resist gusts, as described in this application.

[0031] In this embodiment, the distributed collaborative control method for multiple wind turbine units in a regional wind farm to resist gusts includes steps S10 to S50: Step S10: Based on the operating status data of each wind turbine in the wind farm and the meteorological sensor data, obtain the real-time gust disturbance information perceived by each wind turbine.

[0032] It should be noted that operational status data refers to key parameters reflecting the aerodynamic and transmission chain dynamics of the wind turbine, which are collected in real time by the wind turbine's main controller, including rotor speed, generator torque, and blade pitch angle. Meteorological sensor data refers to three-dimensional wind speed / direction information provided by a high-precision ultrasonic anemometer installed on the top of the nacelle or the tower, as well as vibration acceleration at key points on the nacelle and tower measured by accelerometers.

[0033] Understandably, time synchronization and filtering / denoising preprocessing are performed on operational status data and meteorological sensor data to eliminate the impact of measurement noise and different sampling rates. Subsequently, time-series correlation analysis or a state observer algorithm based on Kalman filtering is used to match and correlate the wind speed mutation sequence measured by the high-speed anemometer with the load response of key components of the unit. By analyzing the propagation lag and amplitude gain of the disturbance signal in time, real-time gust disturbance information reflecting the intensity, direction, duration, and equivalent disturbance energy exerted on the current unit is finally calculated, providing accurate input for subsequent collaborative decision-making.

[0034] In one feasible implementation, the step of obtaining the real-time gust disturbance information sensed by each wind turbine based on the operating status data of each wind turbine in the wind farm and meteorological sensor data includes: Based on the rotor speed, generator torque and blade pitch angle data collected by the main controller of the wind turbine in the wind farm, the operating status data reflecting the current aerodynamic load status of the wind turbine is obtained. Based on data from ultrasonic anemometers and accelerometers installed on the nacelle or tower, meteorological sensor data containing abrupt wind speed components and structural vibration components are obtained. Based on the operational status data and meteorological sensor data, time-series correlation analysis is used to obtain real-time gust disturbance information for each wind turbine, which characterizes the gust intensity, direction, and duration.

[0035] In practical implementation, the first step is to preprocess and synchronize the data from multi-source heterogeneous sensors. Consider a wind turbine generator set. At discrete time points The raw data collected includes: impeller speed Generator torque Pitch angle And longitudinal wind speed measured by an ultrasonic anemometer The tower's forward and backward vibration acceleration measured by accelerometers These data typically have different sampling frequencies and need to be unified to the same timestamp sequence through interpolation (such as linear interpolation) or resampling methods. The high-frequency measurement noise is filtered out using a low-pass filter (such as a Butterworth filter) to obtain a clean time series that can be used for analysis. , , , wait.

[0036] After preprocessing, the core task is to apply time-series correlation analysis to solve for gust disturbance information. First, wind speed sequences are calculated. Compared with load proxy sequences, such as drive train loads estimated from torque and speed. Or the tower bending moment obtained by integrating the vibration acceleration. In multiple lag times Normalized cross-correlation function (CCF) on:

[0037] in, and These are the mean of the sequences, and Let be the standard deviation. Lag time to reach maximum value This refers to the propagation delay of a gust from its detection by the anemometer to the point of causing a significant load response. Gust intensity can be quantified by the difference between the root mean square or maximum value of the wind speed sequence during the disturbance period and the background wind speed. Directional information can be determined by analyzing the lateral wind speed component or by combining it with changes in the nacelle's yaw angle to the wind. Duration... This can be estimated by identifying the length of consecutive time periods exceeding a preset threshold in the wind speed sequence. Finally, these parameters are encapsulated into a structure, which constitutes the generator unit. Real-time, quantified information on gust disturbances.

[0038] In one feasible implementation, the step of obtaining the real-time gust disturbance information sensed by each wind turbine unit through time-series correlation analysis based on the operating status data and meteorological sensor data includes: Calculate the cross-correlation coefficient and autocorrelation function of the operating status data and the meteorological sensor data within a preset time window, respectively. Determine the peak value and phase information corresponding to the cross-correlation coefficient and the autocorrelation function respectively, and determine the propagation path and sequence of gust disturbances based on the peak value and the phase information; Based on the propagation path and the sequence of events, the meteorological sensor data are weighted, fused, and corrected through time-series correlation analysis to obtain the real-time gust disturbance information perceived by each wind turbine.

[0039] In the specific implementation, firstly, within a preset time window T, the operating state data sequence characterizing the load state (such as the estimated value of the bending moment at the bottom of the tower) is calculated. ) and data sequences from various meteorological sensors (such as cabin wind speed) Acceleration in the middle of the tower The cross-correlation coefficient (CCF) between the sequences is calculated. Simultaneously, the autocorrelation function (ACF) of each sequence is also calculated. (The text then abruptly shifts to a seemingly unrelated topic: bending moment.) With cabin wind speed For example, its cross-correlation coefficient is calculated as follows:

[0040] Find the maximum absolute value (peak value) of each CCF and its corresponding time delay. The magnitude of this peak reflects the strength of the linear association between the two variables, while This indicates the delay in signal propagation. For example, if Leading and A positive value indicates that the gust is first captured by the nacelle anemometer and then transmitted to the bottom of the tower, generating a bending moment. By comparing the peak values ​​and time delays between all sensor pairs, the propagation path of the gust on the unit structure (e.g., anemometer → blade load → drive train torque → tower load) and their sequence can be clearly revealed.

[0041] Step S20: Based on the topological relationship between wind turbine units and the gust disturbance information, a cooperative anti-interference demand signal between adjacent wind turbine units is generated through preset communication rules.

[0042] It should be noted that the topological relationship between wind turbines refers to the network structure formed by the relative spatial layout of the turbines within a wind farm and their upstream and downstream relationships under the prevailing wind direction. This is typically abstracted mathematically using a directed graph from graph theory, where nodes represent wind turbines and directed edges represent the propagation paths of disturbances dominated by the wind direction. The cooperative disturbance mitigation demand signal generates instructions or requests containing the intensity, timing, and duration of suggested control actions based on the intensity of gust disturbances sensed by upstream turbines, the estimated propagation time, and the potential impact on downstream turbines.

[0043] Understandably, based on the wind farm layout and prevailing wind direction, the neighbor set for each turbine is predefined and stored. When any turbine calculates real-time gust disturbance information, its local controller immediately packages this information along with its own turbine number and timestamp, and transmits it in real-time to the controllers of all its directly downstream turbines via the on-site LAN or dedicated ring network, using multicast or point-to-point transmission, according to preset communication rules. This generates and sends out a coordinated disturbance rejection signal for the specific target.

[0044] In one feasible implementation, the step of generating a cooperative anti-interference demand signal between adjacent wind turbines based on the topological relationship between wind turbines and the gust disturbance information, through preset communication rules, includes: Based on the topological relationships between wind turbines and historical data of prevailing wind direction, a dynamic wind turbine neighbor relationship network is constructed with the goal of minimizing wake impact. Based on the gust disturbance information and the dynamic wind turbine neighbor relationship network, the disturbance state estimate of each wind turbine in the dynamic wind turbine neighbor relationship network is obtained. The difference between the disturbance state information corresponding to each wind turbine and the estimated disturbance state is determined. Proportional-integral adjustment is performed based on the difference. A cooperative anti-interference demand signal between adjacent wind turbines is generated through preset communication rules. The disturbance state information is the actual disturbance state information of the wind turbine.

[0045] In its implementation, the system first determines the current prevailing wind direction based on high-frequency (e.g., 1Hz) wind vane data from meteorological towers or nacelles. Combining the pre-stored geographical coordinate map of the wind farm, for each wind turbine... The wake influence range is calculated using a wake model. If located Within the wake influence area, and with If the angle between the line connecting the two points and the prevailing wind direction is less than a preset threshold (e.g., ±30°), then it is determined that... yes An upstream neighbor establishes a link in the network from point to directed edges Therefore, a dynamic directed graph of the entire wind farm is constructed. .

[0046] For each unit in the network Its disturbance state estimate It does not rely solely on its own sensors, but rather on all its upstream neighboring units. ( , for Real-time reports of gust disturbance information from upstream neighbor sets. (Such as the perturbation intensity) is obtained through weighted fusion:

[0047] Among them, weight It can be calculated based on the distance between units and the wind direction angle (e.g., the closer the distance and the more directly the wind is directed, the greater the weight). It is a disturbance from spread to The estimated time delay can be estimated by dividing the distance by the average wind speed.

[0048] Each unit The local controller will simultaneously calculate an actual disturbance state information. This can be an index that integrates its own load, vibration, and other signals. Then, the difference value is calculated:

[0049] Difference value Reflects The deviation between the actual disturbance received and the disturbance expected based on upstream information.

[0050] Difference value It is fed into a discrete-time PI controller:

[0051] in, and These are pre-tuned proportional and integral coefficients. It's the control cycle. The regulator's output. This is the core content of the cooperative interference immunity requirement signal. This signal indicates that... To coordinate with its upstream neighbors, adjustments need to be made to its own controller (such as the pitch system). Finally, the signal is encapsulated and sent according to preset communication rules (such as specific data frame formats and communication protocols). The controller may also send information to its downstream neighbors as feedforward information, thereby achieving closed-loop cooperative control based on model prediction and feedback correction.

[0052] In one feasible implementation, the step of obtaining the disturbance state estimate of each wind turbine within the dynamic wind turbine neighbor network based on the gust disturbance information and the dynamic wind turbine neighbor relationship network includes: The gust disturbance information corresponding to each wind turbine is broadcast to all neighboring wind turbines according to the dynamic wind turbine neighbor relationship network. The estimated value is calculated by weighting the gust disturbance information received by each wind turbine from all neighboring wind turbines. When the estimated values ​​of the disturbance state of all wind turbine units reach the preset convergence tolerance range, the weighted average estimated value is used as the estimated value of the disturbance state of each wind turbine unit in the dynamic wind turbine neighbor relationship network.

[0053] In practical implementation, after the system starts up, each wind turbine in the wind farm... The information of the gusts of wind disturbance that it perceives This serves as its initial local state value. Subsequently, each unit, based on the connection relationships defined by the current dynamic wind turbine neighbor relationship network, transmits its own disturbance information through the on-site communication network. It broadcasts to all its direct neighboring units. At the same time, each unit continuously receives disturbance information from all its neighboring units. In this way, each unit obtains a local dataset containing information about itself and its neighbors in each computing cycle.

[0054] After obtaining neighbor information, each unit It then begins executing the core iterative computation. Instead of directly using the raw values ​​it perceives, it updates its perturbation state estimate based on a weighted average formula. This formula can be expressed as:

[0055] in, yes In the The estimated value at the next iteration (initial value) That is ), yes Neighbor set, weight coefficient and It is pre-designed based on the network topology and meets the requirements. This ensures the stability of the iterative process.

[0056] All units should perform this update procedure simultaneously. (Refer to...) Figure 2 , Figure 2 This is a schematic diagram of the iterative update. After one iteration, the new estimated value for each unit... It is no longer merely its own local information, but incorporates information from its first-order neighbors. Then, the unit broadcasts the new estimate obtained in this iteration again, starting the next iteration. In the second iteration, because the neighbors also incorporate information from their neighbors, The estimated value This actually includes information within its two-hop range. This process repeats, and the information about the gust disturbance spreads through the network like ripples. The algorithm stops when it detects that the change in the estimated values ​​of all units in consecutive iterations is less than the preset convergence tolerance range. At this point, the final estimated value held by each unit is the disturbance state estimate.

[0057] Step S30: Based on the safety operation constraints of each wind turbine and the cooperative anti-disturbance requirement signal, obtain the preliminary adjustment amount of the anti-disturbance control parameters of each wind turbine.

[0058] It should be noted that safe operation constraints are the physical limits and operating procedures that wind turbine units must strictly adhere to during operation to ensure equipment safety. These typically include the safety limits for the mechanical loads of critical components, the operating range of the electrical system, the speed range of the wind turbine, and protection thresholds for abnormal conditions.

[0059] In practice, the process of obtaining the initial adjustment amount of the disturbance rejection control parameters for each wind turbine is completed in the main controller of each turbine. The controller internally contains a lookup table or mathematical model that includes various safety operation constraints. Upon receiving a coordinated disturbance rejection request signal, the controller first calculates, based on the real-time monitored turbine status, the maximum feasible adjustment range that satisfies the signal requirements without causing any critical parameters to exceed their safety limits using a constraint optimizer. This command value, after safety boundary trimming, is the initial adjustment amount of the disturbance rejection control parameters.

[0060] In one feasible implementation, the step of obtaining the preliminary adjustment amount of the disturbance rejection control parameters of each wind turbine based on the safety operation constraints of each wind turbine and the cooperative disturbance rejection requirement signal includes: The cooperative disturbance rejection demand signal is mapped to the safety operation constraints of each wind turbine to obtain the target torque response speed and target pitch angle change rate of the generator in each wind turbine. The current operating parameters are determined, and based on the difference between the target torque response speed and the target pitch angle change rate and the current operating parameters, the preliminary adjustment amount of the disturbance rejection control parameters of each wind turbine is obtained.

[0061] In practical implementation, the first step is to calculate the ideal dynamic performance that the generator system and pitch system should theoretically possess to achieve the best disturbance rejection effect, based on the content of the demand signal and the aerodynamic and structural models of the unit. These ideal performance parameters are the target torque response speed and the target pitch angle change rate. This mapping process ensures that the cooperative target can be accurately guided to the correct actuators.

[0062] Subsequently, the system collects current operating parameters in real time, especially the actual dynamic capabilities of the actuators. For example, due to hydraulic pressure or motor current limitations, the actual pitch angle change rate of the pitch system may not instantly reach the theoretical target value. The control system then calculates the difference between the target value and the current actual value. This difference is input into an optimizer or controller to calculate the most feasible adjustment command, i.e., the initial adjustment amount of the disturbance rejection control parameters. This could be a fine-tuning of the PID parameters in the torque control loop, or a temporary modification of the pitch rate limit value.

[0063] Step S40: Based on the overall power scheduling target of the wind farm and the preliminary adjustment amount of the disturbance rejection control parameters, obtain the optimized control command for each wind turbine that takes into account both disturbance rejection and power generation.

[0064] It should be noted that the overall power dispatch target of a wind farm refers to the total active power output command issued by the power grid or the central control system of the wind farm, which requires the entire wind farm to maintain or achieve.

[0065] In practice, the generation of optimized control commands for each wind turbine, which balances disturbance rejection and power generation, is completed in the collaborative controller at the wind farm level. This controller receives the preliminary adjustment of disturbance rejection control parameters from all wind turbines and inputs them into an optimization model along with the overall power scheduling target of the wind farm issued by the superior. The model takes the minimum total power deviation and the optimal disturbance rejection effect as the comprehensive objectives. Based on the wake effect and performance differences between wind turbines, it recalculates and allocates the final power reference value or control parameters for each wind turbine, thereby generating optimized control commands. This allows some less affected units to appropriately increase power generation to compensate for the power loss of units that actively reduce load due to disturbance rejection, ultimately ensuring the stability of the total output of the entire wind farm while effectively suppressing disturbances.

[0066] In one feasible implementation, the step of obtaining optimized control commands for each wind turbine that balance disturbance rejection and power generation based on the overall power scheduling target of the wind farm and the preliminary adjustment amount of the disturbance rejection control parameters includes: Receive the overall power scheduling target of the wind farm within the current scheduling cycle from the wind farm central controller; Based on the preliminary adjustment of the disturbance rejection control parameters of each wind turbine, the predicted power output of each wind turbine during the disturbance rejection operation is predicted. Based on the predicted power output and the total power command of the wind farm, with the optimization objectives of minimizing the total power deviation and the load fluctuation, the power reference values ​​of each wind turbine are redistributed through a distributed optimization algorithm. Based on the power reference value, optimized control commands are obtained for each wind turbine unit, taking into account both disturbance rejection and power generation.

[0067] In practice, the wind farm central controller first receives the overall power scheduling target of the wind farm. Meanwhile, each wind turbine, based on its own disturbance rejection control parameters, makes a preliminary adjustment and, through its built-in aerodynamic-generator coupling model, quickly estimates its predicted power output in the next time period after implementing the adjustment. By summing the predicted outputs of all wind turbines, the total predicted power generation of the entire site can be obtained.

[0068] Typically, because disturbance rejection actions often sacrifice some power generation efficiency, it can lead to... This results in a power deficit.

[0069] To address this issue, the system establishes an optimization problem with the objectives of minimizing total power deviation and minimizing the fluctuation of critical loads across the entire field. Its objective function can be simplified as follows:

[0070]

[0071] in, It is a problem to be solved and assigned to the wind turbine. The power reference value, This refers to the load changes that may result from implementing the new power reference value. It is a weighting coefficient used to balance power generation accuracy and structural safety.

[0072] Due to the large scale of the wind farm and the large number of wind turbines, a distributed optimization algorithm is very effective in solving this problem. Each wind turbine only needs to communicate with a small number of neighboring turbines, exchanging their respective... and preliminary The proposal, after multiple iterations, ultimately included all wind turbines. It will converge to a set of optimal solutions. This set of solutions guarantees accurate tracking of the total power across the entire field. Furthermore, by intelligently allocating power deficits among all wind turbines, the turbines with the least load fluctuations can undertake more power generation tasks, while the turbines experiencing significant disturbances can appropriately reduce their power generation load, thereby generating truly optimized control commands that balance disturbance rejection and power generation.

[0073] In one feasible implementation, the step of reallocating the power reference values ​​of each wind turbine unit based on the predicted power output and the total power command of the wind farm, with the optimization objectives of minimizing the total power deviation and the load fluctuation, through a distributed optimization algorithm includes: Based on the predicted power output of each wind turbine and the current power reference value, optimization variables and a local optimization objective function including power deviation penalty term and load fluctuation penalty term are constructed, wherein the optimization variables are represented by local power adjustment amount; The gradient information and dual variable information of the power adjustment amount are determined based on the total power command of the wind farm and the local optimization objective function. Based on the optimization variables, the gradient information and the dual variable information are optimized using the alternating direction multiplier distributed optimization algorithm. With the goal of minimizing the total power deviation and the load fluctuation, the power reference values ​​of each wind turbine are redistributed.

[0074] In practical implementation, firstly, each wind turbine Based on its own predicted power output and current power reference value We construct its local optimization objective function. A typical function form is as follows:

[0075] in, It is an optimization variable (local power adjustment amount). This is the candidate for the new power reference value. The first term is the power deviation penalty term, and the second term... It is a load fluctuation penalty item. These are weighting coefficients. The function estimates the impact of power adjustments on critical loads.

[0076] Meanwhile, the global constraint that the wind farm needs to satisfy is that the sum of the new power reference values ​​of all wind turbines equals the total power command of the wind farm. ,Right now:

[0077] In each iteration, each wind turbine executes two steps in parallel. First, it utilizes the dual variable of the current iteration. Solve the local subproblem, i.e., minimize the augmented Lagrange function. To update the local The solution process requires calculating the gradient information of the function. And find the one that makes it zero. Then, all wind turbines broadcast their locally calculated new power adjustments to a central coordinator, which updates the dual variable information based on the global power deviation. The formula is expressed as:

[0078] in It is a penalty parameter, and will the new The broadcast was relayed to each wind turbine.

[0079] The wind turbines and coordinator repeatedly and alternately perform the above steps until all wind turbines are in operation. When λ no longer changes significantly, the algorithm converges. At this point, the optimal local power adjustment for each wind turbine is determined. The final power reference value was determined and reassigned. This achieves the synergistic minimization of total power deviation and load fluctuation while meeting the total power requirements of the entire field.

[0080] Step S50: Adjust the actuator of the corresponding wind turbine according to the optimized control command to achieve coordinated anti-gust control of the regional wind field.

[0081] In practical implementation, after receiving the optimized control command from the upper-level coordinating controller, the wind turbine's main controller compares it with the turbine's current actual power, speed, and other state variables. Subsequently, the controller uses its built-in PID controller to calculate the precise action signals required from the pitch and torque control actuators: the pitch system adjusts the pitch angle setpoint according to load smoothing and power limiting strategies, while the converter adjusts the generator torque accordingly, jointly driving the turbine's actual power output to track the new... By executing this process synchronously across all units, coordinated gust control of the regional wind farm is achieved, which means mitigating gust impact and reducing mechanical load at the individual unit level, while maintaining stable total power at the wind farm level.

[0082] In one feasible implementation, the regional wind farm multi-wind turbine distributed collaborative control method for resisting gusts further includes: Record the operating data, control commands, and load response data of each wind turbine unit during each gust event; Based on the operational data, the control commands, and the load response data of the key components, the anti-disturbance effect and power generation efficiency of different control strategies are analyzed using a machine learning model to obtain the analysis results. Based on the analysis results, adaptively adjust communication rules and / or security operation constraints.

[0083] In practical implementation, an offline, continuously learning closed-loop optimization layer can be introduced. A complete log is created for each gust event, recording all operational data at the time of the event, all control commands used, and the resulting load response data of key components. This data is stored in a historical database, forming a continuously expanding experience base.

[0084] Subsequently, machine learning models are used to analyze this data. A supervised learning model can be trained to predict the effectiveness of a certain control strategy. The model's input features can be gust characteristics and control parameters, and the output is an evaluation metric, such as load reduction effect.

[0085] and power generation efficiency loss:

[0086] By analyzing a large amount of event data, hidden patterns were summarized, such as how appropriately increasing the load weight under specific wind directions and turbulence intensities can significantly reduce tower load with only minor power generation losses. Based on this analysis, the system can adaptively adjust the parameters of the control framework. For example, if the model finds that the coordinated response speed under the current communication rules is too slow, causing the windward-side units to fail to provide timely warnings to the downstream-side units, the communication frequency between turbines in specific directions can be dynamically increased or more important state variables can be introduced for exchange. Simultaneously, regarding safety operation constraints, the system can dynamically relax the power change rate limits under certain non-critical operating conditions based on the safety margin of historical load data, thereby improving the ability to track grid dispatch while keeping risks under control.

[0087] This embodiment provides a distributed collaborative control method for multiple wind turbines in a regional wind farm to resist gusts. Based on the operating status data of each turbine and meteorological sensor data within the wind farm, gust disturbance information is detected. Based on the topological relationship between wind turbines and the gust disturbance information, a collaborative disturbance resistance demand signal is generated through preset communication rules. Based on the safety operation constraints of each turbine and the collaborative demand signal, preliminary adjustment amounts for disturbance resistance control parameters are obtained. Based on the overall power scheduling target of the wind farm and the control parameter adjustment amounts, an optimized control command that balances disturbance resistance and power generation is obtained through a distributed optimization algorithm. The actuators are adjusted according to the optimized control command to achieve collaborative gust resistance control. Through this method, efficient distributed collaboration among wind farm turbines can be achieved, effectively resisting gust impacts while ensuring the stable output of the overall power generation of the wind farm.

[0088] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the distributed collaborative control method for multiple wind turbines in regional wind farms to resist gusts. Any simple modifications based on this technical concept are within the protection scope of this application.

[0089] This application also provides a distributed collaborative control system for multiple wind turbines in a regional wind farm to resist gusts. Please refer to [link / reference]. Figure 3 The regional wind farm multi-wind turbine distributed collaborative control system for resisting gusts includes: The data sensing module 10 is used to obtain the real-time gust disturbance information of each wind turbine based on the operating status data of each wind turbine in the wind farm and the meteorological sensor data. The distributed collaborative computing module 20 is used to generate a collaborative anti-interference demand signal between adjacent wind turbines based on the topological relationship between wind turbines and the gust disturbance information, through preset communication rules. The local optimization module 30 is used to obtain the preliminary adjustment amount of the anti-disturbance control parameters of each wind turbine based on the safety operation constraints of each wind turbine and the cooperative anti-disturbance requirement signal. The global coordination module 40 is used to obtain optimized control commands for each wind turbine unit that take into account both disturbance rejection and power generation, based on the overall power scheduling target of the wind farm and the preliminary adjustment amount of the disturbance rejection control parameters. The execution control module 50 is used to adjust the actuator of the corresponding wind turbine according to the optimized control command, so as to realize the coordinated anti-gust control of the regional wind field.

[0090] In one feasible implementation, the data sensing module 10 is further used to obtain operating status data reflecting the current aerodynamic load state of the wind turbine based on the rotor speed, generator torque and pitch angle data collected by the main controller of the wind turbine in the wind farm. Based on data from ultrasonic anemometers and accelerometers installed on the nacelle or tower, meteorological sensor data containing abrupt wind speed components and structural vibration components are obtained. Based on the operational status data and meteorological sensor data, time-series correlation analysis is used to obtain real-time gust disturbance information for each wind turbine, which characterizes the gust intensity, direction, and duration.

[0091] In one feasible implementation, the data sensing module 10 is further configured to calculate the cross-correlation coefficient and autocorrelation function of the operating status data and the meteorological sensor data within a preset time window, respectively. Determine the peak value and phase information corresponding to the cross-correlation coefficient and the autocorrelation function respectively, and determine the propagation path and sequence of gust disturbances based on the peak value and the phase information; Based on the propagation path and the sequence of events, the meteorological sensor data are weighted, fused, and corrected through time-series correlation analysis to obtain the real-time gust disturbance information perceived by each wind turbine.

[0092] In one feasible implementation, the distributed collaborative computing module 20 is also used to construct a dynamic wind turbine neighbor relationship network with the goal of minimizing wake effects, based on the topological relationship between wind turbines and historical data of the prevailing wind direction. Based on the gust disturbance information and the dynamic wind turbine neighbor relationship network, the disturbance state estimate of each wind turbine in the dynamic wind turbine neighbor relationship network is obtained. The difference between the disturbance state information corresponding to each wind turbine and the estimated disturbance state is determined. Proportional-integral adjustment is performed based on the difference. A cooperative anti-interference demand signal between adjacent wind turbines is generated through preset communication rules. The disturbance state information is the actual disturbance state information of the wind turbine.

[0093] In one feasible implementation, the distributed collaborative computing module 20 is further configured to broadcast the gust disturbance information corresponding to each wind turbine to all neighboring wind turbines according to the dynamic wind turbine neighbor relationship network. The estimated value is calculated by weighting the gust disturbance information received by each wind turbine from all neighboring wind turbines. When the estimated values ​​of the disturbance state of all wind turbine units reach the preset convergence tolerance range, the weighted average estimated value is used as the estimated value of the disturbance state of each wind turbine unit in the dynamic wind turbine neighbor relationship network.

[0094] In one feasible implementation, the local optimization module 30 is further configured to map the cooperative anti-disturbance demand signal to the safety operation constraints of each wind turbine, thereby obtaining the target torque response speed and target pitch angle change rate of the generator in each wind turbine. The current operating parameters are determined, and based on the difference between the target torque response speed and the target pitch angle change rate and the current operating parameters, the preliminary adjustment amount of the disturbance rejection control parameters of each wind turbine is obtained.

[0095] In one feasible implementation, the global coordination module 40 is further configured to receive the overall power scheduling target of the wind farm within the current scheduling period issued by the wind farm central controller. Based on the preliminary adjustment of the disturbance rejection control parameters of each wind turbine, the predicted power output of each wind turbine during the disturbance rejection operation is predicted. Based on the predicted power output and the total power command of the wind farm, with the optimization objectives of minimizing the total power deviation and the load fluctuation, the power reference values ​​of each wind turbine are redistributed through a distributed optimization algorithm. Based on the power reference value, optimized control commands are obtained for each wind turbine unit, taking into account both disturbance rejection and power generation.

[0096] In one feasible implementation, the global coordination module 40 is further configured to construct optimization variables and a local optimization objective function including power deviation penalty term and load fluctuation penalty term based on the predicted power output of each wind turbine and the current power reference value, wherein the optimization variables are represented by local power adjustment amount; The gradient information and dual variable information of the power adjustment amount are determined based on the total power command of the wind farm and the local optimization objective function. Based on the optimization variables, the gradient information and the dual variable information are optimized using the alternating direction multiplier distributed optimization algorithm. With the goal of minimizing the total power deviation and the load fluctuation, the power reference values ​​of each wind turbine are redistributed.

[0097] In one feasible implementation, the self-learning module 60 is also used to record the operating data, control commands and load response data of each wind turbine in each gust event. Based on the operational data, the control commands, and the load response data of the key components, the anti-disturbance effect and power generation efficiency of different control strategies are analyzed using a machine learning model to obtain the analysis results. Based on the analysis results, adaptively adjust communication rules and / or security operation constraints.

[0098] The regional wind farm multi-wind turbine distributed collaborative control system for gust resistance provided in this application adopts the regional wind farm multi-wind turbine distributed collaborative control method in the above embodiments, which can solve the technical problems of gust response lag and lack of coordination between units. Compared with the prior art, the beneficial effects of the regional wind farm multi-wind turbine distributed collaborative control system for gust resistance provided in this application are the same as the beneficial effects of the regional wind farm multi-wind turbine distributed collaborative control method for gust resistance provided in the above embodiments, and other technical features in the regional wind farm multi-wind turbine distributed collaborative control system for gust resistance are the same as those disclosed in the above embodiments, and will not be repeated here.

[0099] This application provides a distributed collaborative control device for multiple wind turbines in a regional wind farm to resist gusts. The distributed collaborative control device for multiple wind turbines in a regional wind farm to resist gusts includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the distributed collaborative control method for multiple wind turbines in a regional wind farm to resist gusts in the first embodiment described above.

[0100] The following is for reference. Figure 4 This document illustrates a structural schematic diagram of a distributed collaborative control device for multiple wind turbine units in a regional wind farm, suitable for implementing embodiments of this application. The distributed collaborative control device for multiple wind turbine units in a regional wind farm, as described in this application, may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), and vehicle terminals (e.g., vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 4 The distributed collaborative control device for multiple wind turbines in a regional wind farm shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments in this application.

[0101] like Figure 4 As shown, the regional wind farm multi-wind turbine anti-gust distributed collaborative control device may include a processing unit 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to the program stored in ROM (Read Only Memory) 1002 or the program loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the regional wind farm multi-wind turbine anti-gust distributed collaborative control device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, LCDs (Liquid Crystal Displays), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the regional wind farm multi-wind turbine anti-gust distributed collaborative control equipment to wirelessly or wiredly communicate with other devices to exchange data. Although the figure shows a regional wind farm multi-wind turbine anti-gust distributed collaborative control equipment with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0102] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0103] The regional wind farm multi-wind turbine distributed collaborative control device provided in this application adopts the regional wind farm multi-wind turbine distributed collaborative control method in the above embodiments, which can solve the technical problem of regional wind farm multi-wind turbine distributed collaborative control. Compared with the prior art, the beneficial effects of the regional wind farm multi-wind turbine distributed collaborative control device provided in this application are the same as the beneficial effects of the regional wind farm multi-wind turbine distributed collaborative control method provided in the above embodiments, and other technical features in the regional wind farm multi-wind turbine distributed collaborative control device are the same as the features disclosed in the previous embodiment method, and will not be repeated here.

[0104] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0105] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0106] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the regional wind farm multi-wind turbine anti-gust distributed cooperative control method in the above embodiments.

[0107] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash Memory), optical fibers, CD-ROM (CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0108] The aforementioned computer-readable storage medium may be included in the regional wind farm multi-wind turbine anti-gust distributed collaborative control equipment; or it may exist independently and not be assembled into the regional wind farm multi-wind turbine anti-gust distributed collaborative control equipment.

[0109] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by the regional wind farm multi-wind turbine anti-gust distributed collaborative control device, the regional wind farm multi-wind turbine anti-gust distributed collaborative control device obtains the real-time gust disturbance information perceived by each wind turbine based on the operating status data of each wind turbine in the wind farm and the meteorological sensor data. Based on the topological relationship between wind turbine units and the gust disturbance information, a cooperative anti-interference requirement signal between adjacent wind turbine units is generated through preset communication rules. Based on the safety operation constraints of each wind turbine and the cooperative disturbance rejection requirement signal, the preliminary adjustment amount of the disturbance rejection control parameters of each wind turbine is obtained; Based on the overall power scheduling target of the wind farm and the preliminary adjustment of the disturbance rejection control parameters, the optimized control command for each wind turbine is obtained, which takes into account both disturbance rejection and power generation. According to the optimized control command, the actuators of the corresponding wind turbine units are adjusted to achieve coordinated anti-gust control of the regional wind farm.

[0110] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LAN (Local Area Network) or WAN (Wide Area Network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0111] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0112] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0113] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described distributed collaborative control method for multiple wind turbines in a regional wind farm against gusts. This method can solve the technical problem of distributed collaborative control for multiple wind turbines in a regional wind farm against gusts. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the distributed collaborative control method for multiple wind turbines in a regional wind farm against gusts provided in the above embodiments, and will not be elaborated upon here.

[0114] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the regional wind farm multi-wind turbine anti-gust distributed collaborative control method as described above.

[0115] The computer program product provided in this application can solve the technical problem of distributed collaborative control of multiple wind turbines in a regional wind farm against gusts. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the distributed collaborative control method for multiple wind turbines in a regional wind farm against gusts provided in the above embodiments, and will not be repeated here.

[0116] The above are only some embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for distributed collaborative control of multiple wind turbines against gust in a regional wind field, characterized in that, The regional wind field multi-wind turbine gust distributed collaborative control method comprises: According to the operation state data and meteorological sensor data of each wind turbine in the wind farm, gust disturbance information perceived by each wind turbine in real time is obtained; Based on the topological relationship between wind turbines and the gust disturbance information, a collaborative anti-disturbance demand signal between adjacent wind turbines is generated through a preset communication rule; According to the safety operation constraints of each wind turbine and the collaborative anti-disturbance demand signal, a preliminary adjustment amount of the anti-disturbance control parameter of each wind turbine is obtained; Based on the overall power scheduling target of the wind farm and the preliminary adjustment amount of the anti-disturbance control parameter, an optimized control instruction of each wind turbine considering anti-disturbance and power generation is obtained; According to the optimized control instruction, the actuator of the corresponding wind turbine is adjusted to realize the collaborative anti-gust control of the regional wind farm.

2. The method of claim 1, wherein, The step of obtaining the gust disturbance information perceived by each wind turbine in real time according to the operation state data and meteorological sensor data of each wind turbine in the wind farm comprises: According to the rotor speed, generator torque and pitch angle data collected by the main controller of the wind turbine in the wind farm, operation state data reflecting the current aerodynamic load state of the wind turbine is obtained; Based on the ultrasonic anemometer and accelerometer data installed on the nacelle or tower, meteorological sensor data containing wind speed sudden change component and structure vibration component is obtained; According to the operation state data and meteorological sensor data, through time series correlation analysis, gust disturbance information perceived by each wind turbine in real time is obtained, and the gust disturbance information represents the gust intensity, direction and duration.

3. The method of claim 2, wherein, The step of obtaining the gust disturbance information perceived by each wind turbine in real time according to the operation state data and meteorological sensor data through time series correlation analysis comprises: The cross-correlation coefficient and the autocorrelation function of the operation state data and the meteorological sensor data in a preset time window are calculated respectively; The peak value and phase information corresponding to the cross-correlation coefficient and the autocorrelation function are determined, and the propagation path and sequence of the gust disturbance are determined according to the peak value and the phase information; Based on the propagation path and the sequence, through time series correlation analysis, the meteorological sensor data is weighted and fused and time series corrected to obtain the gust disturbance information perceived by each wind turbine in real time.

4. The method of claim 1, wherein, The step of generating a collaborative anti-disturbance demand signal between adjacent wind turbines based on the topological relationship between wind turbines and the gust disturbance information through a preset communication rule comprises: Based on the topological relationship between wind turbines and the historical data of the main wind direction, a dynamic wind turbine neighbor relationship network aiming at minimizing the wake effect is constructed; Based on the gust disturbance information and the dynamic wind turbine neighbor relationship network, a disturbance state estimation value of each wind turbine in the dynamic wind turbine neighbor relationship network is obtained; The difference value between the disturbance state information of each wind turbine and the disturbance state estimation value is determined, the difference value is adjusted by proportion-integral, and a collaborative anti-disturbance demand signal between adjacent wind turbines is generated through a preset communication rule, and the disturbance state information is the actual disturbance state information of the wind turbine.

5. The method of claim 4, wherein, The step of obtaining the disturbance state estimation value of each wind turbine in the dynamic wind turbine neighbor relationship network based on the gust disturbance information and the dynamic wind turbine neighbor relationship network comprises: broadcasting the gust disturbance information corresponding to each wind turbine to all neighbor wind turbines according to the dynamic wind turbine neighbor relationship network; calculating the estimation value after weighted averaging of the gust disturbance information corresponding to each wind turbine according to the gust disturbance information received by each wind turbine from all neighbor wind turbines; when the estimation value of all wind turbines to the disturbance state reaches a preset convergence tolerance range, taking the weighted average estimation value as the disturbance state estimation value of each wind turbine in the dynamic wind turbine neighbor relationship network.

6. The method of claim 1, wherein, The step of obtaining the preliminary adjustment amount of the anti-disturbance control parameter of each wind turbine according to the safety operation constraint of each wind turbine and the collaborative anti-disturbance demand signal comprises: mapping the collaborative anti-disturbance demand signal into the safety operation constraint of each wind turbine to obtain the target torque response speed and target pitch angle change rate of the generator in each wind turbine; determining the current working condition parameter, and obtaining the preliminary adjustment amount of the anti-disturbance control parameter of each wind turbine based on the difference between the target torque response speed and target pitch angle change rate and the current working condition parameter.

7. The method of claim 1, wherein, The step of obtaining the optimization control instruction of each wind turbine considering anti-disturbance and power generation based on the overall power scheduling target of the wind farm and the preliminary adjustment amount of the anti-disturbance control parameter comprises: receiving the overall power scheduling target of the wind farm in the current scheduling period issued by the central controller of the wind farm; predicting the predicted power output of each wind turbine during the anti-disturbance action according to the preliminary adjustment amount of the anti-disturbance control parameter of each wind turbine; redistributing the power reference value of each wind turbine by a distributed optimization algorithm based on the predicted power output and the total power instruction of the wind farm, with the minimum total power deviation and the minimum load fluctuation as the optimization target; obtaining the optimization control instruction of each wind turbine considering anti-disturbance and power generation according to the power reference value.

8. The method of claim 7, wherein, The step of redistributing the power reference value of each wind turbine by a distributed optimization algorithm based on the predicted power output and the total power instruction of the wind farm, with the minimum total power deviation and the minimum load fluctuation as the optimization target comprises: constructing optimization variables and local optimization objective functions containing power deviation penalty terms and load fluctuation penalty terms according to the predicted power output of each wind turbine and the current power reference value, wherein the optimization variables are represented by local power adjustment amounts; determining gradient information and dual variable information of the power adjustment amount based on the total power instruction of the wind farm and the local optimization objective function; redistributing the power reference value of each wind turbine by an alternating direction multiplier distributed optimization algorithm based on the optimization variables, the gradient information and the dual variable information, with the minimum total power deviation and the minimum load fluctuation as the optimization target.

9. The method of claim 1, wherein, The regional wind farm multi-wind turbine anti-gust distributed collaborative control method further comprises: recording the operation data, control instruction and key component load response data of each wind turbine in each gust event; Based on the operation data, the control instruction and the key component load response data, an anti-interference effect and power generation efficiency of different control strategies are analyzed by a machine learning model to obtain an analysis result; According to the analysis result, a communication rule and / or a safe operation constraint are adaptively adjusted.

10. A regional wind farm multi-wind turbine gust load mitigation distributed cooperative control system, characterized by, The regional wind farm multi-wind turbine anti-gust distributed collaborative control system comprises: A data sensing module is configured to obtain gust disturbance information sensed by each wind turbine in real time according to operation state data and meteorological sensor data of each wind turbine in the wind farm; A distributed collaborative calculation module is configured to generate collaborative anti-interference demand signals between adjacent wind turbines based on a topological relationship between the wind turbines and the gust disturbance information through a preset communication rule; A local optimization module is configured to obtain a preliminary adjustment amount of anti-interference control parameters of each wind turbine according to safe operation constraints of each wind turbine and the collaborative anti-interference demand signals; A global coordination module is configured to obtain optimized control instructions of each wind turbine considering anti-interference and power generation based on a whole wind farm power dispatching target and the preliminary adjustment amount of the anti-interference control parameters; An execution control module is configured to adjust an execution mechanism of a corresponding wind turbine according to the optimized control instructions to realize collaborative anti-gust control of the regional wind farm.

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