Full-scene anti-gust cooperative control method and system

Abstract

The invention discloses a full-scene anti-gust cooperative control method and system, and relates to the technical field of wind power dispatching, and the method comprises the steps: determining a current wind speed and a predicted wind speed corresponding to a wind speed change rate based on a wind speed prediction model; based on the current wind speed and the predicted wind speed, the current gust working condition is determined, and the current gust working condition is any one of a common gust working condition, a strong gust working condition and an extreme gust working condition; based on the current gust working condition, a corresponding cooperative control strategy is determined, and the cooperative control strategy comprises at least one of an attitude adaptive control strategy, a cluster output distribution control strategy and a protection control strategy; based on the cooperative control strategy, key control parameters of all wind generating sets in the target wind power plant under the current gust working condition are adjusted. By means of the mode, the full working conditions of conventional gust, strong gust and typhoon can be covered, a full-chain cooperative control system of single-machine response, cluster cooperation and extreme protection early warning is formed, and anti-gust cooperative control in a full scene is achieved.

Description

Technical Field

[0001] This application relates to the field of wind power dispatching technology, and in particular to a method and system for all-scenario anti-gust collaborative control. Background Technology

[0002] Currently, the wind power industry is gradually transforming from "distributed operation of individual units" to "clustered management of multiple units." On the one hand, the number of units in a single wind farm generally reaches more than 10, and the geographical connections between wind farms are increasing (such as onshore wind power bases and offshore wind power clusters), requiring cluster collaboration to maximize overall efficiency. On the other hand, the grid's requirements for the stability of wind power output are increasing, and the impact of complex wind conditions such as gusts and typhoons on cluster operation is amplified. The traditional independent control mode of individual units does not consider the wake effect between units, resulting in poor overall power output stability. Downstream units are further aggravated by the combined effects of upstream wake and gusts, making it unable to meet the needs of large-scale and high-stability operation.

[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide a method and system for all-scenario anti-gust collaborative control, which aims to solve the technical problems that existing technologies are difficult to apply to complex wind conditions, have poor power output stability, and affect power generation efficiency.

[0005] To achieve the above objectives, this application provides a method for all-scenario anti-gust collaborative control, the method comprising: Based on the wind speed prediction model, determine the predicted wind speed corresponding to the current wind speed and the rate of change of wind speed; Based on the current wind speed and the predicted wind speed, the current gust condition is determined, which is any one of the following: ordinary gust condition, strong gust condition, and extreme gust condition. Based on the current gust conditions, a corresponding collaborative control strategy is determined. The collaborative control strategy includes at least one of the following: attitude adaptive control strategy, cluster power distribution control strategy, and protection control strategy. Based on the aforementioned collaborative control strategy, the key control parameters of each wind turbine generator in the target wind farm under the current gust wind conditions are adjusted.

[0006] In one embodiment, the key control parameters include attitude parameters; The step of adjusting the key control parameters of each wind turbine generator in the target wind farm under the current gust wind condition based on the cooperative control strategy includes: When an attitude adaptation strategy exists in the cooperative control strategy, the attitude correction value of each wind turbine in the target wind farm is calculated under the current gust wind condition. Based on the attitude correction values ​​of each wind turbine generator in the target wind farm under the current gust wind conditions, the attitude parameters of each wind turbine generator in the target wind farm are adjusted.

[0007] In one embodiment, the step of calculating the attitude correction value of each wind turbine in the target wind farm under the current gust wind condition includes: Based on the current gust conditions, determine the corresponding pitch weight, yaw weight, and torque weight in the weight mapping table. A dynamic load allocation weight matrix is ​​generated based on the pitch weight, the yaw weight, and the torque weight. Based on the dynamic load allocation weight matrix, the parameter correction strategy for the attitude parameters is determined. The attitude parameters include at least the blade windward angle, the nacelle windward direction, and the motor torque. Based on the parameter correction strategy, the attitude correction value of each wind turbine in the target wind farm under the current gust wind condition is determined.

[0008] In one embodiment, after the step of adjusting the attitude parameters of each wind turbine in the target wind farm based on the attitude correction values ​​of each wind turbine in the target wind farm under the current gust wind conditions, the method further includes: Acquire attitude response data of each wind turbine generator in the target wind farm under the current gust wind condition. The attitude response data includes at least pitch response data, yaw response data, and torque response data. Based on the attitude response data, a Lyapunov function is constructed; Based on the Lyapunov function, the stability index is calculated; When the stability index meets the preset stability conditions, the attitude parameters of each wind turbine in the target wind farm under the current gust condition will remain unchanged. When the stability index does not meet the preset stability conditions, the pitch weight, yaw weight and torque weight are adjusted based on the emergency adjustment value, and the process returns to the step of generating a dynamic load allocation weight matrix based on the pitch weight, yaw weight and torque weight.

[0009] In one embodiment, the key control parameter includes the power generation frequency; The step of adjusting the key control parameters of each wind turbine generator in the target wind farm under the current gust wind condition based on the cooperative control strategy includes: When a cluster output allocation control strategy exists in the collaborative control strategy, the output efficiency of the wind turbine generator set, the cluster collaboration coefficient, the fatigue damage cost of the corresponding downstream unit of the wind turbine generator set, and the correspondence between the fatigue damage cost coefficient and the revenue are obtained. Based on the game optimization objective, game constraints, and the corresponding relationship, the optimization equation is determined. The game optimization objective includes maximizing power stability and minimizing the fatigue damage cost of downstream units. The game constraints include rated power constraints, wind speed safety threshold constraints, and control threshold constraints. Solve the optimization equation to determine the target output efficiency of each wind turbine in the target wind farm; Based on the target output efficiency of each wind turbine in the target wind farm, the power generation frequency of each wind turbine in the target wind farm is adjusted under the current gust wind conditions.

[0010] In one embodiment, before the steps of obtaining the power output efficiency of the wind turbine generator set, the cluster coordination coefficient, the fatigue damage cost of the corresponding downstream unit of the wind turbine generator set, and the correspondence between the fatigue damage cost coefficient and the revenue, the method further includes: Based on the sensing data, terrain data, and historical wake data of each wind turbine in the target wind farm under the current gust conditions, a digital twin prediction model of the target wind farm is constructed. Based on the digital twin prediction model of the target wind farm, the propagation path and intensity attenuation index of gusts are predicted. Based on the gust propagation path and the intensity attenuation index, identify the impact load superposition risk area and impact load risk area of ​​each wind turbine corresponding to the downstream unit. Based on the impact load superposition risk area and impact load risk area of ​​each wind turbine corresponding to the downstream unit, the fatigue damage cost coefficient of each wind turbine corresponding to the downstream unit is determined.

[0011] In one embodiment, the key control parameter includes the blade pitch angle; The step of adjusting the key control parameters of each wind turbine generator in the target wind farm under the current gust wind condition based on the cooperative control strategy includes: When a protective control strategy exists in the collaborative control strategy, multi-source typhoon data is collected. The multi-source typhoon data includes at least typhoon cloud system morphology data from satellite cloud images, pressure gradient data from ground bar stations, and wind speed profile data from coastal meteorological radar. Based on the multi-source typhoon data, a typhoon evolution model is generated; Based on the typhoon evolution model, the predicted typhoon path and typhoon level are determined. Based on the predicted typhoon path and the predicted typhoon level, the typhoon distance and the typhoon impact time of the target wind farm are determined. When the typhoon distance to the target wind farm is less than or equal to a preset distance threshold or the typhoon impact time at the target wind farm is greater than or equal to a preset impact duration, a typhoon warning is activated, and the blade pitch angle of each wind turbine in the target wind farm is gradually adjusted under the current gust conditions.

[0012] In one embodiment, the step of progressively adjusting the blade pitch angle of each wind turbine in the target wind farm under the current gust wind condition includes: When the activation time of a typhoon warning is less than or equal to the first preset time, the blade pitch angle of each wind turbine in the target wind farm under the current gust conditions is gradually adjusted from the first preset angle to the second preset angle, where the second preset angle is greater than the first preset angle. When the activation duration of a typhoon warning is longer than the first preset duration but shorter than the second preset duration, the blade pitch angle of each wind turbine in the target wind farm under the current gust conditions will be gradually adjusted from the second preset angle to the third preset angle, where the third preset angle is greater than the second preset angle.

[0013] In one embodiment, the step of determining the corresponding coordinated control strategy based on the current gust conditions includes: When the current gust condition is a normal gust condition, the attitude adaptive control strategy will be used as the cooperative control strategy. When the current gust condition is a strong gust condition, the attitude adaptive control strategy and the power distribution control strategy are used as a cooperative control strategy. When the current gust condition is an extreme gust condition, the attitude adaptive control strategy, the power distribution control strategy, and the protection control strategy are used as a coordinated control strategy.

[0014] Furthermore, to achieve the above objectives, this application also proposes a full-scenario anti-gust collaborative control system, which includes: The operating condition determination module is used to determine the predicted wind speed corresponding to the current wind speed and the rate of change of wind speed based on the wind speed prediction model. The working condition determination module is further configured to determine the current gust working condition based on the current wind speed and the predicted wind speed, wherein the current gust working condition is any one of ordinary gust working condition, strong gust working condition and extreme gust working condition. The collaborative control module is used to determine the corresponding collaborative control strategy based on the current gust conditions. The collaborative control strategy includes at least one of the following: attitude adaptive control strategy, cluster output allocation control strategy, and protection control strategy. The collaborative control module is also used to adjust the key control parameters of each wind turbine generator in the target wind farm under the current gust wind conditions, based on the collaborative control strategy.

[0015] In addition, to achieve the above objectives, this application also proposes a full-scene anti-gust collaborative control device, which includes: a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The computer program is configured to implement the steps of the full-scene anti-gust collaborative control method 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 all-scenario anti-gust 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 all-scenario anti-gust collaborative control method described above.

[0018] This application provides a comprehensive anti-gust collaborative control method. Based on a wind speed prediction model, it determines the predicted wind speed corresponding to the current wind speed and the rate of change of wind speed. Based on the current wind speed and the predicted wind speed, it determines the current gust condition, which can be any one of ordinary gust condition, strong gust condition, or extreme gust condition. Based on the current gust condition, it determines a corresponding collaborative control strategy, which includes at least one of attitude adaptive control strategy, cluster power distribution control strategy, and protection control strategy. Based on the collaborative control strategy, it adjusts the key control parameters of each wind turbine generator in the target wind farm under the current gust condition. This application can cover all conditions including conventional gusts, strong gusts, and typhoons, forming a full-chain collaborative control system of single-unit response, cluster collaboration, and extreme protection early warning. It achieves anti-gust collaborative control in all scenarios, ensuring the safe and stable operation of wind turbine generators in the wind farm, thereby ensuring the power generation efficiency of the wind turbine generators. This solves the technical problems of traditional solutions being difficult to apply to complex wind conditions, having poor power output stability, and affecting power generation efficiency. 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 Embodiment 1 of the all-scenario anti-gust collaborative control method of this application; Figure 2 This is a schematic diagram of the overall architecture of the all-scenario anti-gust collaborative control method provided in Embodiment 1 of this application; Figure 3 A schematic diagram of attitude correction for the all-scenario anti-gust cooperative control method provided in Embodiment 1 of this application; Figure 4 A typhoon warning diagram for the all-scenario anti-gust collaborative control method provided in Embodiment 1 of this application; Figure 5 This is a schematic diagram of the module structure of the all-scenario anti-gust collaborative control system in an embodiment of this application; Figure 6 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the all-scenario anti-gust collaborative control method in this application embodiment.

[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 as follows: Based on the wind speed prediction model, determine the predicted wind speed corresponding to the current wind speed and the rate of change of wind speed; based on the current wind speed and the predicted wind speed, determine the current gust condition, which is any one of ordinary gust condition, strong gust condition, and extreme gust condition; based on the current gust condition, determine the corresponding cooperative control strategy, which includes at least one of attitude adaptive control strategy, cluster output allocation control strategy, and protection control strategy; based on the cooperative control strategy, adjust the key control parameters of each wind turbine in the target wind farm under the current gust condition.

[0026] Currently, the impact of complex wind conditions such as gusts and typhoons on cluster operation has been amplified. The traditional single-unit independent control mode does not take into account the wake effect between units, resulting in poor overall power output stability. Downstream units are affected by the superposition of upstream wake and gusts, further aggravating the impact load, which can no longer meet the requirements of large-scale and high stability.

[0027] This application provides a solution that covers all operating conditions including regular gusts, strong gusts, and typhoons, forming a full-chain collaborative control system that integrates single-unit response, cluster collaboration, and extreme protection and early warning. This system enables anti-gust collaborative control in all scenarios, ensuring the safe and stable operation of wind turbine generators in wind farms, thereby guaranteeing the power generation efficiency of wind turbine generators. It solves the technical problems of traditional solutions being difficult to apply to complex wind conditions, having poor power output stability, and affecting power generation efficiency.

[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 full-scene anti-gust collaborative control device. This embodiment does not specifically limit it in this regard. The following uses a full-scene anti-gust collaborative control device as an example to describe this embodiment and the following embodiments.

[0029] This application provides a method for all-scenario anti-gust collaborative control, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the all-scenario anti-gust collaborative control method of this application.

[0030] In this embodiment, the all-scenario anti-gust collaborative control method includes steps S10~S40: Step S10: Based on the wind speed prediction model, determine the predicted wind speed corresponding to the current wind speed and the rate of change of wind speed; It should be noted that the target wind farm is one that requires coordinated control against gusts. In this embodiment, the target wind farm typically includes multiple wind turbine generators, each equipped with a lidar anemometer (measurement range 0-50 m / s, accuracy ±0.1 m / s), which can collect the instantaneous wind speed at the nacelle in real time, i.e., the current wind speed, which can be denoted as... , in m / s.

[0031] Additionally, it should be noted that the real-time rate of change, i.e., the wind speed change rate, is calculated using the differential method based on continuously collected wind speed data (sampling frequency ≥ 10Hz), as shown below:

[0032] In the formula, Indicates the rate of change of wind speed. Indicates the current wind speed. Indicates the wind speed at the previous moment. Indicates the sampling interval. .

[0033] Understandably, the wind speed prediction model is the mathematical model used to predict wind speed, as shown below:

[0034] In the formula, Indicates predicted wind speed, Indicates the rate of change of wind speed. Indicates the current wind speed. This indicates mechanical delay. Specifically, mechanical delay... This can be considered as the mechanical delay of the pitch / yaw system, determined based on the actual situation, and is typically 0.3s-0.5s. Generally, under conditions of low wind speed (e.g., ≤0.5m / s 2 With relatively small mechanical delay, a historical wind speed correction factor of 0.8 can be further applied to reduce prediction error. The corrected predicted wind speed is... , This is a correction factor for historical average wind speeds, calculated as: Historical Average Wind Speed ​​ / Historical Predicted Average Wind Speed; In cases of high wind speeds (e.g.) >1.0m / s 2 Due to the significant mechanical delay, a gust coefficient of 1.2 can be added to further correct the wind speed, resulting in a revised predicted wind speed of [missing value]. , The gust coefficient is calculated as the ratio of the maximum gust speed within 3 seconds to the average wind speed within 10 minutes.

[0035] Step S20: Based on the current wind speed and the predicted wind speed, determine the current gust condition, which is any one of ordinary gust condition, strong gust condition and extreme gust condition. It should be noted that the current gust condition refers to the current operating conditions, which can be any one of the following: normal gust condition, strong gust condition, and extreme gust condition. The normal gust condition means that there are scenarios with regular gusts, the strong gust condition means that there are scenarios with strong gusts, and the extreme gust condition means that there are scenarios with extreme gusts, such as typhoon weather.

[0036] Understandably, the gust coefficient is calculated based on the ratio of the maximum gust speed within 3 seconds to the average wind speed within 10 minutes. This is used to quantify the relative intensity of fluctuations and avoid short-term prediction bias. Based on the current wind speed and the predicted wind speed, the predicted change in wind speed (used to quantify the amplitude of fluctuations) and the predicted rate of change in wind speed (used to quantify the speed of fluctuations and incorporate the prediction trend) are calculated, as shown below:

[0037]

[0038] In the formula, This indicates the predicted change in wind speed. This indicates the rate of change in wind speed forecast. Indicates the current wind speed. Indicates predicted wind speed, This indicates mechanical delay.

[0039] It should be understood that if the current wind speed is greater than or equal to 5 m / s and less than 15 m / s, the predicted wind speed is less than or equal to 15 m / s, the predicted wind speed change is less than or equal to 0.5 m / s, the predicted wind speed change rate is less than 0.5 m / s², and the gust coefficient is greater than or equal to 1.2 and less than or equal to 1.5, then the current gust condition is considered a normal gust condition. If the current wind speed is greater than or equal to 15 m / s and less than 25 m / s, the predicted wind speed is greater than or equal to 15 m / s and less than 25 m / s, the predicted wind speed change is greater than 1 m / s, the predicted wind speed change rate is greater than 1 m / s², and the gust coefficient is greater than 1.5, then the current gust condition is considered a strong gust condition. If the current wind speed is greater than or equal to 25 m / s, the predicted wind speed is greater than or equal to 25 m / s, the predicted wind speed change is greater than 1 m / s, the predicted wind speed change rate is greater than 1 m / s², and the gust coefficient is greater than 1.5, then the current gust condition is considered an extreme gust condition.

[0040] Step S30: Based on the current gust conditions, determine the corresponding cooperative control strategy. The cooperative control strategy includes at least one of the following: attitude adaptive control strategy, cluster output allocation control strategy, and protection control strategy. It should be noted that the cooperative control strategy, which is the cooperative control method used in this embodiment, includes at least one of the attitude adaptive control strategy, cluster output allocation control strategy, and protection control strategy, that is, at least one of the attitude adaptive control strategy, cluster output allocation control strategy, and protection control strategy is selected.

[0041] In one feasible implementation, step S30 may include: when the current gust condition is a normal gust condition, using the attitude adaptive control strategy as a cooperative control strategy; when the current gust condition is a strong gust condition, using the attitude adaptive control strategy and the power distribution control strategy as a cooperative control strategy; and when the current gust condition is an extreme gust condition, using the attitude adaptive control strategy, the power distribution control strategy, and the protection control strategy as a cooperative control strategy.

[0042] Understandably, if the current gust condition is a normal gust condition, then the attitude adaptive control strategy is used as the cooperative control strategy; if the current gust condition is a strong gust condition, then the attitude adaptive control strategy and the power distribution control strategy are used as the cooperative control strategy; if the current gust condition is an extreme gust condition, then the attitude adaptive control strategy, the power distribution control strategy, and the protection control strategy are used as the cooperative control strategy.

[0043] It should be understood that this embodiment adopts a three-level collaborative control system. The first level is an attitude adaptive control strategy, which is a single-unit adaptive control strategy, mainly for the independent control of a single wind turbine. The second level is an output distribution control strategy, which is a cluster collaborative control strategy, mainly for the collaborative control of the cluster to which the wind turbines belong. The third level is a protection control strategy, mainly for switching from "passive wind resistance" to "active protection" during typhoons. In specific implementation, under normal gust conditions, only the first-level single-unit adaptive control strategy is activated to ensure the stable operation of a single unit. Under strong gust conditions, the first and second levels are activated in a coordinated manner to weaken the impact of wake superposition through cluster scheduling. Under extreme gust conditions, all three levels are activated in a comprehensive coordinated manner.

[0044] Step S40: Based on the cooperative control strategy, adjust the key control parameters of each wind turbine generator in the target wind farm under the current gust wind condition.

[0045] It should be noted that key control parameters are important parameters that need to be adjusted during the process of resisting gusts, that is, important parameters that can contribute to resisting gusts, such as: blade windward angle, nacelle windward direction, motor torque. The key control parameters that need to be adjusted are usually different under different gust conditions, and this embodiment does not make specific limitations on them.

[0046] Understandably, according to the coordinated control strategy under different gust conditions, the key control parameters of each wind turbine in the target wind farm are adjusted accordingly to ensure that all wind turbines can operate safely and stably under the gust conditions.

[0047] It should be understood that the overall architecture can be referenced. Figure 2It uses the obtained meteorological data, radar data, and sensor data to make intelligent decisions, issue coordinated commands, and coordinately control the wind turbine generators in the target wind farm based on attitude adaptive control strategy, power distribution control strategy, and protection control strategy.

[0048] This embodiment provides a comprehensive gust-resistant collaborative control method. Based on a wind speed prediction model, it determines the predicted wind speed corresponding to the current wind speed and the rate of change of wind speed. Based on the current wind speed and the predicted wind speed, it determines the current gust condition, which can be any one of ordinary gust, strong gust, or extreme gust conditions. Based on the current gust condition, it determines a corresponding collaborative control strategy, which includes at least one of attitude adaptive control, cluster output allocation control, and protection control strategies. Based on the collaborative control strategy, it adjusts the key control parameters of each wind turbine in the target wind farm under the current gust condition. This embodiment can cover all conditions including conventional gusts, strong gusts, and typhoons, forming a full-chain collaborative control system of single-unit response, cluster collaboration, and extreme protection early warning. This achieves comprehensive gust-resistant collaborative control in all scenarios, ensuring the safe and stable operation of wind turbines in the wind farm, thereby guaranteeing the power generation efficiency of the wind turbines.

[0049] In one feasible implementation, step S40 may include steps S401 to S402: Step S401: When an attitude adaptation strategy exists in the cooperative control strategy, calculate the attitude correction value of each wind turbine in the target wind farm under the current gust wind condition. It should be noted that key control parameters include attitude parameters. In this embodiment, attitude parameters include at least the blade angle of attack, the nacelle's direction of attack, and the motor torque. The attitude correction value is the adjustment amount of the attitude parameters.

[0050] Additionally, it should be noted that in practice, the blade angle is adjusted via pitch angle commands to optimize wind energy capture and unit safety, while the nacelle's windward direction is adjusted via yaw speed commands, ensuring that the wind turbine is always perpendicular to the wind direction to improve power generation efficiency.

[0051] In one feasible implementation, calculating the attitude correction values ​​of each wind turbine generator in the target wind farm under the current gust wind condition may include steps A11 to A14: Step A11: Based on the current gust conditions, determine the corresponding pitch weight, yaw weight, and torque weight in the weight mapping table; It should be noted that different response weights are required under different gust conditions. Response weights include at least pitch weight, yaw weight, and torque weight. In this embodiment, a weight mapping table is used to store the response weights corresponding to different gust conditions, thereby allowing the matching pitch weight, yaw weight, and torque weight to be found according to the current gust condition.

[0052] For example, under normal gust conditions, the pitch weight is 0.4, the yaw weight is 0.3, and the torque weight is 0.3; under strong gust conditions, the pitch weight is 0.6, the yaw weight is 0.2, and the torque weight is 0.2; under extreme gust conditions, the pitch weight is 0.8, the yaw weight is 0.1, and the torque weight is 0.1.

[0053] Step A12: Generate a dynamic load allocation weight matrix based on the pitch weight, the yaw weight, and the torque weight; Understandably, a dynamic load allocation weight matrix is ​​constructed based on the pitch weight, yaw weight, and torque weight corresponding to the current gust conditions.

[0054] Step A13: Based on the dynamic load allocation weight matrix, determine the parameter correction strategy for the attitude parameters, wherein the attitude parameters include at least the blade windward angle, the nacelle windward direction, and the motor torque. It should be noted that, based on the pitch weight, yaw weight, and torque weight in the dynamic load allocation weight matrix, the adjustment direction of the blade angle of attack, the nacelle windward direction, and the motor torque during correction is determined, i.e., the parameter correction strategy.

[0055] It is understandable that a larger weight indicates a greater contribution of the corresponding attitude parameters to the process of resisting gusts. For example, if the pitch weight is in the range of [0, 0.4], the blade angle of attack increases by 5°; if the pitch weight is in the range of [0, 0.4], the parameter correction strategy for the blade angle of attack is to increase by 5°; if the pitch weight is in the range of [0.4, 0.6], the parameter correction strategy for the blade angle of attack is to increase by 10°. This embodiment does not make specific limitations on this.

[0056] Step A14: Based on the parameter correction strategy, determine the attitude correction value of each wind turbine in the target wind farm under the current gust wind condition.

[0057] It should be understood that, according to the parameter correction strategy, the attitude correction values ​​of each wind turbine in the target wind farm are calculated under the current gust conditions.

[0058] Step S402: Based on the attitude correction values ​​of each wind turbine generator in the target wind farm under the current gust wind conditions, adjust the attitude parameters of each wind turbine generator in the target wind farm.

[0059] Understandably, the attitude parameters of each wind turbine in the target wind farm are adjusted according to the attitude correction value, thereby achieving a three-dimensional coordinated response of pitch, yaw, and torque to offset the impact load of gusts in real time.

[0060] In one possible implementation, steps B11-B14 may be included after step S402: Step B11: Obtain the attitude response data of each wind turbine in the target wind farm under the current gust wind condition. The attitude response data includes at least pitch response data, yaw response data, and torque response data. It should be noted that attitude response data refers to the response of the pitch system, yaw system, and motor to control commands, including at least pitch response data, yaw response data, and torque response data, namely the changes in blade angle of attack, nacelle direction of attack, and motor torque.

[0061] Step B12: Construct a Lyapunov function based on the attitude response data; Understandably, Lyapunov functions As shown below:

[0062] In the formula, The state variables represent the Lyapunov functions. In this embodiment, the attitude response data is substituted into the state variables. , This indicates mechanical delay.

[0063] Step B13: Calculate the stability index based on the Lyapunov function; It should be noted that the stability index used in this embodiment is the derivative of the Lyapunov function, i.e. .

[0064] Step B14: When the stability index meets the preset stability conditions, the attitude parameters of each wind turbine in the target wind farm under the current gust condition are kept unchanged. When the stability index does not meet the preset stability conditions, the pitch weight, yaw weight and torque weight are adjusted based on the emergency adjustment value. Then, the process returns to the step of generating a dynamic load allocation weight matrix based on the pitch weight, yaw weight and torque weight.

[0065] Understandably, the preset stability condition is... If the stability index meets the preset stability conditions, no further adjustment of the attitude parameters is needed. If the stability index does not meet the preset stability conditions, the attitude parameters still need to be adjusted. In this case, the response weights are adjusted according to the emergency adjustment value, and the attitude parameters are adjusted again based on the adjusted response weights. The emergency adjustment value is the value at which the response weights need to be adjusted when the stability index does not meet the preset stability conditions. It can be set to a fixed value or it can be adaptively calculated according to the actual situation. This embodiment does not impose specific limitations on this.

[0066] refer to Figure 3 The Lyapunov function is used to determine the current stability. If the stability index does not meet the preset stability conditions, the pitch weight, yaw weight and torque weight are determined. According to the pitch weight, yaw weight and torque weight, the dynamic weight matrix is ​​determined. According to the dynamic weight matrix, the attitude correction is calculated. Finally, the attitude parameters are adjusted according to the attitude correction.

[0067] In another feasible implementation, step S401 may include: steps S401'~S404': Step S401': When there is a cluster output allocation control strategy in the collaborative control strategy, obtain the output efficiency of the wind turbine generator set, the cluster collaboration coefficient, the fatigue damage cost of the downstream unit corresponding to the wind turbine generator set, and the correspondence between the fatigue damage cost coefficient and the revenue. It should be noted that key control parameters include the power generation frequency.

[0068] It is understandable that this embodiment adopts a cluster output allocation algorithm based on game theory, and designs a payoff function, namely the output efficiency of the wind turbine generator, the cluster coordination coefficient, the fatigue damage cost of the corresponding downstream unit of the wind turbine generator, and the correspondence between the fatigue damage cost coefficient and the payoff, as shown below: Profit = Output efficiency × Cluster coordination coefficient - Fatigue damage cost × Fatigue damage cost coefficient In one feasible implementation, steps C11-C14 may be included before obtaining the output efficiency of the wind turbine generator set, the cluster coordination coefficient, the fatigue damage cost of the corresponding downstream units of the wind turbine generator set, and the correspondence between the fatigue damage cost coefficient and the revenue: Step C11: Based on the sensing data, terrain data, and historical wake data of each wind turbine in the target wind farm under the current gust conditions, construct a digital twin prediction model of the target wind farm. It should be noted that this embodiment requires the construction of a digital twin prediction model of the target wind farm, integrating the sensing data (wind speed, power, vibration) of all wind turbine generators, terrain data (slope, obstacle distribution) and historical wake data to reconstruct the wind condition distribution and wake propagation patterns of the wind farm.

[0069] Step C12: Based on the digital twin prediction model of the target wind farm, predict the gust propagation path and intensity attenuation index. Understandably, based on the digital twin model, the propagation path of gusts within the target wind farm is predicted, i.e., the gust propagation path, and the intensity attenuation characteristics / patterns are predicted, i.e., the intensity attenuation index.

[0070] Step C13: Based on the gust propagation path and the intensity attenuation index, identify the impact load superposition risk area and impact load risk area of ​​the downstream units corresponding to each wind turbine. It should be understood that, based on the gust propagation path and intensity attenuation index, the impact load superposition risk area and the impact load risk area of ​​downstream units are identified. The impact load risk area is the area with a high impact load, and the impact load superposition risk area is the area with both a high impact load and other risks. The area outside the impact load superposition risk area and the impact load risk area is the area without impact load risk.

[0071] Step C14: Based on the impact load superposition risk area and impact load risk area of ​​each wind turbine corresponding to the downstream unit, determine the fatigue damage cost coefficient of each wind turbine corresponding to the downstream unit.

[0072] Understandably, different fatigue damage cost coefficients are used for regions without impact load risk, regions with impact load risk, and regions with combined impact load risk. Generally, the fatigue damage cost coefficient for regions without impact load risk is smaller than that for regions with impact load risk, and the fatigue damage cost coefficient for regions with impact load risk is smaller than that for regions with combined impact load risk.

[0073] Step S402': Based on the game optimization objective, game constraints and the corresponding relationship, determine the optimization equation. The game optimization objective includes maximizing power stability and minimizing the fatigue damage cost of downstream units. The game constraints include rated power constraints, wind speed safety threshold constraints and control threshold constraints. It is understandable that the game optimization objective is the optimization objective of the payoff function, namely, maximizing power stability and minimizing the fatigue damage cost of downstream units. The game constraints are the constraints of the payoff function, including rated power constraints, wind speed safety threshold constraints, and control threshold constraints. Among them, the rated power constraint means that the rated power cannot be exceeded, the wind speed safety threshold constraint means that the wind speed safety threshold cannot be exceeded, and the control threshold constraint means that the limit value of pitch / yaw action cannot be exceeded.

[0074] Step S403': Solve the optimization equation to determine the target output efficiency of each wind turbine generator set in the target wind farm; Understandably, solving the optimization equations yields the target power output efficiency of each wind turbine generator in the target wind farm, which is the most suitable power output efficiency.

[0075] Step S404': Based on the target output efficiency of each wind turbine generator set in the target wind farm, adjust the power generation frequency of each wind turbine generator set in the target wind farm under the current gust wind condition.

[0076] It should be understood that the power generation frequency is adjusted according to the target output efficiency in order to adjust the power generation.

[0077] In another feasible implementation, step S401 may include: steps S401''~S405'': Step S401'': When a protective control strategy exists in the collaborative control strategy, multi-source typhoon data is collected. The multi-source typhoon data includes at least typhoon cloud system morphology data from satellite cloud images, pressure gradient data from ground pressure stations, and wind speed profile data from coastal meteorological radar. It should be noted that the control parameters include the blade pitch angle.

[0078] Understandably, data on typhoon cloud system morphology from satellite cloud images, pressure gradient data from ground barometers, and wind speed profile data from coastal meteorological radars are collected as multi-source typhoon data. This data is then cleaned and aligned spatiotemporally to form a unified input dataset.

[0079] Step S402'': Based on the multi-source typhoon data, generate a typhoon evolution model; It is understandable that typhoon evolution models are trained using multi-source typhoon data.

[0080] It should be understood that in this embodiment, the typhoon evolution model uses a CNN-LSTM model, which includes a CNN network and an LSTM network. The CNN network is used to extract spatial features such as the location of the typhoon eye and the intensity of the cloud system from the satellite cloud image, while the LSTM network is used to learn the evolution of the typhoon path and intensity over time and output the typhoon prediction results for the next 24 hours. The typhoon prediction results include the predicted typhoon path and the predicted typhoon level.

[0081] Step S403'': Based on the typhoon evolution model, determine the predicted typhoon path and the predicted typhoon level; Understandably, the typhoon evolution model outputs the predicted typhoon path and typhoon level, i.e., the predicted typhoon path and the predicted typhoon level.

[0082] Step S404'': Based on the predicted typhoon path and the predicted typhoon level, determine the typhoon distance and the typhoon impact time of the target wind farm; It should be understood that, based on the typhoon's predicted path and typhoon level, the distance between the target wind farm and the typhoon, i.e., the typhoon distance, is calculated, and the overall impact duration, i.e., the typhoon's impact time, is estimated.

[0083] Step S405'': When the typhoon distance to the target wind farm is less than or equal to a preset distance threshold or the typhoon impact time of the target wind farm is greater than or equal to a preset impact duration, a typhoon warning is activated, and the blade pitch angle of each wind turbine in the target wind farm is gradually adjusted under the current gust conditions.

[0084] It should be noted that the preset distance threshold and preset impact duration are the thresholds set for the typhoon warning. For example, the preset distance threshold is 50km and the preset impact duration is 2 hours.

[0085] Understandably, if the typhoon distance to the target wind farm is less than or equal to the preset distance threshold, or the typhoon impact time to the target wind farm is greater than or equal to the preset impact duration, then a typhoon warning is considered necessary, and the blade pitch angle is adjusted gradually.

[0086] In one feasible implementation, step S405'' may include steps D11~D12: Step D11: When the activation duration of the typhoon warning is less than or equal to the first preset duration, the blade pitch angle of each wind turbine in the target wind farm under the current gust condition is gradually adjusted from the first preset angle to the second preset angle, where the second preset angle is greater than the first preset angle. It should be noted that the activation duration of a typhoon warning refers to the duration after the typhoon warning is activated. The first preset duration can typically be set to 1 hour. In this embodiment, the first preset angle is less than the second preset angle. The first preset angle can be set to 0°, i.e., the initial value, and the second preset angle can be set to 30°.

[0087] Understandably, within one hour of the warning being activated, the blade pitch angle was gradually adjusted from 0° to 30°, while the generator was gradually reduced to 30% of its rated power.

[0088] Step D12: When the typhoon warning activation duration is greater than the first preset duration and less than the second preset duration, the blade pitch angle of each wind turbine in the target wind farm under the current gust condition is gradually adjusted from the second preset angle to the third preset angle, where the third preset angle is greater than the second preset angle.

[0089] It should be noted that the second preset duration can typically be set to 2 hours. In this embodiment, the second preset angle is smaller than the third preset angle, which can be set to 85°.

[0090] Understandably, within 1-2 hours after the warning is activated, the blade pitch angle will continue to be adjusted to 85° (feathering state), the generator will be shut down, and the yaw system will be adjusted to a 30° headwind direction to reduce the windward load.

[0091] refer to Figure 4 Typhoon evolution models are constructed using typhoon cloud system morphology data from satellite cloud images, pressure gradient data from ground bar stations, and wind speed profile data from coastal meteorological radars. The typhoon evolution model is then used to determine the typhoon prediction path and typhoon prediction level. The blade pitch angle is then gradually adjusted according to the typhoon prediction path and typhoon prediction level.

[0092] In addition, a false trigger verification mechanism for the protection system is set up, and a consistency verification of multi-source data is combined with the satellite cloud image and radar data deviation ≤10% to filter out false typhoon information.

[0093] The above are only three feasible implementation methods of step S40 provided in this embodiment. This embodiment does not specifically limit the specific implementation method of step S40.

[0094] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the full-scenario anti-gust collaborative control method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0095] This application also provides a full-scenario anti-gust collaborative control system; please refer to... Figure 5 The full-scenario anti-gust collaborative control system includes: The operating condition determination module 10 is used to determine the predicted wind speed corresponding to the current wind speed and the rate of change of wind speed based on the wind speed prediction model. The working condition determination module 10 is further configured to determine the current gust working condition based on the current wind speed and the predicted wind speed, wherein the current gust working condition is any one of ordinary gust working condition, strong gust working condition and extreme gust working condition. The collaborative control module 20 is used to determine a corresponding collaborative control strategy based on the current gust conditions. The collaborative control strategy includes at least one of the following: attitude adaptive control strategy, cluster output allocation control strategy, and protection control strategy. The collaborative control module 20 is also used to adjust the key control parameters of each wind turbine generator in the target wind farm under the current gust wind conditions based on the collaborative control strategy.

[0096] In one feasible implementation, the cooperative control module 20 is further configured to calculate the attitude correction value of each wind turbine generator in the target wind farm under the current gust condition when an attitude adaptive strategy exists in the cooperative control strategy. Based on the attitude correction values ​​of each wind turbine generator in the target wind farm under the current gust wind conditions, the attitude parameters of each wind turbine generator in the target wind farm are adjusted.

[0097] In one feasible implementation, the cooperative control module 20 is further configured to determine the corresponding pitch weight, yaw weight, and torque weight in the weight mapping table based on the current gust conditions. A dynamic load allocation weight matrix is ​​generated based on the pitch weight, the yaw weight, and the torque weight. Based on the dynamic load allocation weight matrix, the parameter correction strategy for the attitude parameters is determined. The attitude parameters include at least the blade windward angle, the nacelle windward direction, and the motor torque. Based on the parameter correction strategy, the attitude correction value of each wind turbine in the target wind farm under the current gust wind condition is determined.

[0098] In one feasible implementation, the cooperative control module 20 is further configured to acquire attitude response data of each wind turbine generator in the target wind farm under the current gust wind condition, wherein the attitude response data includes at least pitch response data, yaw response data and torque response data. Based on the attitude response data, a Lyapunov function is constructed; Based on the Lyapunov function, the stability index is calculated; When the stability index meets the preset stability conditions, the attitude parameters of each wind turbine in the target wind farm under the current gust condition will remain unchanged. When the stability index does not meet the preset stability conditions, the pitch weight, yaw weight and torque weight are adjusted based on the emergency adjustment value, and the process returns to the step of generating a dynamic load allocation weight matrix based on the pitch weight, yaw weight and torque weight.

[0099] In one feasible implementation, the collaborative control module 20 is further configured to, when there is a cluster output allocation control strategy in the collaborative control strategy, obtain the output efficiency of the wind turbine generator set, the cluster collaboration coefficient, the fatigue damage cost of the downstream unit corresponding to the wind turbine generator set, and the correspondence between the fatigue damage cost coefficient and the revenue. Based on the game optimization objective, game constraints, and the corresponding relationship, the optimization equation is determined. The game optimization objective includes maximizing power stability and minimizing the fatigue damage cost of downstream units. The game constraints include rated power constraints, wind speed safety threshold constraints, and control threshold constraints. Solve the optimization equation to determine the target output efficiency of each wind turbine in the target wind farm; Based on the target output efficiency of each wind turbine in the target wind farm, the power generation frequency of each wind turbine in the target wind farm is adjusted under the current gust wind conditions.

[0100] In one feasible implementation, the collaborative control module 20 is further configured to construct a digital twin prediction model of the target wind farm based on the sensing data, terrain data, and historical wake data of each wind turbine in the target wind farm under the current gust conditions. Based on the digital twin prediction model of the target wind farm, the propagation path and intensity attenuation index of gusts are predicted. Based on the gust propagation path and the intensity attenuation index, identify the impact load superposition risk area and impact load risk area of ​​each wind turbine corresponding to the downstream unit. Based on the impact load superposition risk area and impact load risk area of ​​each wind turbine corresponding to the downstream unit, the fatigue damage cost coefficient of each wind turbine corresponding to the downstream unit is determined.

[0101] In one feasible implementation, the collaborative control module 20 is further configured to collect multi-source typhoon data when a protective control strategy exists in the collaborative control strategy. The multi-source typhoon data includes at least typhoon cloud system morphology data from satellite cloud images, pressure gradient data from ground pressure stations, and wind speed profile data from coastal meteorological radar. Based on the multi-source typhoon data, a typhoon evolution model is generated; Based on the typhoon evolution model, the predicted typhoon path and typhoon level are determined. Based on the predicted typhoon path and the predicted typhoon level, the typhoon distance and the typhoon impact time of the target wind farm are determined. When the typhoon distance to the target wind farm is less than or equal to a preset distance threshold or the typhoon impact time at the target wind farm is greater than or equal to a preset impact duration, a typhoon warning is activated, and the blade pitch angle of each wind turbine in the target wind farm is gradually adjusted under the current gust conditions.

[0102] In one feasible implementation, the collaborative control module 20 is further configured to, when the activation duration of the typhoon warning is less than or equal to the first preset duration, gradually adjust the blade pitch angle of each wind turbine in the target wind farm under the current gust condition from the first preset angle to the second preset angle, wherein the second preset angle is greater than the first preset angle. When the activation duration of a typhoon warning is longer than the first preset duration but shorter than the second preset duration, the blade pitch angle of each wind turbine in the target wind farm under the current gust conditions will be gradually adjusted from the second preset angle to the third preset angle, where the third preset angle is greater than the second preset angle.

[0103] In one feasible implementation, the cooperative control module 20 is further configured to use the attitude adaptive control strategy as the cooperative control strategy when the current gust condition is a normal gust condition. When the current gust condition is a strong gust condition, the attitude adaptive control strategy and the power distribution control strategy are used as a cooperative control strategy. When the current gust condition is an extreme gust condition, the attitude adaptive control strategy, the power distribution control strategy, and the protection control strategy are used as a coordinated control strategy.

[0104] The all-scenario anti-gust collaborative control system provided in this application, employing the all-scenario anti-gust collaborative control method in the above embodiments, can solve the technical problems of traditional solutions being difficult to apply to complex wind conditions, having poor power output stability, and affecting power generation efficiency. Compared with the prior art, the beneficial effects of the all-scenario anti-gust collaborative control system provided in this application are the same as those of the all-scenario anti-gust collaborative control method provided in the above embodiments, and other technical features in the all-scenario anti-gust collaborative control system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0105] This application provides a full-scene anti-gust collaborative control device, which 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 full-scene anti-gust collaborative control method in the first embodiment above.

[0106] The following is for reference. Figure 6The diagram illustrates a structural schematic suitable for implementing the all-scenario anti-gust collaborative control device in the embodiments of this application. The all-scenario anti-gust collaborative control device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), 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 6 The illustrated all-scenario anti-gust collaborative control device is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0107] like Figure 6 As shown, the all-scenario gust-resistant collaborative control device may include a processing system 1001 (e.g., a central processing unit, a graphics processing unit, 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 system 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the all-scenario gust-resistant collaborative control device. The processing system 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 systems 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output systems 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage systems 1003 including, for example, magnetic tapes, hard disks, etc.; and communication systems 1009. Communication system 1009 allows the all-scenario anti-gust collaborative control device to exchange data with other devices wirelessly or via wired communication. Although the figure shows an all-scenario anti-gust collaborative control device 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.

[0108] 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 system, or installed from storage system 1003, or installed from ROM 1002. When the computer program is executed by processing system 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0109] The all-scenario anti-gust collaborative control device provided in this application, employing the all-scenario anti-gust collaborative control method in the above embodiments, can solve the technical problems of traditional solutions being difficult to apply to complex wind conditions, having poor power output stability, and affecting power generation efficiency. Compared with the prior art, the beneficial effects of the all-scenario anti-gust collaborative control device provided in this application are the same as those of the all-scenario anti-gust collaborative control method provided in the above embodiments, and other technical features in this all-scenario anti-gust collaborative control device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0110] 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.

[0111] 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.

[0112] 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 all-scenario anti-gust collaborative control method in the above embodiments.

[0113] 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, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), 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.

[0114] The aforementioned computer-readable storage medium may be included in the all-scenario anti-gust collaborative control device; or it may exist independently and not be assembled into the all-scenario anti-gust collaborative control device.

[0115] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the all-scenario anti-gust collaborative control device, the all-scenario anti-gust collaborative control device: determines the predicted wind speed corresponding to the current wind speed and the rate of change of wind speed based on a wind speed prediction model; determines the current gust condition based on the current wind speed and the predicted wind speed, wherein the current gust condition is any one of ordinary gust condition, strong gust condition, and extreme gust condition; determines the corresponding collaborative control strategy based on the current gust condition, wherein the collaborative control strategy includes at least one of attitude adaptive control strategy, cluster output allocation control strategy, and protection control strategy; and adjusts the key control parameters of each wind turbine generator in the target wind farm under the current gust condition based on the collaborative control strategy.

[0116] 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++, and 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 a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0117] 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.

[0118] 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.

[0119] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., computer programs) for executing the above-described all-scenario anti-gust collaborative control method. This solves the technical problems of traditional solutions being difficult to apply to complex wind conditions, having poor power output stability, and affecting power generation efficiency. 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 all-scenario anti-gust collaborative control method provided in the above embodiments, and will not be repeated here.

[0120] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the all-scenario anti-gust collaborative control method described above.

[0121] The computer program product provided in this application can solve the technical problems of traditional solutions being difficult to apply to complex wind conditions, having poor power output stability, and affecting power generation efficiency. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the all-scenario anti-gust collaborative control method provided in the above embodiments, and will not be repeated here.

[0122] 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 coordinated control against gusts in all scenarios, characterized in that, The method includes: Based on the wind speed prediction model, determine the predicted wind speed corresponding to the current wind speed and the rate of change of wind speed; Based on the current wind speed and the predicted wind speed, the current gust condition is determined, which is any one of the following: ordinary gust condition, strong gust condition, and extreme gust condition. Based on the current gust conditions, a corresponding collaborative control strategy is determined. The collaborative control strategy includes at least one of the following: attitude adaptive control strategy, cluster power distribution control strategy, and protection control strategy. Based on the aforementioned collaborative control strategy, the key control parameters of each wind turbine generator in the target wind farm under the current gust wind conditions are adjusted.

2. The method as described in claim 1, characterized in that, The key control parameters include attitude parameters; The step of adjusting the key control parameters of each wind turbine generator in the target wind farm under the current gust wind condition based on the cooperative control strategy includes: When an attitude adaptation strategy exists in the cooperative control strategy, the attitude correction value of each wind turbine in the target wind farm is calculated under the current gust wind condition. Based on the attitude correction values ​​of each wind turbine generator in the target wind farm under the current gust wind conditions, the attitude parameters of each wind turbine generator in the target wind farm are adjusted.

3. The method as described in claim 2, characterized in that, The steps for calculating the attitude correction values ​​of each wind turbine in the target wind farm under the current gust wind condition include: Based on the current gust conditions, determine the corresponding pitch weight, yaw weight, and torque weight in the weight mapping table. A dynamic load allocation weight matrix is ​​generated based on the pitch weight, the yaw weight, and the torque weight. Based on the dynamic load allocation weight matrix, the parameter correction strategy for the attitude parameters is determined. The attitude parameters include at least the blade windward angle, the nacelle windward direction, and the motor torque. Based on the parameter correction strategy, the attitude correction value of each wind turbine in the target wind farm under the current gust wind condition is determined.

4. The method as described in claim 3, characterized in that, The step of adjusting the attitude parameters of each wind turbine in the target wind farm based on the attitude correction values ​​of each wind turbine in the target wind farm under the current gust wind conditions further includes: Acquire attitude response data of each wind turbine generator in the target wind farm under the current gust wind condition. The attitude response data includes at least pitch response data, yaw response data, and torque response data. Based on the attitude response data, a Lyapunov function is constructed; Based on the Lyapunov function, the stability index is calculated; When the stability index meets the preset stability conditions, the attitude parameters of each wind turbine in the target wind farm under the current gust condition will remain unchanged. When the stability index does not meet the preset stability conditions, the pitch weight, yaw weight and torque weight are adjusted based on the emergency adjustment value, and the process returns to the step of generating a dynamic load allocation weight matrix based on the pitch weight, yaw weight and torque weight.

5. The method as described in claim 1, characterized in that, The key control parameters include the power generation frequency; The step of adjusting the key control parameters of each wind turbine generator in the target wind farm under the current gust wind condition based on the cooperative control strategy includes: When a cluster output allocation control strategy exists in the collaborative control strategy, the output efficiency of the wind turbine generator set, the cluster collaboration coefficient, the fatigue damage cost of the corresponding downstream unit of the wind turbine generator set, and the correspondence between the fatigue damage cost coefficient and the revenue are obtained. Based on the game optimization objective, game constraints, and the corresponding relationship, the optimization equation is determined. The game optimization objective includes maximizing power stability and minimizing the fatigue damage cost of downstream units. The game constraints include rated power constraints, wind speed safety threshold constraints, and control threshold constraints. Solve the optimization equation to determine the target output efficiency of each wind turbine in the target wind farm; Based on the target output efficiency of each wind turbine in the target wind farm, the power generation frequency of each wind turbine in the target wind farm is adjusted under the current gust wind conditions.

6. The method as described in claim 5, characterized in that, Before obtaining the output efficiency, cluster coordination coefficient, fatigue damage cost of downstream units corresponding to the wind turbine generators, and the correlation between fatigue damage cost coefficient and revenue, the following steps are also included: Based on the sensing data, terrain data, and historical wake data of each wind turbine in the target wind farm under the current gust conditions, a digital twin prediction model of the target wind farm is constructed. Based on the digital twin prediction model of the target wind farm, the propagation path and intensity attenuation index of gusts are predicted. Based on the gust propagation path and the intensity attenuation index, identify the impact load superposition risk area and impact load risk area of ​​each wind turbine corresponding to the downstream unit. Based on the impact load superposition risk area and impact load risk area of ​​each wind turbine corresponding to the downstream unit, the fatigue damage cost coefficient of each wind turbine corresponding to the downstream unit is determined.

7. The method as described in claim 1, characterized in that, The key control parameters include the blade pitch angle; The step of adjusting the key control parameters of each wind turbine generator in the target wind farm under the current gust wind condition based on the cooperative control strategy includes: When a protective control strategy exists in the collaborative control strategy, multi-source typhoon data is collected. The multi-source typhoon data includes at least typhoon cloud system morphology data from satellite cloud images, pressure gradient data from ground bar stations, and wind speed profile data from coastal meteorological radar. Based on the multi-source typhoon data, a typhoon evolution model is generated; Based on the typhoon evolution model, the predicted typhoon path and typhoon level are determined. Based on the predicted typhoon path and the predicted typhoon level, the typhoon distance and the typhoon impact time of the target wind farm are determined. When the typhoon distance to the target wind farm is less than or equal to a preset distance threshold or the typhoon impact time at the target wind farm is greater than or equal to a preset impact duration, a typhoon warning is activated, and the blade pitch angle of each wind turbine in the target wind farm is gradually adjusted under the current gust conditions.

8. The method as described in claim 7, characterized in that, The steps for progressively adjusting the blade pitch angle of each wind turbine in the target wind farm under the current gust wind conditions include: When the activation time of a typhoon warning is less than or equal to the first preset time, the blade pitch angle of each wind turbine in the target wind farm under the current gust conditions is gradually adjusted from the first preset angle to the second preset angle, where the second preset angle is greater than the first preset angle. When the activation duration of a typhoon warning is longer than the first preset duration but shorter than the second preset duration, the blade pitch angle of each wind turbine in the target wind farm under the current gust conditions will be gradually adjusted from the second preset angle to the third preset angle, where the third preset angle is greater than the second preset angle.

9. The method as described in claim 1, characterized in that, The step of determining the corresponding coordinated control strategy based on the current gust conditions includes: When the current gust condition is a normal gust condition, the attitude adaptive control strategy will be used as the cooperative control strategy. When the current gust condition is a strong gust condition, the attitude adaptive control strategy and the power distribution control strategy are used as a cooperative control strategy. When the current gust condition is an extreme gust condition, the attitude adaptive control strategy, the power distribution control strategy, and the protection control strategy are used as a coordinated control strategy.

10. A full-scenario anti-gust collaborative control system, characterized in that, The system includes: The operating condition determination module is used to determine the predicted wind speed corresponding to the current wind speed and the rate of change of wind speed based on the wind speed prediction model. The working condition determination module is further configured to determine the current gust working condition based on the current wind speed and the predicted wind speed, wherein the current gust working condition is any one of ordinary gust working condition, strong gust working condition and extreme gust working condition. The collaborative control module is used to determine the corresponding collaborative control strategy based on the current gust conditions. The collaborative control strategy includes at least one of the following: attitude adaptive control strategy, cluster output allocation control strategy, and protection control strategy. The collaborative control module is also used to adjust the key control parameters of each wind turbine generator in the target wind farm under the current gust wind conditions, based on the collaborative control strategy.

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