A model predictive control-based active power control method and system for a wind turbine group and related devices

Abstract

The application provides a wind turbine group active power control method and system based on model predictive control and related devices, including the following steps: initializing relevant system parameters, the relevant system parameters including the number of wind turbines in the target wind farm, the rated power of a single wind turbine, the adjustable range of the pitch angle, the prediction time domain and the control time domain; based on the relevant system parameters, a pre-constructed single-machine power prediction model and a group state space model, a wind turbine state vector and the predicted power corresponding to each wind turbine are obtained; based on the obtained wind turbine state vector and the predicted power corresponding to each wind turbine, a set rolling optimization objective function and constraint conditions are combined to obtain the optimal control sequence of the wind turbines in the target wind farm; the pitch angle of each wind turbine is adjusted based on the obtained optimal control sequence; the application actively smooths and balances the pitch angle action of each wind turbine through an optimization algorithm, significantly reduces the equivalent fatigue load of key components such as variable pitch bearings, blade roots and the like caused by frequent or excessive control actions, and effectively prolongs the service life of the equipment.

Description

Technical Field

[0001] This invention belongs to the field of wind power generation and wind turbine group control technology, specifically involving a method, system and related devices for active power control of wind turbine groups based on model predictive control. Background Technology

[0002] With the increasing penetration rate of wind power, the power grid is placing higher demands on the ability of wind farms to regulate active power at the minute or even second level. Traditional wind farm power control often adopts maximum power point tracking (MPPT) or simple proportional allocation for a single wind turbine generator (hereinafter referred to as "wind turbine generator"), which makes it difficult to take into account grid dispatch instructions, wind turbine generator operation safety and fatigue load balance.

[0003] Currently, mainstream wind farm energy management systems (EMS) still rely on PID (PID control) and rule bases as their core, with a typical process as follows: the dispatch center issues a total active power command for the entire wind farm. P ref EMS is allocated to each wind turbine according to a fixed ratio or the proportion of available power of the wind turbine, and then implemented through a single-unit PI controller. However, traditional PID or empirical rule control has three major drawbacks: 1) Each wind turbine independently performs maximum power tracking, with high-wind-speed turbines operating at full load and low-wind-speed turbines idling, resulting in poor coordination among the turbine groups and a decrease in the average efficiency across the entire field. 2) PID parameters are difficult to tune, and may overshoot by 5% to 10% when faced with sudden changes in wind speed. The scheduling tracking error often exceeds ±3%, resulting in a delayed response. 3) Frequent and large-amplitude pitch angle movements significantly increase the equivalent fatigue load on key components.

[0004] Existing technologies, such as patent CN119582351A, propose an active power control method and system for wind farms. This method employs a strong classifier based on the AdaBoost algorithm to identify wind turbines affected by wake effects, and combines this with a dynamic programming algorithm to select controllable wind turbine groups unaffected by wake interference. It then performs active power control with the minimum number of turbines, effectively reducing the impact of wake effects on control accuracy and improving the overall control efficiency of the wind farm. However, this method's handling of wake modeling and the dynamic coupling relationship between wind turbines is relatively static and simplified, making it difficult to cope with real-time changes such as sudden wind speed fluctuations. Furthermore, the control strategy focuses on turbine selection rather than multi-turbine collaborative optimization, failing to fully consider the dynamic coordination and fatigue load balancing among wind turbine units. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system and related device for active power control of wind turbine generator groups based on model predictive control, which solves the defects of poor group coordination and slow response in existing power control methods for wind turbine generator groups.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a method for active power control of a wind turbine generator group based on model predictive control, comprising the following steps: Initialize relevant system parameters, including the number of wind turbines in the target wind farm, the rated power of a single wind turbine, the adjustable range of the pitch angle, the prediction time domain, and the control time domain; Based on relevant system parameters and a pre-built single-unit power prediction model and a cluster state space model, the wind turbine state vector and the predicted power of each wind turbine are obtained. Based on the obtained wind turbine state vector and the predicted power of each wind turbine, combined with the set rolling optimization objective function and constraints, the optimal control sequence of the wind turbines in the target wind farm is obtained. Adjust the pitch angle of each wind turbine based on the obtained optimal control sequence.

[0007] Preferably, the pre-built single-machine power prediction model is as follows:

[0008] in, For the first Power forecast values ​​for each wind turbine unit; air density; My wind turbine radius; The wind energy utilization coefficient; The tip speed ratio; The pitch angle; This is the predicted wind speed value; This represents the current discrete time step. To start from the current discrete time step Start predicting the offset of future time steps forward; For the first One wind turbine unit.

[0009] Preferably, the pre-constructed cluster state-space model is as follows:

[0010] in, This is the state vector of the wind turbine unit; This represents the rate of change of the pitch angle. The effective wind speed to be embedded in the wake effect; For the system matrix; The input matrix; Let be the perturbation matrix.

[0011] Preferably, the set rolling optimization objective function is:

[0012] in, Predict the total power of the fleet; For power grid dispatch instructions; For the first Change in pitch angle of individual wind turbine units; This refers to the load weighting coefficient; The total number of wind turbines in the target wind farm.

[0013] Preferably, the constraints include single-unit power value, pitch angle change rate constraint, and group power tracking error, wherein: Single unit power limit: ; Pitch angle change rate constraint: ; Cluster power tracking error: ; In the formula, For the first Power values ​​of individual wind turbine units; For the first Minimum power of a single wind turbine unit; For the first Maximum power of each wind turbine unit; The unit is seconds; Total power of the fleet; For power grid dispatch instructions; For the first The change in pitch angle of each wind turbine unit.

[0014] Preferably, the control method further includes: Real-time acquisition of the deviation between the actual power of the target wind farm and the predicted total power of the target wind farm's turbine cluster; If the obtained deviation is greater than or equal to the set threshold, the single-machine power prediction model will be updated.

[0015] Secondly, the present invention provides an active power control system for a wind turbine generator group based on model predictive control, comprising: The parameter setting unit is used to initialize relevant system parameters, including the number of wind turbines in the target wind farm, the rated power of a single wind turbine, the adjustable range of the pitch angle, the prediction time domain, and the control time domain. The power prediction unit is used to obtain the wind turbine state vector and the predicted power of each wind turbine based on relevant system parameters combined with a pre-built single-unit power prediction model and a cluster state space model. The control quantity acquisition unit is used to obtain the optimal control sequence of the wind turbines in the target wind farm based on the acquired wind turbine state vector and the predicted power corresponding to each wind turbine, combined with the set rolling optimization objective function and constraints. The control unit is used to adjust the pitch angle of each wind turbine based on the obtained optimal control sequence.

[0016] Thirdly, the present invention provides an electronic device including a processor and a memory, wherein the memory stores computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the method described thereon.

[0017] Fourthly, the present invention provides a computer program product, the computer program product including computer-executable instructions, which, when executed, implement the method described.

[0018] Fifthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the method described herein.

[0019] Compared with the prior art, the beneficial effects achieved by the present invention are: This invention provides a model predictive control-based active power control method for wind turbine clusters. By constructing a cluster state-space model incorporating wake effects and designing a rolling optimization objective function, it achieves coordinated optimization control of the active power of the wind turbine cluster, with significant and comprehensive benefits. This method effectively overcomes the technical shortcomings of traditional PID control or proportional allocation strategies, such as poor cluster coordination, response lag, large overshoot, and significant scheduling tracking errors. Through second-level rolling prediction and online optimization, it significantly improves the wind farm's rapid response capability and tracking accuracy to grid scheduling commands at the minute or even second level. This ensures that the total active power of the cluster can achieve second-level accurate tracking under scenarios of sudden wind speed changes and rapid command changes, and stably controls the tracking error within a set range. Simultaneously, this invention introduces a penalty term for pitch angle changes and a load weight coefficient into the objective function, explicitly incorporating fatigue load suppression into the optimization objective. Through the optimization algorithm, it actively smooths and balances the pitch angle movements of each wind turbine, significantly reducing the equivalent fatigue load on key components such as pitch bearings and blade roots caused by frequent or excessive control actions, effectively extending equipment lifespan. Furthermore, this method, by integrating a real-time feedback correction mechanism, can update the power prediction model and wind speed predictor online, continuously correcting prediction deviations caused by model mismatch or environmental disturbances. This results in strong robustness and adaptability, compatible with the uncertainties and complex constraints of wind farm operation. The system has a clear structure, and its strategy is independent of the specific size or layout of the wind farm, demonstrating strong scalability and engineering application potential. It provides an effective technical solution for improving the stability, economy, and security of large-scale wind power grid connection. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the process involved in the embodiments of the present invention. Detailed Implementation

[0021] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0022] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0023] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0024] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0025] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0026] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0027] Example 1 This embodiment provides a method for controlling the active power of a wind turbine generator group based on model predictive control, which includes the following steps: Initialize relevant system parameters, including the number of wind turbines in the target wind farm, the rated power of a single wind turbine, the adjustable range of the pitch angle, the prediction time domain, and the control time domain; Based on relevant system parameters and a pre-built single-unit power prediction model and a cluster state space model, the wind turbine state vector and the predicted power of each wind turbine are obtained. Based on the obtained wind turbine state vector and the predicted power of each wind turbine, combined with the set rolling optimization objective function and constraints, the optimal control sequence of the wind turbines in the target wind farm is obtained. Adjust the pitch angle of each wind turbine based on the obtained optimal control sequence.

[0028] Example 2 This embodiment provides a model predictive control-based active power control method for wind turbine clusters. By constructing a predictive model for the wind turbines, designing a rolling optimization objective function, introducing constraints, and combining a feedback correction mechanism, it achieves coordinated active power allocation and optimization among multiple wind turbines in a wind farm. The specific implementation steps of this embodiment are as follows: Step 1: Initialize system parameters First, initialize the relevant system parameters based on the specific conditions of the wind farm. For example, set the number of wind turbines in the wind farm to [number missing]. N =20, each wind turbine has a rated power of 5MW and an adjustable pitch angle range of 0°~30°. Simultaneously, the prediction time domain is determined. N p =10 (step size is 1s) and control time domain N c =5. The setting of these parameters provides the foundation for subsequent model building and control strategy implementation.

[0029] Step 2: Construct a wind turbine prediction model to obtain the state prediction sequence for each wind turbine and the total power prediction value of the wind farm cluster. Specifically: 1) Single-unit power prediction model: For each wind turbine, a power prediction model is established. The expression of the power prediction model is as follows:

[0030] in, This represents the current discrete time step. To start from the current discrete time step Start predicting the offset of future time steps forward; For air density, take 1.225 kg / m³; The radius of the wind turbine; The wind energy utilization coefficient is determined using a two-dimensional interpolation table, with inputs including the tip speed ratio. and propeller pitch angle ; Rotational speed; for The forward wind speed forecast is calculated using the ARIMA model and corrected online by incorporating real-time SCADA data. ; For the first Power forecast values ​​for each wind turbine unit; For the first One wind turbine unit.

[0031] 2) Wind turbine cluster state-space model: describes the coupling relationship between multiple wind turbines in a wind farm, and its state equations are as follows:

[0032] in, This is the wind turbine state vector, including the pitch angle. i Rotation speed oh ,power P ; This represents the rate of change of the pitch angle. The effective wind speed is embedded to account for the wake effect. Matrices A, B, and D are constructed based on the actual conditions of the wind farm to reflect the mutual influence between wind turbines and the effects of external disturbances. A is the system matrix, describing how the wind turbine state (pitch angle θ, speed ω, power P) evolves over time; B is the input matrix, describing how the control input (pitch angle change rate Δθ) affects the state changes; and D is the disturbance matrix, describing how external disturbances (wind speed v) affect the state changes, introducing coupling between wind turbines. The construction methods of the system matrix A, input matrix B, and disturbance matrix D are as follows: First, based on the mechanism and dynamics of wind turbine generators, a single wind turbine unit is established. i Nonlinearized model:

[0033] in, Let be the single-unit state vector of the i-th wind turbine. Let i be the control variable for the i-th wind turbine unit; Let be the disturbance of the i-th wind turbine unit.

[0034] To apply this to the quadratic programming solution of MPC, the nonlinear model needs to be discretized and linearized at the current operating point (x0, u0, v0) (e.g., using the Jacobian matrix) to obtain a linear discrete model:

[0035] Where Ai (a 3x3 matrix) represents the coupling relationship between the states of the i-th wind turbine, and is obtained through the nonlinear model. f Find the partial derivative (Jacobi matrix) with respect to the state vector xi and take its value at the operating point: Bi (a 3x1 matrix) represents the influence of the control input (pitch rate) on the wind turbine state, which is determined by the nonlinear model. f Find the partial derivative with respect to the control input ui and obtain its value at the operating point: Di (a 3x1 matrix) represents the impact of effective wind speed disturbance on the state of the wind turbine. This is achieved through the analysis of a nonlinear model. f Find the partial derivative with respect to the perturbation vi and obtain its value at the operating point: .

[0036] The models of all N wind turbines in the wind farm are combined. Assuming that the state dynamics of the wind turbines are independent and coupled only through wake wind speeds, the global system matrix A and the input matrix B are block diagonal matrices:

[0037]

[0038] The global perturbation matrix D is used to introduce the wake coupling effect between wind turbine units, and its structure is as follows:

[0039] Where W is an N x N tail flow influence weight matrix, whose elements We This represents the weight of the wind speed disturbance at the j-th wind turbine on the i-th wind turbine. The specific elements of matrix W are calculated and updated in real time based on the relative positions of the wind turbines within the wind farm, and are derived from the modified Jensen wake model.

[0040] Introducing a modified Jensen wake model to quantify inter-wind turbine interference:

[0041] in, For the first j Free wind speed at the typhoon generator unit; For the first i Typhoon turbine unit for the first j The wake wind speed of the typhoon generator unit; Weights are added to the wake. For the wake model, the first j All wind turbines upwind of the typhoon generators causing interference.

[0042]

[0043] in, dij For the first i Upstream wind turbine and the first j Distance to the typhoon generator (downstream); α This is the wake attenuation coefficient (default value is 0.075; complex terrain can be identified online). The axial induction factor for upstream wind turbine units; C T is the thrust coefficient; R The radius of the wind turbine; the weighted wake superposition. It can solve the problem of overlapping wakes from multiple wind turbine units. β Take 0.05.

[0044] In summary, the core prediction model is the state-space equation, where... This includes the effective wind speed predicted and corrected by the ARIMA model, which takes into account the wake effect of upstream wind turbines and is calculated using a modified Jensen model. This is for predicting future conditions. and control quantity Based on this, the predicted wind turbine power output is calculated using the given single-unit power prediction formula. Total power of the aircraft group .

[0045] Step 3: Solving the objective function using rolling optimization 1) Set a rolling optimization objective function to achieve synergistic optimization of power point tracking accuracy and wind turbine fatigue load. Frequent or large pitch angle movements are one of the main reasons for increased fatigue loads on critical components (such as pitch bearings and blade roots). Therefore, penalizing pitch angle changes is a common and effective simplified method to indirectly suppress fatigue loads on critical components (especially the pitch system). The objective function is:

[0046] in, Predict the total power of the fleet. P ref For power grid dispatch instructions, Δ i i For the first i Change in pitch angle of typhoon turbine generator set This is the load weighting coefficient, used to balance the relationship between power tracking accuracy and fatigue load.

[0047] 2) Introduce constraints, including single-unit power limits. Pitch angle change rate constraint and power tracking error of the aircraft group This is to ensure the feasibility and security of the control strategy.

[0048] Where s is the unit second. For the first Power values ​​of individual wind turbine units; For the first Minimum power of a single wind turbine unit; For the first Maximum power of each wind turbine unit; Total power of the fleet; For power grid dispatch instructions; For the first The change in pitch angle of each wind turbine unit.

[0049] 3) A quadratic programming (QP) solver is used to transform the objective function into standard QP form, and the optimal control sequence is calculated online. By solving the QP problem, we obtain the sequence of control variables that minimize the objective function under the current prediction model and constraints.

[0050] Step 4: Instruction Distribution and Execution The first set of control variables obtained by the solution The control parameters are sent to the pitch control systems of each wind turbine, guiding them to adjust according to the optimized pitch angle command to achieve precise power distribution and coordinated optimization. The remaining control sequences are discarded and await re-optimization in the next control cycle.

[0051] Step 5: Feedback Correction 1) After each control cycle, the actual power of the wind farm is collected. Calculate its relationship with the predicted total power of the fleet. Deviation between .

[0052] 2) If the absolute value of the deviation If the power exceeds a set threshold (which can be 1% to 5% of the total rated power of the wind farm, with the specific value set according to the actual conditions of the wind farm and accuracy requirements), the single-unit power prediction model will be updated. For example, adjusting... C p The values ​​in the lookup table are adjusted to more closely approximate actual operating conditions; at the same time, the wind speed predictor is recalibrated to improve prediction accuracy.

[0053] By cyclically executing the above steps, this application can achieve coordinated active power control of multiple wind turbine units in a wind farm, effectively improve power response speed, reduce fatigue load on wind turbine units, and be compatible with uncertainties and constraints. It has strong scalability and can be applied to wind farms of different sizes and types.

[0054] This application provides a wind turbine cluster active power control method based on model predictive control (MPC). It establishes a single-unit power-wake coupled state-space model and designs a rolling optimization objective function, encompassing both the cluster's total power tracking error and pitch angle variation. This achieves coordinated optimization of power tracking accuracy and wind turbine fatigue load, solving for the globally optimal allocation sequence under constraints of single-unit power, pitch angle rate, and cluster error. Compared to traditional PID or proportional allocation EMS, this method replaces fixed rules with second-level rolling predictions, achieving second-level accurate tracking of cluster active power during sudden wind speed changes and dispatch command variations. Simultaneously, it significantly reduces fatigue load on key components by balancing pitch angle action, offering advantages of high compatibility and easy scalability.

[0055] Example 3 This embodiment provides an active power control system for a wind turbine generator group based on model predictive control, including: The parameter setting unit is used to initialize relevant system parameters, including the number of wind turbines in the target wind farm, the rated power of a single wind turbine, the adjustable range of the pitch angle, the prediction time domain, and the control time domain. The power prediction unit is used to obtain the wind turbine state vector and the predicted power of each wind turbine based on relevant system parameters combined with a pre-built single-unit power prediction model and a cluster state space model. The control quantity acquisition unit is used to obtain the optimal control sequence of the wind turbines in the target wind farm based on the acquired wind turbine state vector and the predicted power corresponding to each wind turbine, combined with the set rolling optimization objective function and constraints. The control unit is used to adjust the pitch angle of each wind turbine based on the obtained optimal control sequence.

[0056] Example 4 This embodiment also provides a computing device. The computing device includes a bus, a processor, a memory, and a communication interface. The processor, memory, and communication interface communicate with each other via the bus. The computing device can be a server or a terminal device. It should be understood that this application does not limit the number of processors and memory in the computing device.

[0057] A bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, a bus can include a path for transmitting information between various components of a computing device (e.g., memory, processor, communication interfaces).

[0058] The processor may include any one or more of the following: central processing unit (CPU), graphics processing unit (GPU), tensor processing unit (TPU), application specific integrated circuit (ASIC), field-programmable gate array (FPGA), microprocessor (MP), or digital signal processor (DSP).

[0059] Memory can include volatile memory, such as random access memory (RAM). Processors can also include non-volatile memory. volatile memory, such as read-only memory (ROM). ROM (memory only), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0060] The memory stores executable program code, which the processor executes to implement the functions of the aforementioned units, thereby achieving, for example, the method described in Embodiment 1. That is, the memory may store instructions for the methods and functions relating to the computing device in any of the above embodiments.

[0061] The communication interface uses transceiver modules such as, but not limited to, network interface cards and transceivers to enable communication between computing devices and other devices or communication networks.

[0062] Example 5 This embodiment also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, cause the processor to perform the methods and functions of the computing device involved in any of the above embodiments.

[0063] Generally, the various embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software, which can be executed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of this disclosure are shown and described as block diagrams, flowcharts, or represented using some other illustration, it should be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as, as non-limiting examples, in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0064] Example 6 This embodiment provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which execute in a device on a target real or virtual processor to perform the processes / methods as described above with reference to the accompanying drawings. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. The machine-executable instructions for the program modules can execute within a local or distributed device. In a distributed device, the program modules can reside in both local and remote storage media.

[0065] Computer program code used to implement the methods of this disclosure may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when executed by the computer or other programmable data processing apparatus, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be performed. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0066] In the context of this disclosure, computer program code or related data may be carried on any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and so on. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0067] Computer-readable media can be any tangible medium that contains or stores programs for or relating to an instruction execution system, apparatus, or device, or a data storage device such as a data center containing one or more available media. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. More detailed examples of computer-readable storage media include electrical connections with 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 storage devices, magnetic storage devices, or any suitable combination thereof.

[0068] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for active power control of a wind turbine generator group based on model predictive control, characterized in that, Includes the following steps: Initialize relevant system parameters, including the number of wind turbines in the target wind farm, the rated power of a single wind turbine, the adjustable range of the pitch angle, the prediction time domain, and the control time domain; Based on relevant system parameters and a pre-built single-unit power prediction model and a cluster state space model, the wind turbine state vector and the predicted power of each wind turbine are obtained. Based on the obtained wind turbine state vector and the predicted power of each wind turbine, combined with the set rolling optimization objective function and constraints, the optimal control sequence of the wind turbines in the target wind farm is obtained. Adjust the pitch angle of each wind turbine based on the obtained optimal control sequence.

2. The active power control method for a wind turbine generator group based on model predictive control according to claim 1, characterized in that, The pre-built single-machine power prediction model is as follows: in, For the first Power forecast values ​​for each wind turbine unit; air density; My wind turbine radius; The wind energy utilization coefficient; The tip speed ratio; The pitch angle; This is the predicted wind speed value; This represents the current discrete time step. To start from the current discrete time step Start predicting the offset of future time steps forward; For the first One wind turbine unit.

3. The active power control method for wind turbine generator groups based on model predictive control according to claim 1, characterized in that, The pre-built cluster state space model is as follows: in, This is the state vector of the wind turbine unit; This represents the rate of change of the pitch angle. The effective wind speed to be embedded in the wake effect; For the system matrix; The input matrix; Let be the perturbation matrix.

4. The active power control method for wind turbine generator groups based on model predictive control according to claim 1, characterized in that, The defined objective function for scroll optimization is: in, Predict the total power of the fleet; For power grid dispatch instructions; For the first Change in pitch angle of individual wind turbine units; This refers to the load weighting coefficient; The total number of wind turbines in the target wind farm.

5. The active power control method for wind turbine generator groups based on model predictive control according to claim 1, characterized in that, The constraints include single-unit power values, pitch angle change rate constraints, and power tracking error of the aircraft group, among which: Single unit power limit: ; Pitch angle change rate constraint: ; Cluster power tracking error: ; In the formula, For the first Power values ​​of individual wind turbine units; For the first Minimum power of a single wind turbine unit; For the first Maximum power of each wind turbine unit; The unit is seconds; Total power of the fleet; For power grid dispatch instructions; For the first The change in pitch angle of each wind turbine unit.

6. The active power control method for a wind turbine generator group based on model predictive control according to claim 1, characterized in that, The control method further includes: Real-time acquisition of the deviation between the actual power of the target wind farm and the predicted total power of the target wind farm's turbine cluster; If the obtained deviation is greater than or equal to the set threshold, the single-machine power prediction model will be updated.

7. A model predictive control-based active power control system for a wind turbine generator group, comprising: The parameter setting unit is used to initialize relevant system parameters, including the number of wind turbines in the target wind farm, the rated power of a single wind turbine, the adjustable range of the pitch angle, the prediction time domain, and the control time domain. The power prediction unit is used to obtain the wind turbine state vector and the predicted power of each wind turbine based on relevant system parameters combined with a pre-built single-unit power prediction model and a cluster state space model. The control quantity acquisition unit is used to obtain the optimal control sequence of the wind turbines in the target wind farm based on the acquired wind turbine state vector and the predicted power corresponding to each wind turbine, combined with the set rolling optimization objective function and constraints. The control unit is used to adjust the pitch angle of each wind turbine based on the obtained optimal control sequence.

8. An electronic device, characterized in that, It includes a processor and a memory, the memory storing computer instructions that, when executed by the processor, cause the electronic device to perform the method of any one of claims 1 to 6.

9. A computer program product, characterized in that, The computer program product includes computer-executable instructions that, when executed, implement the method of any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when executed by a processor, implement the method of any one of claims 1 to 6.

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