A method and system for collaborative optimization of variable pitch control of offshore wind farms

Abstract

The present invention discloses a method and system for coordinated optimization of pitch control of an offshore wind farm, belonging to the technical field of wind power generation. It includes: obtaining power control signals and load reduction control signals of each wind turbine cluster; generating pitch control instructions based on the power control signal and load reduction control signal and transmitting them to the corresponding pitch action actuator for pitch control based on the preset optimal control target; the power control signal is determined by processing the wind turbine parameters and wind speed data of the offshore wind farm by the operation processing unit with the maximum output power of the offshore wind farm as the control target; the load reduction control signal is generated by the single-machine controller of the wind turbine cluster using the key features that affect the power output of the wind turbine, combined with the dynamic linear time-invariant model of the wind turbine. The coordinated control of the offshore wind farm is realized, while taking into account fatigue management and power optimization; the problem that the traditional wind turbine pitch control strategy is difficult to achieve coordinated control of the wind farm is overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a method and system for collaboratively optimizing pitch control of an offshore wind farm. Background Art

[0002] The statements in this section merely mention background art related to the present invention and do not necessarily constitute prior art.

[0003] Large-scale and efficient offshore wind farms are one of the key directions for high-quality development of offshore wind power in the future. The installation heights of wind turbines in offshore wind farms are similar. The wake effect of upstream wind turbines and the strong turbulence in the wake area will cause the power output of downstream wind turbines to decrease and further increase the fatigue load of the blades. At the same time, with the increase in unit capacity, the size and weight of various wind turbine components are also increasing. The large-scale unit equipment further aggravates the wake effect between units, aggravates the impact of unbalanced load distribution on the operating conditions of wind turbines, and causes fatigue to the pitch mechanism, bearings, towers and other components. The wind turbines in the entire field are coupled with each other and affect each other, which seriously restricts the output power and affects the operating conditions of wind turbines.

[0004] Traditional wind turbine pitch control strategies focus on studying the power generation performance and quality of a single unit, making it difficult to balance fatigue management and power optimization, and unable to simultaneously achieve the control objectives of wind farm coordinated optimization and load reduction requirements. In addition, due to the large distance between wind turbines in offshore wind farms, some wind turbines may be far from the coast, making it difficult to maintain real-time communication with the centralized control center on shore, further affecting the response speed and stability of the system. Summary of the invention

[0005] In order to address the deficiencies in the prior art, the present invention provides a method, system, electronic device, computer-readable storage medium and computer program product for collaboratively optimizing pitch control of an offshore wind farm, which can achieve collaborative optimization of the wind farm and reduce load with less communication dependence, thereby effectively improving the power generation efficiency of the wind farm and the safety of wind turbine operation.

[0006] In a first aspect, the present invention provides a method for collaboratively optimizing pitch control of an offshore wind farm;

[0007] A method for coordinated optimization of pitch control of an offshore wind farm, comprising:

[0008] Obtain power control signals and load reduction control signals of each wind turbine cluster;

[0009] According to the power control signal and the load reduction control signal, based on the preset optimal control target, a pitch control instruction is generated to be transmitted to the corresponding pitch action actuator for pitch control;

[0010] Among them, the power control signal is determined by processing the wind turbine parameters and wind speed data of the offshore wind farm through the computing processing unit with the maximum output power of the offshore wind farm as the control target; the load reduction control signal is generated by the single-machine controller of the wind turbine cluster using the key features that affect the power output of the wind turbine, combined with the dynamic linear time-invariant model of the wind turbine.

[0011] In some embodiments, the wind turbine cluster is determined by establishing a wind farm network partition by constructing a coupling relationship matrix between wind turbines through a central processing unit.

[0012] In some implementations, constructing a coupling relationship matrix between wind turbines to establish a wind farm network, and dividing the wind turbine clusters specifically includes:

[0013] Acquire real-time operation data of the offshore wind farm, process the real-time operation data by correlation analysis method, and determine key features;

[0014] Based on the key features, a directed graph of the wind farm network is constructed; historical operation data of the offshore wind farm is used to analyze the coupling related to the wake effect, a weight is assigned to each edge in the directed graph of the wind farm network, and an adjacency matrix of the wind farm network is determined;

[0015] The K-means clustering algorithm is used to cluster the directed graph of the wind farm network to determine the wind turbine clusters and the corresponding control centers.

[0016] In some embodiments, the method of generating a pitch control instruction based on a preset optimal control target according to the power control signal and the load reduction control signal is as follows: according to the uniform component corresponding to the power control signal and the cosine component and the sine component corresponding to the load reduction control signal, iterative optimization is performed with fatigue management and power optimization as the goals to generate a pitch control instruction.

[0017] In some embodiments, taking the maximum output power of the offshore wind farm as the control target, processing the wind turbine parameters and wind speed data of the offshore wind farm to determine the power control signal specifically includes:

[0018] Based on the wind turbine parameters of the wind turbine cluster, the wind speed of the wind turbine cluster is used as input, the shielding of the wind turbine against the incoming wind is used as the optimization parameter, and the maximum output power of the entire wind farm is used as the scheduling target to perform iterative optimization to determine the power control signal of each wind turbine cluster;

[0019] The wind speed data of the downstream wind turbine cluster is determined according to the wind speed data of the upstream wind turbine cluster.

[0020] In some embodiments, utilizing key features that affect the power output of the wind turbine and combining a dynamic linear time-invariant model of the wind turbine to generate the load reduction control signal specifically includes:

[0021] Obtain the nonlinear aerodynamic motion equations of the wind turbine and perform linearization to establish a linearized model of the dynamic equations;

[0022] The linearized model of aerodynamic load is converted into the state space model of wind turbine load by using MBC coordinate transformation. The state space model of wind turbine load is solved by control algorithm to generate load reduction control signal.

[0023] In a second aspect, the present invention provides a collaborative optimization pitch control system for an offshore wind farm;

[0024] An offshore wind farm collaborative optimization pitch control system, comprising:

[0025] A calculation processing unit, used to process the wind turbine parameters and wind speed data of the offshore wind farm with the maximum output power of the offshore wind farm as the control target, and determine the power control signal;

[0026] A single machine controller for utilizing key characteristics affecting wind turbine power output and combining a dynamic linear time-invariant model of the wind turbine to generate a load shedding control signal;

[0027] A control unit, used to obtain power control signals and load reduction control signals of each wind turbine cluster;

[0028] According to the power control signal and the load reduction control signal, based on the preset optimal control target, a pitch control instruction is generated to be transmitted to the corresponding pitch action actuator for pitch control;

[0029] The pitch action execution unit is used to obtain the pitch control instruction and execute the pitch control.

[0030] In a third aspect, the present invention provides an electronic device;

[0031] An electronic device comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-mentioned offshore wind farm collaborative optimization pitch control method.

[0032] In a fourth aspect, the present invention provides a computer-readable storage medium;

[0033] A computer-readable storage medium stores a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned offshore wind farm collaborative optimization pitch control method.

[0034] In a fifth aspect, the present invention provides a computer program product;

[0035] A computer program product includes a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned offshore wind farm collaborative optimization pitch control method.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. The technical solution provided by the present invention divides large offshore wind farms according to the relative positions of wind turbines and the influence of wind turbine movements, establishes an offshore wind farm collaborative optimization model influenced by dynamic wake based on the wind farm wake effect model on the basis of the wind turbine cluster division, and quickly solves the output power of different wind turbine clusters through a computing processing unit.

[0038] According to the single wind turbine operating status and environmental factors, key features are extracted to establish a simplified load model for describing the aerodynamic load of the wind turbine. The three pitch angles are converted into three components that decouple power control and load control using MBC coordinate transformation. The single-machine controller generates a load reduction control signal, which is combined with the power tracking control signal and the load reduction control signal to generate control instructions based on the optimal control target. This minimizes the communication dependence of different wind turbine clusters in the wind farm on the central control node. Compared with the traditional multi-wind turbine wake effect model, it reduces the complexity of the model, reduces the amount of calculation, and improves the stability and adaptability of the control system.

[0039] The application of this method can realize the coordinated control of offshore wind farms, taking into account fatigue management and power optimization at the same time, overcoming the shortcomings of traditional wind turbine pitch control strategies that only study the power generation performance and quality of a single machine, cannot execute coordinated control instructions, have high communication requirements, and have poor stability.

[0040] 2. The technical solution provided by the present invention is based on the collected wind farm environmental data and single-machine operation data. The central processing unit divides the wind turbines with a high degree of coupling into multiple smaller clusters according to the network weight value to divide the large offshore wind farm, greatly reducing the degree of communication dependence, and using computing and other units to achieve coordinated control of offshore wind farms, while taking into account fatigue management and power optimization. The application of this system can effectively reduce communication dependence, achieve the control goals of wind farm coordinated optimization and load reduction under the condition of low communication dependence, and effectively improve the power generation efficiency of wind farms and the safety of wind turbine operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0042] Figure 1 A schematic flow chart of a method for collaboratively optimizing pitch control of an offshore wind farm provided by an embodiment of the present invention;

[0043] Figure 2 A schematic diagram of a process for dividing a wind turbine cluster in an offshore wind farm provided by an embodiment of the present invention;

[0044] Figure 3 A schematic diagram of a flow chart of generating a pitch control instruction provided by an embodiment of the present invention;

[0045] Figure 4 A schematic diagram of the system architecture of a method for collaboratively optimizing pitch control of an offshore wind farm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0047] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "include" and "have" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0048] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0049] Embodiment 1

[0050] Traditional wind turbine pitch control strategies focus on studying only the power generation performance and power generation quality of a single machine, which makes it difficult to achieve coordinated control of wind farms, and cannot take into account both fatigue management and power optimization. In addition, they have high communication requirements and affect the response speed and stability of the system. Therefore, the present invention provides a method for collaborative optimization of offshore wind farm pitch control.

[0051] Next, combine Figure 1-Figure 3 , a method for coordinated optimization of pitch control of an offshore wind farm disclosed in this embodiment is described in detail. The method for coordinated optimization of pitch control of an offshore wind farm is applied to a control unit and comprises the following steps:

[0052] S1. Obtain power control signals and load reduction control signals of each wind turbine cluster.

[0053] In order to reduce the communication dependence of the coordinated optimization pitch control of the offshore wind farm, before S1 is executed, the offshore wind farm is divided into multiple wind turbine clusters by the central processing unit, as an implementation method, specifically including:

[0054] (1) Obtain the real-time operating data of offshore wind turbines, extract the real-time wind speed, wind direction, power output, system load and environmental status data from the real-time operating data, and perform data cleaning, anomaly detection and normalization processing.

[0055] In this embodiment, real-time operation data is collected through the SCADA system.

[0056] (2) The processed data are analyzed by correlation analysis to determine the key characteristics that affect wind turbine power output and load demand.

[0057] Specifically, the correlation analysis method may be Pearson correlation coefficient, Spearman rank correlation coefficient, etc.

[0058] In this embodiment, the final key features are determined by calculating the Pearson correlation coefficient between the processed data and the wind turbine power demand and load demand and screening according to the size of the Pearson correlation coefficient; the final key features include the effective wind speed at the blade.

[0059] (3) Construct a directed graph of wind farm network based on key features , the node set V represents each wind turbine, the edge set A represents the relationship between wind turbines, and W represents the adjacency matrix.

[0060] (4) Obtain the historical operation data of the offshore wind farm. Based on the data of the degree of influence of the wake on each wind turbine measured in the wind farm over a period of time in the historical operation data, analyze the degree of influence of the wake on each wind turbine, the overlap area of ​​the wake, the rotor diameter of the wind turbine, and the relative distance of the adjacent wind turbine arrays. Assign a weight to each edge in the directed graph of the wind farm network to indicate the strength of the coupling relationship between wind turbines. Then, the adjacency matrix of the wind farm network can be defined as , , where S represents the wake overlap area, D represents the wind turbine rotor diameter, and R represents the relative distance between adjacent wind turbine arrays.

[0061] Exemplarily, the historical operation data includes wind speed, air pressure and turbulence intensity before and after each wind turbine in the wind farm over a period of time. The wake influence intensity of each wind turbine affected by the wake is obtained by simulation using CFD simulation software.

[0062] The CFD simulation software is an existing software. When in use, the wind speed, air pressure and turbulence intensity before and after each wind turbine in the wind farm over a period of time are input into the CFD simulation software. After being processed by the CFD simulation software, the wake influence intensity corresponding to each wind turbine can be directly output.

[0063] The wake overlap area between each wind turbine and the upstream wind turbine, the wind turbine rotor diameter, and the relative distance between adjacent wind turbine arrays are collected. The wake overlap area between each wind turbine and the upstream wind turbine, the wind turbine rotor diameter, the relative distance between adjacent wind turbine arrays, and the wake influence intensity are regressed and analyzed to obtain the relationship between the wake and the wake influence intensity, which is expressed as:

[0064] ;

[0065] In the formula, is the regression coefficient, obtained through the above regression analysis.

[0066] Finally, according to , assign weights to each edge in the directed graph of the wind farm network.

[0067] (5) All mutually pointing nodes on the directed graph of the wind farm network are processed according to the preset judgment threshold.

[0068] Specifically, the weight value of the edge with a weight greater than the judgment threshold is subtracted from the weight value of the edge with a weight less than the judgment threshold, and the obtained weight difference is used as the weight difference for retaining the edge with a weight greater than the judgment threshold, and the edge with a weight less than the judgment threshold is deleted.

[0069] Among them, the judgment threshold is expressed as:

[0070] ;

[0071] In the formula, k represents the judgment parameter of deletion, Represents the average value of the wake strength.

[0072] (6) The K-means clustering algorithm is used to cluster the processed wind farm network directed graph.

[0073] Specifically, a similarity matrix and a standard Laplace matrix are constructed for the processed wind farm network directed graph, the constructed similarity matrix and the standard Laplace matrix are used as clustering sample set inputs, the number of clusters K and the number of iterations n are set, and the K-means clustering algorithm is run for clustering.

[0074] The K-means algorithm is a dynamic clustering algorithm that first performs a rough pre-classification, then gradually iterates and adjusts, and updates after a set period to achieve dynamic division of wind turbine clusters.

[0075] In order to achieve coordinated optimization control of offshore wind farms and realize fatigue management and power optimization at the same time, it is necessary to determine the power control signal and the load reduction control signal. In this embodiment, the power control signal is determined by the operation processing unit with the maximum output power of the offshore wind farm as the control target, and the real-time operation data and environmental data of the offshore wind farm are processed and determined and output to the control unit; the load reduction control signal is generated by the single-machine controller of the wind turbine cluster using the key features that affect the power output of the wind turbine, combined with the dynamic linear time-invariant model of the wind turbine, and output to the control unit.

[0076] As an implementation method, taking the maximum output power of the offshore wind farm as the control target, the specific process of processing the real-time operation data and environmental data of the offshore wind farm to determine the power control signal is as follows:

[0077] Step 1: Obtain the real-time wind speed, wind direction and wind turbine parameters of the wind farm, and calculate the output power of the upstream wind turbine cluster.

[0078] Exemplarily, the output power of the upstream wind turbine cluster is expressed as:

[0079] ;

[0080] Where n is the number of wind turbines in the upstream wind turbine cluster, ρ represents the air density, r represents the wind turbine rotor radius, v Indicates the real-time wind speed. represents the wind energy utilization coefficient, Indicates the degree of wind turbine blocking the incoming wind.

[0081] Step 2: Determine the ambient wind speed of the downstream wind turbine cluster based on the wind speed and wind direction of the upstream wind turbine cluster, and then calculate the output power of the downstream wind turbine cluster; take the maximum output power of the entire wind farm as the scheduling target, and calculate and distribute the power control signals of each wind turbine cluster by the central processing unit.

[0082] According to the wind speed and wind direction of the upstream wind turbine cluster, the ambient wind speed of the downstream wind turbine cluster is determined, and the ambient wind speed of the downstream wind turbine cluster is substituted into the above output power calculation formula to obtain the output power of the downstream wind turbine cluster.

[0083] Taking the maximum output power of the entire wind farm as the dispatching target, the power of each wind turbine cluster is iteratively calculated within the entire farm, expressed as:

[0084] ;

[0085] In the formula, Represents the total output power of the wind farm, Indicates iThe output power of a wind turbine cluster, represents the air density, represents the wind turbine rotor radius, is the wind speed at the wind turbine, Indicates the shielding of incoming wind by the wind turbine. , Indicates the rated power of the wind turbine.

[0086] In this embodiment, the power is described as a function of the shielding condition of the wind turbine to the incoming wind to realize the solution of the power control instruction. Combined with the motion equation of the wind rotor, the wind turbine's pitch angle is controlled to achieve the corresponding shielding condition to realize the power control of the wind turbine. The motion equation of the wind rotor is expressed as:

[0087] ;

[0088] In the formula, is the tower moving speed, is the moment of inertia of the wind wheel, is the wind speed at the wind turbine, is the wind wheel angular velocity, is the generator torque, is the uniform component of the pitch after coordinate transformation, that is, The first component in is the partial derivative of the blade torque with respect to the effective wind speed, is the partial derivative of the blade torque with respect to the pitch angle.

[0089] Specifically, in the iterative optimization process, the real-time wind speed of the wind turbine cluster is used as input. As the optimization parameters of the intelligent optimization algorithm, an optimal or near-optimal set of control variables is solved by iterative calculation through the intelligent optimization algorithm. Here, the intelligent optimization algorithm can be an ant colony algorithm, a gray wolf algorithm, or a particle swarm algorithm. This embodiment does not improve the intelligent optimization algorithm, and will not be repeated here.

[0090] In this step, considering that the wind speed and wind direction of the downstream wind turbines are different due to the shielding of the upstream wind turbines, the downstream wind speed is calculated using the numerical simulation method according to the wind speed and wind direction of the upstream wind turbine cluster, and then the output power calculation formula is brought in to calculate the power of the downstream wind turbines. The numerical simulation method can calculate the downstream wind speed using existing engineering wake models, such as the Jensen model, the Frandsen model, and the Gaussian model.

[0091] Next, taking the Jensen model as an example, the process of determining the ambient wind speed of the downstream wind turbine cluster based on the wind speed and wind direction of the upstream wind turbine cluster is specifically described:

[0092] Assuming that the wake profile is flat-topped, the velocity attenuation coefficient can be derived from the law of conservation of mass as follows:

[0093] ;

[0094] In the formula, is the velocity attenuation coefficient, It is the axial distance downstream of the wind turbine, which can reflect the wind direction; is the thrust coefficient of the wind turbine, D is the blade diameter of the upstream wind turbine, k is the wake attenuation constant, the wind speed at the downstream wind turbine is expressed as:

[0095] ;

[0096] In the formula, Indicates the natural wind speed, that is, the real-time wind speed at the upstream wind turbine cluster.

[0097] In some embodiments, the downstream wind speed may also be determined using a fluid dynamics model.

[0098] Specifically, the Navier-Stokes equations that control the entire flow field in the wind farm are established for numerical solution. If the fluid mechanics model is the EVM wake model, assuming that the wake region is symmetrical with the two tail axes, the wake of the wind turbine with an axisymmetric structure can be calculated using the eddy viscosity constraint and the Reynolds time-averaged NS equations, which can be expressed as:

[0099] ;

[0100] In the formula, Indicates the natural wind speed, Indicates the wind speed at the downstream wind turbine, that is, the axial distance downstream of the wind turbine l The wind speed at represents the initial wind speed decay at the wake centerline, b Indicates the wake width.

[0101] As an implementation method, the specific process of generating a load reduction control signal by utilizing key features that affect the power output of a wind turbine and combining a dynamic linear time-invariant model of the wind turbine is as follows:

[0102] (a) Based on the nonlinear aerodynamic motion equation of the wind turbine, the blades are regarded as rigid blades, the nonlinear aerodynamic motion equation is linearized near the steady-state operating point, and the key features are extracted to establish a linearized model of the dynamic equation to describe the load condition of the wind turbine.

[0103] Among them, the nonlinear aerodynamic motion equation is expressed as:

[0104] ;

[0105] In the formula, M is the mass matrix of the system, C is the damping matrix of the system, B is the stiffness matrix of the system, express The thrust of natural wind on the wind turbine rotor at any moment, Represents the displacement of the tower top.

[0106] Specifically, the above nonlinear aerodynamic motion equation describes the tower movement caused by thrust. By performing small perturbations on the variables in the nonlinear aerodynamic motion equation near the steady-state operating point, the linearized equation is obtained using Taylor expansion to obtain the aerodynamic load linearization model.

[0107] The linearized model of aerodynamic load is expressed as:

[0108] ;

[0109] In the formula, For the i The change of bending moment in the flapping direction of the blade root; For the i The pitch angle of each blade; For the i The effective wind speed in the flapping direction at each blade; K is The partial derivative of the bending moment with respect to the pitch angle at the operating point is ， is the partial derivative of the bending moment with respect to the relative wind speed at the working point.

[0110] The expression of effective wind speed is related to the change of blade azimuth angle, which can be expressed as:

[0111] ;

[0112] In the formula, is the axial velocity at the top of the tower; For the i The absolute wind speed of each blade; For the i The spatial position angle of the blade is 0° when the blade is pointing vertically upward; R is the blade radius; H is the tower height.

[0113] The aerodynamic load linearization model is a periodic time-varying model. The periodic time-varying model refers to the load model directly established by the physical model. The expression of the bending moment will contain the impeller azimuth angle. The azimuth angle of the blade changes all the time during one rotation, which is unfavorable to the control. Therefore, it needs to be transformed into a time-invariant model through coordinate transformation.

[0114] (b) The MBC coordinate transformation is used to transform the linearized aerodynamic load model into a linear time-invariant model expressed in a stationary coordinate system.

[0115] Since there are periodic coefficients related to the impeller azimuth angle in the periodic time-varying model, and the three blade pitch angles are related to each other and jointly affect the wind turbine output power and aerodynamic load, it is difficult to use traditional control strategies to design a controller. In order to apply the linear time-invariant model, in this embodiment, the MBC coordinate transformation matrix is ​​multiplied by the three blade pitch angles of the three-blade wind turbine, and then the three blade pitch angles of the three-blade wind turbine are transformed into a uniform component related to the generated power, and a cosine component and a sine component related to the blade root load.

[0116] Here, the MBC coordinate transformation matrix is ​​expressed as:

[0117] .

[0118] After MBC coordinate transformation, the load calculation expression is converted into a linear steady form. The unbalanced loads borne by the wind rotor are mainly pitch bending moment and yaw bending moment. The variables such as the front and rear displacement and speed of the tower top are selected as the state space, the pitch angle is used as the control input, and the output variables are pitch bending moment and yaw bending moment. The state space model of wind turbine load is established.

[0119] Among them, the state space model of wind turbine load is expressed as:

[0120] ;

[0121] In the formula, A represents the system matrix, B represents the input matrix, represents the wind speed input matrix, C represents the output matrix, represents the displacement and velocity of the tower top, that is, ; The control input is the pitch angle, which is obtained by the cosine and sine components express, , are the cosine and sine components respectively, Indicates wind speed input.

[0122] Here, A , B , C is a coefficient matrix, and the selection of the specific coefficient matrix is ​​determined by those skilled in the art according to actual conditions.

[0123] (c) After obtaining the state space model of the wind turbine load, the PID control algorithm is used to solve the state space model of the wind turbine load to obtain The value of .

[0124] S2. Generate a pitch control instruction according to the power control signal and the load reduction control signal based on a preset optimal control target, and transmit it to the corresponding pitch action actuator for pitch control.

[0125] Specifically, in the above steps, a unified component is obtained from the power control signal, and a cosine component and a sine component are obtained from the load reduction control signal; in view of the wind farm collaborative optimization target and the load reduction requirement, a control instruction is generated based on the optimal control target, and the pitch angle is adjusted so that J(U) reaches a minimum value, thereby obtaining the optimal component control instruction. J(U) It is expressed as:

[0126] ;

[0127] in, represents the blade pitch angle of the wind turbine, It indicates the set pitch angle. It should be noted that the pitch angle at this time is the component after coordinate transformation, and the set value is calculated by the processing unit and the stand-alone controller. and is the weight matrix, A fatigue model that represents the wind turbine system and reflects the fatigue conditions of other wind turbine components.

[0128] Here, The first item represents the tracking of the instruction, and the second item represents the operating condition of the wind turbine. The actual meaning is to take into account both fatigue management and power optimization, allowing each wind turbine to make small changes based on the given instruction, so as to better achieve both fatigue management and power optimization.

[0129] In this embodiment, a fatigue model of the wind turbine system is established through the key physical features extracted in the above steps.

[0130] Specifically, state variables such as the front and rear vibration modes of the tower and the average flapping modes of the blades are selected to describe the fatigue caused by oscillations due to random interference of wind and operation of wind turbines. For example, the description of tower vibration, blade vibration, shaft vibration caused by frequent changes in torque, etc., which are related to the changes in the pitch angle of the wind turbine, can describe the operating conditions of a single machine, but the modeling is complex and lacks explicit expression.

[0131] In this embodiment, the displacement and speed of related physical quantities are limited to reduce fatigue caused by oscillation and frequent changes of the mechanism. The fatigue model is expressed as:

[0132] ;

[0133] in, x The forward and backward displacement of the tower can be selected, and its first-order derivative represents the moving speed of the tower, represents the blade pitch angle of the wind turbine, It is a coefficient set by technical personnel according to different requirements. During the control process, the vibration of the tower and the speed of change of the pitch angle are limited to reduce the fatigue of the wind turbine vibration and the pitch bearing caused by frequent changes.

[0134] Then, the three component control instructions are transformed through the MBC coordinate inverse transformation to obtain the independent pitch angle instruction in the rotating coordinate system, and sent to the pitch actuator to perform the pitch action to complete the independent pitch control.

[0135] Embodiment 2

[0136] Combination Figure 4 Based on the first embodiment, this embodiment discloses a collaborative optimization pitch control system for an offshore wind farm, including a data processing unit, a central processing unit, an operation processing unit, a stand-alone controller, a communication unit, a control unit, a detection unit and a pitch action execution unit.

[0137] The data processing unit is used to access the SCADA system to collect real-time operation data, including the real-time wind speed, wind direction, power output, system load and environmental status data of the wind farm, and perform data cleaning, anomaly detection and other processing on the collected data.

[0138] The central processing unit is used to define the network of wind farms based on the wake effect of offshore wind farms and the influence of wind turbine action, divide wind turbines with a high degree of coupling into multiple smaller clusters according to the network weight value, and analyze the changes in the wind farm network degree based on the control variables and wind turbine dynamic model provided by the distributed cluster optimization, determine whether to update the cluster division results, and update the wind farm network and cluster division results within a certain period.

[0139] The operation processing unit is used to process the wind turbine parameters and wind speed data of the offshore wind farm with the maximum output power of the offshore wind farm as the control target, and determine the power control signal.

[0140] The detection unit is used to detect physical quantities such as wind turbine rotation speed, bending moment at blade root, and ambient wind speed.

[0141] The single-machine controller is used to generate the load reduction control signal of the wind turbine based on the optimal control index calculation according to the single-machine operation information collected by the detection unit and the wind turbine load model. The control unit is used to receive the power control signal output by the operation processing unit and the load reduction control signal output by the single-machine controller, and then calculate the control instructions of the three blade components and further obtain the actual pitch angles of the three blades through the MBC coordinate inverse transformation and send them to the pitch action execution unit.

[0142] The pitch action execution unit is used to receive the control signal from the control unit and realize the pitch action through the electrical actuator.

[0143] Embodiment 3

[0144] Embodiment 3 of the present invention provides an electronic device, including a memory and a processor, and computer instructions stored in the memory and running on the processor. When the computer instructions are executed by the processor, the steps of the above-mentioned offshore wind farm collaborative optimization pitch control method are completed.

[0145] Embodiment 4

[0146] Embodiment 4 of the present invention provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the steps of the above-mentioned offshore wind farm collaborative optimization pitch control method are completed.

[0147] Embodiment 5

[0148] Embodiment 5 of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned offshore wind farm collaborative optimization pitch control method.

[0149] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0150] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0151] These computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0152] The description of each embodiment in the above embodiments has different emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0153] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for coordinated optimization of pitch control of an offshore wind farm, characterized in that: include: Obtain power control signals and load reduction control signals of each wind turbine cluster; According to the power control signal and the load reduction control signal, based on the preset optimal control target, a pitch control instruction is generated to be transmitted to the corresponding pitch action actuator for pitch control; The power control signal is determined by processing the wind turbine parameters and wind speed data of the offshore wind farm by the computing processing unit with the maximum output power of the offshore wind farm as the control target; the load reduction control signal is generated by the single-machine controller of the wind turbine cluster using the key features that affect the power output of the wind turbine and combining the dynamic linear time-invariant model of the wind turbine; The generating of the pitch control instruction according to the power control signal and the load reduction control signal based on the preset optimal control target is specifically: performing iterative optimization with fatigue management and power optimization as the goal according to the uniform component corresponding to the power control signal and the cosine component and sine component corresponding to the load reduction control signal to generate the pitch control instruction; Taking the maximum output power of the offshore wind farm as the control target, processing the wind turbine parameters and wind speed data of the offshore wind farm to determine the power control signal specifically includes: Based on the wind turbine parameters of the wind turbine cluster, the wind speed of the wind turbine cluster is used as input, the shielding of the wind turbine against the incoming wind is used as the optimization parameter, and the maximum output power of the entire wind farm is used as the scheduling target to perform iterative optimization to determine the power control signal of each wind turbine cluster; Wherein, the wind speed data of the downstream wind turbine cluster is determined according to the wind speed data of the upstream wind turbine cluster; Using the key features that affect the power output of the wind turbine and combining the dynamic linear time-invariant model of the wind turbine to generate the load reduction control signal specifically includes: Obtain the nonlinear aerodynamic motion equations of the wind turbine and perform linearization to establish a linearized model of the dynamic equations; The linearized model of aerodynamic load is converted into the state space model of wind turbine load by using MBC coordinate transformation. The state space model of wind turbine load is solved by control algorithm to generate load reduction control signal.

2. The offshore wind farm coordinated optimization pitch control method according to claim 1, characterized in that: The wind turbine cluster is determined by establishing a wind farm network division by constructing a coupling relationship matrix between wind turbines through a central processing unit.

3. The offshore wind farm coordinated optimization pitch control method according to claim 2, characterized in that: Constructing a coupling relationship matrix between wind turbines to establish a wind farm network, and dividing the wind turbine clusters specifically includes: Acquire real-time operation data of the offshore wind farm, process the real-time operation data by correlation analysis method, and determine key features; Based on the key features, a directed graph of the wind farm network is constructed; historical operation data of the offshore wind farm is used to analyze the coupling related to the wake effect, a weight is assigned to each edge in the directed graph of the wind farm network, and an adjacency matrix of the wind farm network is determined; The K-means clustering algorithm is used to cluster the directed graph of the wind farm network to determine the wind turbine clusters and the corresponding control centers.

4. A coordinated optimization pitch control system for an offshore wind farm, characterized in that: include: A calculation processing unit, used to process the wind turbine parameters and wind speed data of the offshore wind farm with the maximum output power of the offshore wind farm as the control target, and determine the power control signal; A single machine controller for utilizing key characteristics affecting wind turbine power output and combining a dynamic linear time-invariant model of the wind turbine to generate a load shedding control signal; A control unit, used to obtain power control signals and load reduction control signals of each wind turbine cluster; According to the power control signal and the load reduction control signal, based on the preset optimal control target, a pitch control instruction is generated to be transmitted to the corresponding pitch action actuator for pitch control; A pitch action execution unit, used to obtain a pitch control instruction and execute pitch control; The generating of the pitch control instruction according to the power control signal and the load reduction control signal based on the preset optimal control target is specifically: performing iterative optimization with fatigue management and power optimization as the goal according to the uniform component corresponding to the power control signal and the cosine component and sine component corresponding to the load reduction control signal to generate the pitch control instruction; Taking the maximum output power of the offshore wind farm as the control target, processing the wind turbine parameters and wind speed data of the offshore wind farm to determine the power control signal specifically includes: Based on the wind turbine parameters of the wind turbine cluster, the wind speed of the wind turbine cluster is used as input, the shielding of the wind turbine against the incoming wind is used as the optimization parameter, and the maximum output power of the entire wind farm is used as the scheduling target to perform iterative optimization to determine the power control signal of each wind turbine cluster; Wherein, the wind speed data of the downstream wind turbine cluster is determined according to the wind speed data of the upstream wind turbine cluster; Using the key features that affect the power output of the wind turbine and combining the dynamic linear time-invariant model of the wind turbine to generate the load reduction control signal specifically includes: Obtain the nonlinear aerodynamic motion equations of the wind turbine and perform linearization to establish a linearized model of the dynamic equations; The linearized model of aerodynamic load is converted into the state space model of wind turbine load by using MBC coordinate transformation. The state space model of wind turbine load is solved by control algorithm to generate load reduction control signal.

5. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the offshore wind farm collaborative optimization pitch control method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the steps of the offshore wind farm collaborative optimization pitch control method described in any one of claims 1 to 3 are implemented.

7. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the offshore wind farm collaborative optimization pitch control method described in any one of claims 1 to 3 are implemented.

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