CN114841090A - Wind power plant grouping optimization control method, system, device and medium - Google Patents
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Abstract
The invention relates to the technical field of wind power plant control, and particularly provides a wind power plant grouping optimization control method, a system, a device and a medium, aiming at solving the problems of higher calculation cost and communication cost among wind generation sets and poor applicability of an optimization control method in the process of performing optimization control on a wind power plant. For the purpose, the method can establish the wake undirected graph of the wake relation degree among the wind turbine generators in the wind power plant according to the wake effect, divide the wind power plant into a plurality of sub-wind power plants according to the wake undirected graph, obtain the optimal control strategy of each wind turbine generator in each sub-wind power plant by taking the sub-wind power plants as control objects, and optimally control each sub-wind power plant. Because the number of the wind turbine generators in the sub wind farm is small, in the process of optimal control, the calculation cost and the communication cost among the wind turbine generators can be greatly reduced, and the influence of the space change of the boundary conditions on the optimal control of the wind farm can also be reduced.
Description
Technical Field
The invention relates to the technical field of wind power plant control, and particularly provides a wind power plant grouping optimization control method, a wind power plant grouping optimization control system, a wind power plant grouping optimization control device and a wind power plant grouping optimization control medium.
Background
In a wind power plant, an upstream wind turbine generator unit can generate wake flow after absorbing wind energy, and the power generation efficiency of a downstream wind turbine generator unit located in a wake flow area is reduced due to the influence of the wake flow. Meanwhile, if only the maximization of each wind turbine is taken as an optimization target, the output power of the whole wind power plant is probably not maximized due to the influence of wake flow. In order to improve the overall output power of the wind power plant, an active yaw control method is mostly adopted, and the active yaw control is carried out on the upstream wind power generation unit, so that the wake flow of the upstream wind power is deflected, the downstream wind power generation unit is far away from the wake flow area, and the overall output power of the wind power plant is further maximized. Currently, a centralized optimization control method is mostly adopted to perform optimization control on a wind farm, that is, each wind turbine generator in the wind farm is coordinately controlled through a special control system, so that the output power of the wind farm is maximized.
However, with the increase of the number of wind turbines in the wind farm, the calculation cost of optimal control in the wind farm and the communication cost of communication between the wind turbines are increased; and with the increase of the number of the wind generation sets, the area of the wind power plant is increased, and the spatial change of the boundary conditions is more obvious, so that the applicability of the centralized optimization control method is greatly reduced.
Accordingly, there is a need in the art for a new wind farm control scheme to address the above-mentioned problems.
Disclosure of Invention
In order to overcome the defects, the invention is provided to solve or at least partially solve the problems that in the process of optimally controlling the wind power plant, the calculation cost and the communication cost between the wind generation sets are high, and the applicability of the optimal control method is poor.
In a first aspect, the present invention provides a wind farm group optimization control method, comprising:
establishing a wake undirected graph of the wake relation degree between wind generating sets in the wind power plant according to the wake effect;
according to the wake undirected graph, dividing the wind power plant into a plurality of sub wind power plants, wherein each sub wind power plant comprises a plurality of wind power generation sets;
aiming at each sub wind power plant, acquiring an optimal control strategy of each wind turbine generator in the sub wind power plant by taking the maximum output power of the sub wind power plant as a control target;
and respectively carrying out optimization control on each sub wind power plant according to the optimal control strategy of each sub wind power plant.
In one technical solution of the wind farm grouping optimization control method, the step of "establishing a wake undirected graph of wake influence degrees among wind turbine generators in the wind farm according to wake effects" includes:
determining the influence degree of the wake flow of each upstream wind turbine generator on the wake flow of the downstream wind turbine generator according to the wake flow effect;
establishing a wake flow weight matrix of the wind power plant according to the wake flow influence degree;
establishing a wake directed graph of the wind power plant according to the wake weight matrix; the nodes of the wake directed graph are wind turbines in the wind power plant, and the adjacent matrix of the wake directed graph is the wake weight matrix;
and converting the wake directed graph into the wake undirected graph so as to convert the influence degree of the wake of the upstream wind turbine generator on the wake of the downstream wind turbine generator into the wake relation degree between the upstream wind turbine generator and the downstream wind turbine generator.
In one technical solution of the wind farm grouping optimization control method, the step of determining the wake influence degree of the wake of each upstream wind turbine generator on the downstream wind turbine generator according to the wake effect includes determining the wake influence degree of the wake of each upstream wind turbine generator on the downstream wind turbine generator according to the following formula:
wherein, w ij The degree of wake effect of the upstream wind turbine i on the downstream wind turbine j, D T Is the diameter of the wind wheel of the wind turbine, x ij The distance between the flow directions of the upstream wind turbine generator i and the downstream wind turbine generator j,
is the average wind speed at j wind wheel of downstream wind turbine generator set, A overlap The area overlapping ratio of the wake flow of the upstream wind turbine generator i at the wind wheel of the downstream wind turbine generator j and the wind wheel of the downstream wind turbine generator is shown; calculating the area overlap ratio by the following formula:
In one technical solution of the wind farm grouping optimization control method, the step of dividing the wind farm into a plurality of sub-wind farms according to the wake undirected graph, wherein each sub-wind farm comprises a plurality of wind turbines respectively comprises:
according to the wake undirected graph and a set of top-ranked wind generation sets in the wind power plant, dividing the wind power plant into a plurality of wind power plant subsets by applying a breadth-first search algorithm;
aiming at each wind power plant subset, establishing a wind power plant subgraph corresponding to the wind power plant subset according to the wind power plant subset and an adjacent matrix of the wake undirected graph;
according to the wind farm subgraph, a spectral clustering algorithm is applied to divide the wind farm subset into a plurality of sub wind farms;
the head-end wind turbine generator is a wind turbine generator which is not affected by wake flow in the inflow wind direction.
In one technical solution of the wind farm grouping optimization control method, the step of dividing the wind farm into a plurality of wind farm subsets by applying a breadth-first search algorithm according to the wake undirected graph and a set of top-ranked wind turbines in the wind farm includes:
aiming at each head wind turbine generator, applying a breadth-first search algorithm, taking the current head wind turbine generator as an initial node, and retrieving all nodes in the wake undirected graph to obtain a node set formed by all nodes communicated with the current head wind turbine generator;
comparing the node set corresponding to the current head-arranged wind turbine generator with the node sets corresponding to other head-arranged wind turbine generators, and if the node set corresponding to the current head-arranged wind turbine generator is the same as the node sets corresponding to the other head-arranged wind turbine generators, omitting the node set corresponding to the current head-arranged wind turbine generator;
and taking the remaining plurality of node sets as a plurality of wind farm subsets.
In one technical solution of the wind farm grouping optimization control method, the step of dividing the wind farm subset into a plurality of sub-wind farms by applying a spectral clustering algorithm according to the wind farm subgraph comprises:
aiming at each wind power plant subgraph, determining the number of head-ranked wind generation sets in a wind power plant subset corresponding to the current wind power plant subgraph;
applying a spectral clustering algorithm, and constructing a degree matrix of the spectral clustering algorithm according to an adjacent matrix of the current sub-graph of the wind power plant;
constructing a Laplace matrix of a spectral clustering algorithm according to the degree matrix;
normalizing the Laplace matrix to obtain a normalized Laplace matrix;
establishing a first matrix according to the standardized Laplace matrix and the number of the wind generation sets at the top of the row;
normalizing each row vector in the first matrix to obtain a second matrix;
clustering the row vectors of the second matrix by applying a K-means clustering algorithm according to the second matrix to obtain a clustering result;
and according to the clustering result, taking the wind turbine generator corresponding to the clustering result as a sub wind power plant to obtain a plurality of sub wind power plants.
In one technical solution of the wind farm grouping optimization control method, the step of "constructing a degree matrix of a spectral clustering algorithm according to an adjacent matrix of a current wind farm subgraph" includes:
acquiring the sum of each row of elements in the adjacency matrix according to the adjacency matrix of the wind power plant subgraph;
constructing the degree matrix according to the sum of each row of elements in the adjacent matrix; and/or the presence of a gas in the gas,
the step of constructing a laplacian matrix of a spectral clustering algorithm based on the degree matrix comprises constructing the laplacian matrix according to the following formula:
L＝D-W
wherein L is the Laplac matrix, D is the degree matrix, and W is an adjacent matrix of the wind farm subgraph; and/or the presence of a gas in the gas,
the step of "normalizing said laplace matrix to obtain a normalized laplace matrix" comprises obtaining a normalized laplace matrix according to the following formula:
L sym ＝D -1/2 LD -1/2
wherein L is sym Is the normalized Laplace matrix; and/or the presence of a gas in the gas,
the step of establishing a first matrix according to the standardized Laplace matrix and the number of the wind generation sets at the top of the row comprises the following steps:
acquiring k eigenvectors with the minimum eigenvalues in the standardized Laplace matrix according to the number k of the wind generation sets at the top of the row;
establishing the first matrix according to the k eigenvectors; and/or the presence of a gas in the gas,
the step of "normalizing each row vector in the first matrix to obtain a second matrix" includes normalizing each row vector in the first matrix according to the following formula to obtain the second matrix:
wherein, t ij Is an element located in the ith row and jth column of the second matrix, a ij Being the element located in the ith row and jth column of the first matrix,
is the sum of the squares of the elements of the ith row of the first matrix.
In a second aspect, the present invention provides a wind farm group optimization control system, the system comprising:
the wake undirected graph establishing module is configured to establish a wake undirected graph of the degree of wake relation among wind turbine sets in the wind power plant according to a wake effect;
the sub wind power plant dividing module is configured to divide the wind power plant into a plurality of sub wind power plants according to the wake undirected graph, and each sub wind power plant comprises a plurality of wind power generation sets;
the sub wind power plant control strategy acquisition module is configured to acquire an optimal control strategy of each wind turbine generator in each sub wind power plant with the maximum output power of the sub wind power plant as a control target for each sub wind power plant;
and the wind power plant grouping optimization control module is configured to perform optimization control on each sub wind power plant according to the optimal control strategy of each sub wind power plant.
In a third aspect, a control device is provided, comprising a processor and a memory device adapted to store a plurality of program codes adapted to be loaded and run by the processor to perform a wind farm group optimization control method according to any of the above-mentioned aspects of the wind farm group optimization control method.
In a fourth aspect, a computer readable storage medium is provided, having stored therein a plurality of program codes adapted to be loaded and run by a processor to perform the wind farm group optimization control method of any one of the above-described aspects of the wind farm group optimization control method.
One or more technical schemes of the invention at least have one or more of the following beneficial effects:
in the technical scheme of implementing the method, the wake undirected graph of the wake relation degree between the wind turbine generators in the wind power plant can be established according to the wake effect, the wind power plant is divided into a plurality of sub wind power plants according to the wake undirected graph, the sub wind power plants are used as control objects, the optimal control strategy of each wind turbine generator in each sub wind power plant is obtained, and each sub wind power plant is optimally controlled according to the optimal control strategy. Through the configuration mode, the wind power plant can be divided into the plurality of sub wind power plants, the sub wind power plants are subjected to optimization control, and the number of the wind power generation sets in the sub wind power plants is small, so that the calculation cost and the communication cost among the wind power generation sets can be greatly reduced in the optimization control process. Meanwhile, because the number of the wind generation sets in the sub wind power plant is small, the area is small, the boundary condition space change can be reduced, the influence of the boundary condition space change on the wind power plant optimization control process is reduced, and the applicability of the wind power plant grouping optimization control method is higher.
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The disclosure of the present invention will become more readily understood with reference to the accompanying drawings. As is readily understood by those skilled in the art: these drawings are for illustrative purposes only and are not intended to constitute a limitation on the scope of the present invention. Wherein:
FIG. 1 is a flow chart illustrating the main steps of a wind farm group optimization control method according to one embodiment of the present invention;
FIG. 2 is a schematic flow chart of the main steps of dividing a wind farm into a plurality of sub-wind farms according to one embodiment of the invention;
FIG. 3 is a schematic flow chart of the main steps for obtaining an optimal control strategy for each sub-wind farm according to one embodiment of the present invention;
FIG. 4 is a schematic flow chart of main steps of dividing a wind farm into a plurality of sub-wind farms according to another embodiment of the invention;
FIG. 5 is a schematic view of wind speed distribution at hub height of a wind turbine set in a wind farm according to an example of embodiment of the present invention;
FIG. 6 is a schematic representation of the wake effect level of a wind park in a wind farm according to an example of embodiment of the present invention;
FIG. 7 is a schematic illustration of sub-wind farm partitioning according to an example of an embodiment of the present invention;
FIG. 8 is a schematic diagram comparing the optimization control results of a wind farm group optimization control method with a traditional optimization control method and a centralized optimization control method according to an example of the embodiment of the invention;
FIG. 9 is a schematic diagram of the main structure of a wind farm group optimization control system according to an embodiment of the invention.
Detailed Description
Some embodiments of the invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and are not intended to limit the scope of the present invention.
In the description of the present invention, a "module" or "processor" may include hardware, software, or a combination of both. A module may comprise hardware circuitry, various suitable sensors, communication ports, memory, may comprise software components such as program code, or may be a combination of software and hardware. The processor may be a central processing unit, a microprocessor, a digital signal processor, or any other suitable processor. The processor has data and/or signal processing functionality. The processor may be implemented in software, hardware, or a combination thereof. Non-transitory computer readable storage media include any suitable medium that can store program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random-access memory, and the like. The term "a and/or B" denotes all possible combinations of a and B, such as a alone, B alone or a and B. The term "at least one A or B" or "at least one of A and B" means similar to "A and/or B" and may include only A, only B, or both A and B. The singular forms "a", "an" and "the" may include the plural forms as well.
Referring to fig. 1, fig. 1 is a flow chart illustrating main steps of a wind farm grouping optimization control method according to an embodiment of the invention. As shown in fig. 1, the wind farm grouping optimization control method in the embodiment of the present invention mainly includes the following steps S101 to S104.
Step S101: establishing a wake undirected graph of the wake relation degree between wind generating sets in the wind power plant according to the wake effect;
in the embodiment, a wake undirected graph representing the degree of wake relationship between wind turbines in a wind farm under current inflow wind conditions can be established according to the wake effect. The inflow wind conditions include, among other things, an inflow wind speed and an inflow wind direction.
In one embodiment, the position coordinates of each wind turbine can be determined by using an inertial coordinate system according to the positions of the wind turbines in the wind farm, and the wind turbines are numbered.
In one embodiment, parameters of the wind turbine in the wind farm, such as the hub height of the wind turbine, the diameter of the wind turbine, the power-wind speed curve, the thrust-wind speed curve, and the like, may be set.
In one embodiment, a simulation model of a wind farm may be set up to determine wake effects of the wind farm. In the present embodiment, an engineering wake model based on an analytic method may be adopted as a simulation model of a wind farm. Firstly, setting solving parameters of an engineering wake flow model, and setting a corresponding wake flow velocity loss model, a corresponding wake flow turbulence model, a corresponding wake flow conversion model and a corresponding superposition model. Wherein the wake velocity deficit model may be obtained according to the following equation (1):
u in formula (1) z Is the free stream wind speed u of the region of the wind turbine z z Is the wake velocity of the region in which the wind turbine z is located, a z Is an axial induction factor, k, of the wind turbine generator z e Is the wake expansion ratio, m U,z (γ z ) For wake recovery of the rate parameter, gamma z Is the yaw angle, Δ x, of the wind turbine z z Is the distance between the downstream wind turbine and the wind turbine z, and DT is the diameter of the wind turbine.
m U,z (γ z ) Can be obtained according to the following equation (2):
m in formula (2) U,1 ＝M U,2 ＝M U,n Parameter defining the width of the wake expansion for the near-wake zone, M U,3 ＝M U,4 ＝M U,f Parameter defining the width of the wake expansion for the far wake zone, M U,5 ＝M U,6 ＝M U,m Defining parameters of the width of wake expansion for the mixed area, wherein the value range of z at least comprises 1,2, 3, 4, 5, 6, n, m and f, wherein 1,2 and n represent wind turbines positioned in a near wake area, 3, 4 and f represent wind turbines positioned in a far wake area, and 5, 6 and m represent wind turbines positioned in the mixed area; alpha is alpha U And beta U And restoring the rate parameters for the downstream tail flows in different areas.
The wake turbulence model may be obtained by the following equation (3):
wherein, Delta I z For additional turbulence intensity of the wind turbine z, I ∞ Is the ambient turbulence intensity.
The wake deflection model may be obtained using the following equation (4) -equation (8):
where δ is wake deflection distance, x 0 Gamma is the yaw angle of the wind turbine generator, C T Is the thrust coefficient of the wind turbine, I is the turbulence intensity of the wind turbine, k y ，k z The change rate of the wake flow in the horizontal and vertical directions, x is the distance from the downstream of the wind wheel to the wind wheel, alpha * ，β * In order to be an empirical parameter,
σ y 、σ z is the median value of the calculation process.
The overlay model can be obtained by the following equation (9):
wherein u is i The method comprises the following steps that the wind turbine generator is positioned in a wake flow superposition area of N wind turbine generators at the upstream, wherein the wind turbine generator is the inflow wind speed of a wind turbine generator i; u. of j The inflow wind speed u in front of the upstream fan unit j ji The wind speed of the wake area of the wind turbine at the position of the wind turbine i.
In one embodiment, simulation control can be performed by adopting wake flow velocity loss models such as Jensen and BPA, wake flow deflection models such as jimeenez and ishihara, and wake flow superposition models such as linear superposition and maximum superposition.
In one embodiment, a computational fluid dynamics based numerical simulation model may also be used as the simulation model for the wind farm.
Step S102: according to the wake undirected graph, the wind power plant is divided into a plurality of sub wind power plants, and each sub wind power plant comprises a plurality of wind power generation sets.
In this embodiment, the wind farm may be divided into a plurality of sub-wind farms according to the wake relationship degree represented by the wake undirected graph, that is, the wind turbine generator with the higher wake relationship degree is divided into one sub-wind farm.
Step S103: and aiming at each sub wind power plant, acquiring the optimal control strategy of each wind turbine generator in the sub wind power plant by taking the maximum output power of the sub wind power plant as a control target.
In this embodiment, each sub-wind farm may be used as a control unit, the output power of the sub-wind farm is obtained through simulation of a simulation model of the wind farm, and the optimal control strategy of each wind turbine in the sub-wind farm is obtained by using the maximization of the output power as a control target.
In one embodiment, the control variables of the wind turbines may be updated using an optimization algorithm to obtain an optimal control strategy for each wind turbine in the sub-wind farm.
In one embodiment, the control variable may be yaw angle.
Step S104: and respectively carrying out optimization control on each sub wind power plant according to the optimal control strategy of each sub wind power plant.
In this embodiment, local communication between the wind turbine generators in the sub wind farms may be controlled according to the optimal control strategy of each sub wind farm obtained in step S103, so as to implement optimal control on each sub wind farm.
Based on the steps S101 to S104, the embodiment of the present invention can establish a wake undirected graph of the wake relationship degree between wind turbine generators in the wind farm according to the wake effect, divide the wind farm into a plurality of sub-wind farms according to the wake undirected graph, obtain the optimal control strategy for each wind turbine generator in each sub-wind farm with the sub-wind farms as the control objects, and perform optimal control on each sub-wind farm according to the optimal control strategy. Through the configuration mode, the wind power plant can be divided into the plurality of sub wind power plants, the sub wind power plants are subjected to optimization control, and the number of the wind generating sets in the sub wind power plants is small, so that the calculation cost and the communication cost among the wind generating sets can be greatly reduced in the optimization control process. Meanwhile, because the number of the wind generation sets in the sub wind power plant is small, the area is small, the boundary condition space change can be reduced, the influence of the boundary condition space change on the wind power plant optimization control process is reduced, and the applicability of the wind power plant grouping optimization control method is higher.
Step S101 to step S103 will be further described below.
In one implementation of the embodiment of the present invention, step S101 may include the following steps S1011 to S1014:
step S1011: and determining the influence degree of the wake flow of each upstream wind turbine generator on the wake flow of the downstream wind turbine generator according to the wake flow effect.
In the embodiment, the inflow wind condition of the wind power plant can be set, and the influence degree of the wake of the upstream wind turbine generator on the wake of the downstream wind turbine generator in the wind power plant is obtained through the simulation model of the wind power plant.
In one embodiment, the extent of the wake impact of each upstream wind turbine on the wake of the downstream wind turbine may be determined according to the following equation (10):
wherein, w ij The degree of wake effect of the upstream wind turbine i on the downstream wind turbine j, D T Is the diameter of the wind wheel of the wind turbine, x ij The distance between the flow directions of the upstream wind turbine generator i and the downstream wind turbine generator j,
is the average wind speed, A, at the j wind wheel of the downstream wind turbine overlap The area overlapping ratio of the wake flow of the upstream wind turbine generator i at the wind wheel of the downstream wind turbine generator j and the wind wheel of the downstream wind turbine generator is shown; the area overlapping ratio is calculated by the following equation (11):
Step S1012: and establishing a wake flow weight matrix of the wind power plant according to the wake flow influence degree.
In this embodiment, a wake weight matrix of the wind farm may be established according to the wake influence degree of the wake of each upstream wind turbine on the wake of the downstream wind turbine, which is obtained in step S1011. If there are N wind turbines in the wind farm, the wake weight matrix E of the wind farm may be expressed as:
step S1013: establishing a wake directed graph of the wind power plant according to the wake weight matrix; the nodes of the wake directed graph are wind turbines in the wind power plant, and the adjacent matrix of the wake directed graph is a wake weight matrix. The adjacency matrix refers to a relationship between vertices in the graph.
In this embodiment, the wind turbines in the wind farm may be used as nodes of the wake directed graph, the wake weight matrix E may be used as an adjacent matrix of the wake directed graph, and the wake directed graph G ═ V, E may be established, where V ═ E i I ═ 1,2, …, N } represents the node matrix of the wake directed graph, E ═ w { (w) } ij I, j ═ 1,2, …, N represents the adjacency matrix of the wake directed graph. The wake flow directed graph represents the influence degree of the wake flow between the wind turbines.
Step S1014: and converting the wake directed graph into a wake undirected graph so as to convert the influence degree of the wake of the upstream wind turbine generator on the wake of the downstream wind turbine generator into the wake relation degree between the upstream wind turbine generator and the downstream wind turbine generator.
In this embodiment, the wake directed graph of the influence degree of the upstream wind turbine generator on the wake of the downstream wind turbine generator can be converted into a wake undirected graph of the wake relation degree between the wind turbine generators. Namely, the wake flow is directed to the tail of the upstream wind turbine generator i to the downstream wind turbine generator j in the graphThe degree of influence of the flow is converted into a degree of wake relation, i.e. w ij ＝w ji . The wake flow directed graph is converted into the wake flow no-line graph, the wake flow relation among the wind turbine generators is not lost, and the influence direction does not need to be considered on the basis of keeping the wake flow relation among the wind turbine generators.
In one embodiment, the wake directed graph G ═ V, E may be converted to a wake undirected graph according to equation (12) below
Wherein the content of the first and second substances,
adjacency matrix being a wake undirected graph, E T Which is the transpose of the adjacency matrix E of the wake directed graph.
In one implementation of the embodiment of the present invention, step S102 may further include the following steps S1021 to S1023:
step S1021: according to a wake undirected graph and a set of top-ranked wind generation sets in a wind farm, a breadth-first search algorithm is applied to divide the wind farm into a plurality of wind farm subsets, wherein the top-ranked wind generation sets are wind generation sets which are not affected by wake flows in the inflow direction.
In the present embodiment, the step S1021 may further include the following steps S10211 to S10213:
step S10211: for each head-ranked wind turbine generator set, a Breadth-First Search Algorithm (BFS) is applied, the current head-ranked wind turbine generator set is used as an initial node, all nodes in a wake undirected graph are searched, and a node set formed by all nodes communicated with the current head-ranked wind turbine generator set is obtained. The breadth-first search algorithm is a blind search method, is a search algorithm for a graph, and requires that the relevance of a problem can be represented by the graph. It does not consider the possible location of the results and searches through the entire graph until the results are found.
In the embodiment, a breadth-first search algorithm is applied to obtain all nodes communicated with the current head-ranked wind turbine generator, and the nodes are used as a node set.
Step S10212: and comparing the node set corresponding to the current head-row wind turbine generator with the node sets corresponding to other head-row wind turbine generators, and if the node set corresponding to the current head-row wind turbine generator is the same as the node sets corresponding to the other head-row wind turbine generators, omitting the node set corresponding to the current head-row wind turbine generator.
In this embodiment, since there may also be a communication relationship between the wind turbine generators, a condition that a node set corresponding to the current wind turbine generator is the same as node sets corresponding to other wind turbine generators may result, and in this condition, the node set corresponding to the current wind turbine generator may be omitted.
Step S10213: and taking the remaining plurality of node sets as a plurality of wind farm subsets.
In this embodiment, the remaining plurality of node sets may be referred to as a plurality of wind farm subsets. The set of m wind farm subsets may be denoted S ═ S by S 1 ,…,S p ,…,S m In which S is p For the wind farm subset, p is 1,2, …, m.
Step S1022: and aiming at each wind power plant subset, establishing a wind power plant subgraph corresponding to the wind power plant subset according to the wind power plant subset and the adjacency matrix of the wake undirected graph.
In the present embodiment, the wind farm subset S is addressed p The wind farm subset S can be established according to the adjacency matrix of the wake undirected graph of the wind farm p Corresponding wind farm subgraph
Wherein
According to S p The wind turbine generator is numbered
The middle index is obtained.
Step S1023: and according to the wind farm subgraph, a spectral clustering algorithm is applied to divide the wind farm subset into a plurality of sub wind farms.
In the present embodiment, step S1023 may further include the following steps S10231 to S10238:
step S10231: and aiming at each wind power plant subgraph, determining the number of head-ranked wind generation sets in the wind power plant subset corresponding to the current wind power plant subgraph.
In this embodiment, the number of head-row wind turbines in each wind farm subset, i.e., the number of wind turbines that are not affected by the wake flow, may be determined.
Step S10232: and (3) applying a spectral clustering algorithm, and constructing a degree matrix of the spectral clustering algorithm according to the adjacency matrix of the current wind power plant subgraph. The spectral clustering algorithm is an algorithm evolved based on knowledge of graph theory, and the main idea is to regard all data as points in space, the points can be connected by edges, the weight value of the edge between the points with longer distance is lower, the weight value of the edge between the points with shorter distance is higher, then the graph formed by all data points is cut, the sum of the weights of the edges between different sub-graphs after the graph is cut is as low as possible, and the sum of the weights of the edges in the sub-graphs is as high as possible, so that the clustering purpose is achieved. The degree matrix is a diagonal matrix, and the elements on the diagonal of the degree matrix are the degrees of the respective vertices.
In the present embodiment, the degree matrix may be constructed according to the following steps S102321 and S102322:
step S102321: and acquiring the sum of each row of elements in the adjacency matrix according to the adjacency matrix of the wind power plant subgraph.
Step S102322: and constructing a degree matrix according to the sum of the elements of each row in the adjacency matrix.
In this embodiment, the following equation (13) may be used to sum elements of each row in the adjacent matrix of the wind farm subgraph, and the sum result is used as an element on the opposite corner of the degree matrix to construct the degree matrix.
The constructed degree matrix D is:
step S10233: and constructing a Laplace matrix of the spectral clustering algorithm according to the degree matrix.
In this embodiment, the laplacian matrix may be constructed according to the following equation (14):
L＝D-W (14)
wherein L is a Laplac matrix, and W is an adjacent matrix of the wind power plant subgraph.
Step S10234: the laplacian matrix is normalized to obtain a normalized laplacian matrix.
In this embodiment, the normalized laplacian matrix can be obtained according to the following equation (15):
L sym ＝D -1/2 LD -1/2 (15)
wherein L is sym To normalize the laplacian matrix.
Step S10235: and establishing a first matrix according to the standardized Laplace matrix and the number of the wind generation sets at the top of the row.
In this embodiment, k eigenvectors u with the minimum eigenvalue in the standard laplace matrix can be obtained according to the number k of head-ranked wind turbine generators in the wind turbine subset 1 ,u 2 ,…,u k Using the k eigenvectors as row vectors of the first matrix to establish a first matrix U ═ U 1 ,u 2 ,…,u k ]∈R n×k That is, the first matrix is an n × k order matrix.
Step S10236: and normalizing each row vector in the first matrix to obtain a second matrix.
In this embodiment, each row direction in the first matrix U may be according to the following formula (16)Quantity standardization, and obtaining a second matrix T epsilon R n×k ：
Wherein, t ij Is an element located in the ith row and jth column of the second matrix, a ij Being the element located in the ith row and jth column of the first matrix,
is the sum of the squares of the elements of the ith row of the first matrix.
Step S10237: and clustering the row vectors of the second matrix by using a K-means clustering algorithm according to the second matrix to obtain a clustering result.
In this embodiment, y may be used i Representing the row vector of the second matrix, applying the K-means clustering algorithm to the { y i } i＝1,2,…,n ∈R k Clustering to obtain a clustering result C 1 ,C 2 ,…,C k 。
Step S10238: and according to the clustering result, taking the wind turbine generator corresponding to the clustering result as a sub wind power plant to obtain a plurality of sub wind power plants.
In this embodiment, the clustering result C can be used 1 ,C 2 ,…,C k Obtaining the wind turbine set corresponding to the clustering result, and taking all the wind turbine sets corresponding to one clustering result as a wind power plant group to obtain a wind power plant group A 1 ,A 2 ,…,A k Wherein A is i ＝{j|y j ∈C i }. And traversing the set S to obtain a wind power plant grouping result obtained by clustering all wind power generation sets in the wind power plant. The plurality of sub wind farms can be obtained according to the grouping result of the wind farms and the unit coordinates of the wind farms.
In one implementation of the embodiment of the present invention, reference may be made to fig. 2 and fig. 3, fig. 2 is a schematic flow chart of main steps of dividing a wind farm into a plurality of sub-wind farms according to one implementation of the embodiment of the present invention, and fig. 3 is a schematic flow chart of main steps of dividing the wind farm into a plurality of sub-wind farms according to the embodiment of the present inventionThe flow diagram of the main steps of obtaining the optimal control strategy of each sub-wind farm is shown. As shown in FIG. 2, the wind farm is divided into a plurality of wind farm groups A 1 ,A 2 ,…,A k Then, the sub-wind farms can be iteratively optimized by using an optimization algorithm to obtain the optimal control strategy of the sub-wind farms. As shown in fig. 3, step S103 may further include the following steps S1031 to S1039:
step S1031: and setting wind power plant inflow wind conditions and wind power plant groups Ai.
In the present embodiment, when obtaining the optimal control strategy of the wind farm group Ai, the inflow wind conditions of the wind farm may be set first.
Step S1032: and constructing a sub wind power plant WFi.
In the present embodiment, the sub-wind farms WFi may be constructed from the coordinates of the wind farm groups Ai and the wind farm wind turbine groups.
Step S1033: and setting a yaw angle of the wind turbine generator.
In the present embodiment, the yaw angle of the wind turbine generator set in the sub-wind farm WFi may be set.
Step S1034: and (5) simulating a wind power plant model.
In this embodiment, a model simulation may be performed using a simulation model of a wind farm according to a yaw angle of the wind turbine.
Step S1035: and obtaining the output power Pi of the sub wind power plant.
In the present embodiment, the output power Pi in the sub-wind farm WFi may be obtained from the result of the model simulation.
Step S1036: judging whether the maximum iteration times or the maximum output power is reached; if yes, go to step S1037; if not, go to step S1038.
In this embodiment, after the output power of the sub-wind farm in the current iteration is obtained, whether the optimization algorithm reaches the preset maximum iteration number or whether the output power of the sub-wind farm reaches the maximum can be determined. If yes, go to step S1037; if not, it goes to step S1038.
Step S1037: and outputting the optimal control strategy Qi of the wind generating sets in the sub wind power plant.
In this embodiment, when the optimization algorithm reaches a preset maximum iteration number, or the output power of the sub-wind farm reaches the maximum, the optimal control strategy Qi of the wind turbine generator in the sub-wind farm may be output.
Step S1038: and updating the yaw angle of the wind turbine generator and jumping to the step S1033.
In this embodiment, when the optimization algorithm does not reach the preset maximum iteration number and the output power of the sub-wind farm does not reach the maximum, the yaw angle of the wind turbine generator may be updated, and the step S1033 is skipped to continue the model simulation of the wind farm by using the optimization algorithm.
In one embodiment, referring to fig. 4, fig. 4 is a schematic flow chart of main steps of dividing a wind farm into a plurality of sub-wind farms according to another embodiment of the invention. As shown in fig. 4, in the present embodiment, dividing the wind farm into a plurality of sub-wind farms may include the following steps S201 to S212:
step S201: and establishing a wake directed graph G ═ V, E.
In this embodiment, step S201 is similar to the method described in step S1013, and for simplicity of description, the description is omitted here.
Step S202: converting a directed graph G into an undirected graph
In this embodiment, step S202 is similar to the method described in step S1014, and is not described herein again for simplicity of description.
Step S203: the set S is established by an extent search algorithm.
In this embodiment, step S203 is similar to the method described in step S1021, and is not described herein again for simplicity.
Step S204: p is 1.
In this embodiment, the first subset of wind farms is first grouped.
Step S205: according to set S p Generating subgraphs
In this embodiment, step S205 is similar to the method described in step S1022, and is not described herein again for simplicity of description.
Step S206: judgment of k p ＞1。
In the present embodiment, the wind farm subset S is determined p Whether the number of the middle row head wind turbine generators is more than 1 or not; if yes, jumping to step S207; if not, go to step S211.
Step S207: diagraph by spectral clustering algorithm
And (6) clustering.
In this embodiment, step S207 is similar to the method described in step S1023, and therefore, for simplicity of description, the description thereof is omitted.
Step S208: generation of A p 。
In this embodiment, step S208 is similar to the method described in step S10238, and therefore, for simplicity of description, the description thereof is omitted.
Step S209: m?
In the embodiment, whether the current wind farm subset is the last wind farm subset is judged; if yes, jumping to step S210; if not, go to step S212.
Step S210: and a wind turbine set A.
In this embodiment, the wind turbine group set a divided by the wind farm may be output as a plurality of sub-wind farms and then ended.
Step S211: a. the p ＝S p 。
In this embodiment, when there is only one wind turbine generator in the wind farm subset, the wind farm subset S can be directly divided into two p As A p And then jumps to step S208.
Step S212: p is p + 1.
In the present embodiment, p +1 is assigned to p, and the process skips to step S205 to continue grouping the next wind farm subset.
An example according to an embodiment of the present invention will be described with reference to fig. 5 to 8. FIG. 5 is a schematic diagram of wind velocity distribution at hub height of a wind turbine set in a wind farm according to an example of an embodiment of the present invention; FIG. 6 is a schematic representation of the wake effect level of a wind park in a wind farm according to an example of embodiment of the present invention; FIG. 7 is a schematic illustration of sub-wind farm partitioning according to an example of an embodiment of the present invention; FIG. 8 is a schematic diagram comparing the optimization control results of the wind farm grouping optimization control method according to an example of the embodiment of the invention with the traditional optimization control method and the centralized optimization control method. In fig. 8, Traditional corresponds to a conventional optimization control method, Centralized corresponds to a Centralized optimization control method, and Decentralized corresponds to a packet optimization control method according to an embodiment of the present invention. Distance in x-direction of the wind farm on abscissa of fig. 5, one ordinate is distance in y-direction of the wind farm, and the other ordinate is wind speed; the distances in the x-direction of the wind farm on the abscissa and the distances in the y-direction of the wind farm on the ordinate of fig. 6 and 7; the abscissa of fig. 8 is the wind turbine generator, and the ordinate is the output power. In the example, a wind farm is constructed based on 30 wind turbines, the inflow wind speed of the wind farm is 8m/s, the inflow wind direction is 30 degrees, and the turbulence intensity is 5 percent. The conventional optimization control method, the centralized optimization control method, and the packet optimization control method of the embodiment of the present invention are compared. Table 1 shows the comparison results of the output power and the calculation solution time of the three optimization control methods. From fig. 8 and table 1, it can be seen that the wind farm grouping optimization control method of the embodiment of the invention not only can effectively improve the output power of the wind farm, but also has lower calculation cost.
TABLE 1 comparison of output power and calculation solution time for three optimization control methods
| Full field power [ MW] | Power boost | Solution time [ s ]] | |
| Traditional optimization control method | 45.35 | 0％ | 0.1 |
| Centralized optimization control method | 46.18 | 1.83％ | 145.4 |
| Packet optimization control method | 46.14 | 1.74％ | 4.0 |
It should be noted that, although the foregoing embodiments describe each step in a specific sequence, those skilled in the art can understand that, in order to achieve the effect of the present invention, different steps do not have to be executed in such a sequence, and they may be executed simultaneously (in parallel) or in other sequences, and these changes are all within the scope of the present invention.
Further, the invention also provides a wind power plant grouping optimization control system.
Referring to fig. 9, fig. 9 is a main structural block diagram of a wind farm grouping optimization control system according to an embodiment of the invention. As shown in fig. 9, the wind farm grouping optimization control system in the embodiment of the present invention may include a wake undirected graph establishing module, a sub-wind farm dividing module, a sub-wind farm control strategy obtaining module, and a wind farm grouping optimization control module. In this embodiment, the wake undirected graph establishing module may be configured to establish a wake undirected graph of a degree of wake relationships between wind turbines in the wind farm according to a wake effect. The sub-wind farm dividing module can be configured to divide the wind farm into a plurality of sub-wind farms according to the wake undirected graph, wherein each sub-wind farm comprises a plurality of wind turbines. The sub-wind farm control strategy obtaining module may be configured to obtain, for each sub-wind farm, an optimal control strategy for each wind turbine in the sub-wind farm with the output power of the sub-wind farm being the maximum control target. The wind farm group optimization control module can be configured to respectively carry out optimization control on each sub wind farm according to the optimal control strategy of each sub wind farm.
The wind farm grouping optimization control system is used for executing the embodiment of the wind farm grouping optimization control method shown in fig. 1, and the technical principles, the solved technical problems and the generated technical effects of the two are similar, and it can be clearly understood by those skilled in the art that for convenience and simplicity of description, the specific working process and related description of the wind farm grouping optimization control system may refer to the content described in the embodiment of the wind farm grouping optimization control method, and no further description is given here.
It will be understood by those skilled in the art that all or part of the flow of the method according to the above-described embodiment may be implemented by a computer program, which may be stored in a computer-readable storage medium and used to implement the steps of the above-described embodiments of the method when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable storage medium may include: any entity or device capable of carrying said computer program code, media, usb disk, removable hard disk, magnetic diskette, optical disk, computer memory, read-only memory, random access memory, electrical carrier wave signals, telecommunication signals, software distribution media, etc. It should be noted that the computer readable storage medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable storage media that does not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
Furthermore, the invention also provides a control device. In an embodiment of the control device according to the invention, the control device comprises a processor and a memory device, the memory device may be configured to store a program for executing the wind farm group optimization control method of the above-mentioned method embodiment, and the processor may be configured to execute a program in the memory device, the program including but not limited to a program for executing the wind farm group optimization control method of the above-mentioned method embodiment. For convenience of explanation, only the parts related to the embodiments of the present invention are shown, and details of the specific techniques are not disclosed. The control device may be a control device apparatus formed including various electronic apparatuses.
Further, the invention also provides a computer readable storage medium. In one computer-readable storage medium embodiment according to the present invention, a computer-readable storage medium may be configured to store a program for executing the wind farm group optimization control method of the above-described method embodiment, which may be loaded and executed by a processor to implement the above-described wind farm group optimization control method. For convenience of explanation, only the parts related to the embodiments of the present invention are shown, and details of the specific techniques are not disclosed. The computer readable storage medium may be a storage device formed by including various electronic devices, and optionally, the computer readable storage medium is a non-transitory computer readable storage medium in the embodiment of the present invention.
Further, it should be understood that, since the configuration of each module is only for explaining the functional units of the apparatus of the present invention, the corresponding physical devices of the modules may be the processor itself, or a part of software, a part of hardware, or a part of a combination of software and hardware in the processor. Thus, the number of individual modules in the figures is merely illustrative.
Those skilled in the art will appreciate that the various modules in the apparatus may be adaptively split or combined. Such splitting or combining of specific modules does not cause the technical solutions to deviate from the principle of the present invention, and therefore, the technical solutions after splitting or combining will fall within the protection scope of the present invention.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is apparent to those skilled in the art that the scope of the present invention is not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
Claims (10)
1. A wind power plant grouping optimization control method is characterized by comprising the following steps:
establishing a wake undirected graph of the wake relation degree between wind generating sets in the wind power plant according to the wake effect;
according to the wake undirected graph, dividing the wind power plant into a plurality of sub wind power plants, wherein each sub wind power plant comprises a plurality of wind power generation sets;
aiming at each sub wind power plant, acquiring an optimal control strategy of each wind turbine generator in the sub wind power plant by taking the maximum output power of the sub wind power plant as a control target;
and respectively carrying out optimization control on each sub wind power plant according to the optimal control strategy of each sub wind power plant.
2. The wind farm grouping optimization control method according to claim 1, wherein the step of establishing a wake undirected graph of wake influence degrees among wind turbine generators in the wind farm according to wake effects comprises:
determining the influence degree of the wake flow of each upstream wind turbine generator on the wake flow of the downstream wind turbine generator according to the wake flow effect;
establishing a wake flow weight matrix of the wind power plant according to the wake flow influence degree;
establishing a wake directed graph of the wind power plant according to the wake weight matrix; the nodes of the wake directed graph are wind turbines in the wind power plant, and the adjacent matrix of the wake directed graph is the wake weight matrix;
and converting the wake directed graph into the wake undirected graph so as to convert the influence degree of the wake of the upstream wind turbine generator on the wake of the downstream wind turbine generator into the wake relation degree between the upstream wind turbine generator and the downstream wind turbine generator.
3. The wind farm grouping optimization control method according to claim 2, wherein the step of determining the wake impact degree of the wake of each upstream wind turbine on the downstream wind turbine according to the wake effect comprises determining the wake impact degree of the wake of each upstream wind turbine on the downstream wind turbine according to the following formula:
wherein, w ij The degree of wake effect of the upstream wind turbine i on the downstream wind turbine j, D T Is the diameter of the wind wheel of the wind turbine, x ij The distance between the flow directions of the upstream wind turbine generator i and the downstream wind turbine generator j,
is the average wind speed at j wind wheel of downstream wind turbine generator set, A overlap The wake flow of the upstream wind turbine generator i is overlapped with the area of the wind wheel of the downstream wind turbine generator at the wind wheel of the downstream wind turbine generator jA ratio; calculating the area overlap ratio by the following formula:
4. The wind farm grouping optimization control method according to claim 2, wherein the step of dividing the wind farm into a plurality of sub-wind farms each including a plurality of wind turbines according to the wake undirected graph comprises:
according to the wake undirected graph and a set of top-ranked wind generation sets in the wind power plant, dividing the wind power plant into a plurality of wind power plant subsets by applying a breadth-first search algorithm;
aiming at each wind power plant subset, establishing a wind power plant subgraph corresponding to the wind power plant subset according to the wind power plant subset and an adjacent matrix of the wake undirected graph;
according to the wind farm subgraph, a spectral clustering algorithm is applied to divide the wind farm subset into a plurality of sub wind farms;
the head-end wind turbine generator is a wind turbine generator which is not affected by wake flow in the inflow wind direction.
5. The wind farm grouping optimization control method according to claim 4, wherein the step of dividing the wind farm into a plurality of wind farm subsets by applying a breadth-first search algorithm according to the wake undirected graph and the set of top-ranked wind turbines in the wind farm comprises:
aiming at each head wind turbine generator, applying a breadth-first search algorithm, taking the current head wind turbine generator as an initial node, and retrieving all nodes in the wake undirected graph to obtain a node set formed by all nodes communicated with the current head wind turbine generator;
comparing the node set corresponding to the current head-arranged wind turbine generator with the node sets corresponding to other head-arranged wind turbine generators, and if the node set corresponding to the current head-arranged wind turbine generator is the same as the node sets corresponding to the other head-arranged wind turbine generators, omitting the node set corresponding to the current head-arranged wind turbine generator;
and taking the remaining plurality of node sets as a plurality of wind farm subsets.
6. The wind farm grouping optimization control method according to claim 4, wherein the step of dividing the wind farm subset into a plurality of sub-wind farms by applying a spectral clustering algorithm according to the wind farm subgraph comprises:
aiming at each wind power plant subgraph, determining the number of head-ranked wind generation sets in a wind power plant subset corresponding to the current wind power plant subgraph;
applying a spectral clustering algorithm, and constructing a degree matrix of the spectral clustering algorithm according to an adjacent matrix of the current sub-graph of the wind power plant;
constructing a Laplace matrix of a spectral clustering algorithm according to the degree matrix;
normalizing the Laplace matrix to obtain a normalized Laplace matrix;
establishing a first matrix according to the standardized Laplace matrix and the number of the wind generation sets at the top of the row;
normalizing each row vector in the first matrix to obtain a second matrix;
clustering the row vectors of the second matrix by applying a K-means clustering algorithm according to the second matrix to obtain a clustering result;
and according to the clustering result, taking the wind turbine generator corresponding to the clustering result as a sub wind power plant to obtain a plurality of sub wind power plants.
7. The wind farm grouping optimization control method according to claim 6, wherein the step of constructing the degree matrix of the spectral clustering algorithm according to the adjacency matrix of the current wind farm subgraph comprises the steps of:
acquiring the sum of each row of elements in the adjacency matrix according to the adjacency matrix of the wind power plant subgraph;
constructing the degree matrix according to the sum of each row of elements in the adjacent matrix; and/or the presence of a gas in the gas,
the step of constructing a laplacian matrix of a spectral clustering algorithm based on the degree matrix comprises constructing the laplacian matrix according to the following formula:
L＝D-W
wherein L is the Laplac matrix, D is the degree matrix, and W is an adjacent matrix of the wind farm subgraph; and/or the presence of a gas in the gas,
the step of "normalizing said laplace matrix to obtain a normalized laplace matrix" comprises obtaining a normalized laplace matrix according to the following formula:
L sym ＝D -1/2 LD -1/2
wherein L is sym Is the normalized Laplace matrix; and/or the presence of a gas in the gas,
the step of establishing a first matrix according to the standardized Laplace matrix and the number of the wind generation sets at the top of the row comprises the following steps:
acquiring k eigenvectors with the minimum eigenvalues in the standardized Laplace matrix according to the number k of the wind generation sets at the top of the row;
establishing the first matrix according to the k eigenvectors; and/or the presence of a gas in the gas,
the step of "normalizing each row vector in the first matrix to obtain a second matrix" includes normalizing each row vector in the first matrix according to the following formula to obtain the second matrix:
wherein, t ij Is the element located in the ith row and jth column of the second matrix,a ij being the element located in the ith row and jth column of the first matrix,
is the sum of the squares of the elements of the ith row of the first matrix.
8. A wind farm group optimization control system, the system comprising:
the wake undirected graph establishing module is configured to establish a wake undirected graph of the degree of wake relation among wind turbine sets in the wind power plant according to a wake effect;
the sub wind power plant dividing module is configured to divide the wind power plant into a plurality of sub wind power plants according to the wake undirected graph, and each sub wind power plant comprises a plurality of wind power generation sets;
the sub wind power plant control strategy acquisition module is configured to acquire an optimal control strategy of each wind turbine generator in each sub wind power plant with the maximum output power of the sub wind power plant as a control target for each sub wind power plant;
and the wind power plant grouping optimization control module is configured to perform optimization control on each sub wind power plant according to the optimal control strategy of each sub wind power plant.
9. A control device comprising a processor and a memory device adapted to store a plurality of program codes, characterized in that said program codes are adapted to be loaded and run by said processor to perform a wind farm group optimization control method according to any of claims 1 to 7.
10. A computer readable storage medium having a plurality of program codes stored therein, characterized in that said program codes are adapted to be loaded and run by a processor to perform a wind farm group optimization control method according to any of claims 1 to 7.
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| CN116771596A (en) * | 2023-06-30 | 2023-09-19 | 渤海石油航务建筑工程有限责任公司 | Offshore wind farm wake flow steering control method and related equipment |
| CN116993026A (en) * | 2023-09-26 | 2023-11-03 | 无锡九方科技有限公司 | Large-scale wind power plant unit operation parameter optimization method |
| CN117420773A (en) * | 2023-08-31 | 2024-01-19 | 南京国电南自维美德自动化有限公司 | Wake flow cooperative control method and system for wind farm |
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| CN116771596A (en) * | 2023-06-30 | 2023-09-19 | 渤海石油航务建筑工程有限责任公司 | Offshore wind farm wake flow steering control method and related equipment |
| CN116771596B (en) * | 2023-06-30 | 2024-06-04 | 渤海石油航务建筑工程有限责任公司 | Offshore wind farm wake flow steering control method and related equipment |
| CN117420773A (en) * | 2023-08-31 | 2024-01-19 | 南京国电南自维美德自动化有限公司 | Wake flow cooperative control method and system for wind farm |
| CN116993026A (en) * | 2023-09-26 | 2023-11-03 | 无锡九方科技有限公司 | Large-scale wind power plant unit operation parameter optimization method |
| CN116993026B (en) * | 2023-09-26 | 2023-12-19 | 无锡九方科技有限公司 | Large-scale wind power plant unit operation parameter optimization method |
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