CN114021346B - Distributed operation optimization method for wind farm based on wake flow directed graph - Google Patents
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Abstract
The embodiment of the disclosure provides a distributed operation optimization method for a wind farm based on a wake flow directed graph, which belongs to the crossing field of new energy and automation technology, and specifically comprises the following steps: obtaining initial wake flow distribution of a target wind power plant; calculating the effective wind speed of each fan; constructing a directed network diagram corresponding to a target wind power plant; clustering the directed network graph through a preset clustering method to obtain a plurality of subsets; performing two-stage optimization adjustment operation on each subset according to the state type of the target wind power plant to obtain a new subset and a corresponding optimal control variable thereof; substituting all the optimal control variables and the effective wind speed into the distributed optimization function to obtain the output power. According to the scheme, based on the directed graph and the spectral clustering, a distributed operation optimization mechanism based on intelligent clustering is constructed according to wind power plant optimization action characteristics, an optimal control variable is obtained, power maximization of the wind power plant is achieved through control output, and efficiency and performance of wind power plant optimization are improved.
Description
Technical Field
The embodiment of the disclosure relates to the field of new energy and automation technology intersection, in particular to a distributed operation optimization method for a wind power plant based on a wake flow directed graph.
Background
At present, offshore wind power is rapidly developed by virtue of the advantages of high wind speed, small turbulence, small wind cutting and the like, and keeps a stable rising trend, so that the offshore wind power becomes an important direction of new energy planning. The wind speed and the wind direction of the offshore wind farm are relatively stable, the wake effect is prolonged, and the coupling relationship between fans is highly complicated. The traditional control method is greedy control of a single fan, so that the power generated by a downstream fan is smaller, and the power generation efficiency of a wind power plant is seriously affected.
In order to improve the power generation efficiency of a wind power plant, an effective approach is to adopt wake control on the wind power plant and reduce wake effects among fans, and the traditional wind power plant operation control adopts a centralized control architecture and relies on a centralized controller to communicate and optimize all fans. The centralized control method needs higher communication capability, is easy to sink into local optimum, has lower optimizing effect, is suitable for small-sized wind power plants, gradually increases the size of the offshore wind power plants in recent years, and changes the wind power plant fans into large-size variable speed type wind power plants. In large offshore wind farms, as the number of fans increases, the control variables of the wind farm also increase in a near-exponential fashion. Based on this, distributed control architecture and optimization algorithms have received extensive attention from researchers. At present, a distributed control architecture of a wind farm is mainly divided into: the neighborhood communication based on the free model and the network division based on the graph theory have higher solving efficiency, but fewer consideration factors, are more suitable for small wind power plants with simple coupling relation, the network dividing method based on graph theory can have more direct correlation between fans in the whole field, but the related method is still in an exploration stage, and the influence of a control process on network change is not considered, so that the optimization efficiency and the optimization performance are insufficient.
It can be seen that there is a need for an efficient and high performance wake flow directed graph based wind farm distributed operation optimization method.
Disclosure of Invention
In view of the above, the embodiments of the present disclosure provide a distributed operation optimization method for a wind farm based on wake flow directed graphs, which at least partially solves the problems of poor optimization efficiency and poor optimization performance in the prior art.
The embodiment of the disclosure provides a wind farm distributed operation optimization method based on wake flow directed graphs, which comprises the following steps:
inputting position information and wind condition information of a fan in a target wind power plant into a wake model to obtain initial wake distribution of the target wind power plant;
calculating the effective wind speed of each fan according to the relevance between the initial wake flow distribution and each fan;
Constructing a directed network diagram corresponding to the target wind power plant according to the coupling relation between the wake effect and the distance information of each fan;
clustering the directed network graph by a preset clustering method to obtain a plurality of subsets;
Performing two-stage optimization adjustment operation on each subset according to the state type of the target wind power plant to obtain a new subset and an optimal control variable corresponding to the new subset;
Substituting all the optimal control variables and the effective wind speed into a distributed optimization function to obtain output power.
According to a specific implementation manner of the embodiment of the present disclosure, the step of inputting the position information and the wind condition information of the fan in the target wind farm into the wake model to obtain the initial wake distribution of the target wind farm includes:
Establishing a Cartesian coordinate system transformed according to a flow field, bringing the position information and the wind condition information into the Cartesian coordinate system, and calculating a wake zone type of each fan, wherein the wake zone type comprises a near wake zone, a far wake zone and a mixing zone;
And forming the initial wake distribution according to the wake zone type of each fan.
According to a specific implementation of the embodiment of the disclosure, an x-axis of the cartesian coordinate system points to an incoming wind direction of the target wind farm, and a y-axis points to a direction orthogonal to the x-axis.
According to a specific implementation manner of the embodiment of the present disclosure, the step of calculating the effective wind speed of each fan according to the correlation between the initial wake distribution and each fan includes:
calculating a speed deficiency coefficient corresponding to the wake area type of each fan, and calculating the overlapping area of different wake areas of the fans at the upstream of the target wind power plant and the impeller surface of the fan at the downstream of the target wind power plant;
And calculating the effective wind speed of each fan according to the speed deficiency coefficient and the overlapping area.
According to a specific implementation manner of the embodiment of the present disclosure, the step of constructing a directed network diagram corresponding to the target wind farm according to a coupling relationship between a wake effect and distance information of each fan includes:
Defining each fan in the target wind power plant as a node, and forming a plurality of edges between different nodes;
calculating a weight coefficient of each edge according to the overlapping area and the distance information between adjacent nodes to form an adjacent weight matrix;
And taking the adjacency weight matrix as the directed network graph.
According to a specific implementation manner of the embodiment of the present disclosure, the step of clustering the directed network map by a preset clustering method to obtain a plurality of subsets includes:
Constructing a similarity matrix of spectrum analysis according to the directed network graph, and constructing a main diagonal matrix according to the access degree of all the nodes;
Normalizing a Laplace matrix according to the similarity matrix and the main diagonal matrix;
Solving the Laplace matrix to obtain a feature matrix;
And clustering the feature matrix by using a K-means++ algorithm to obtain a plurality of subsets.
According to a specific implementation of the embodiment of the disclosure, the state type includes an initial action phase and a gentle running phase.
According to a specific implementation manner of the embodiment of the present disclosure, the step of performing a two-stage optimization adjustment operation on each subset according to the state type of the target wind farm to obtain a new subset and a corresponding optimal control variable thereof includes:
in the initial action stage, performing rapid iterative optimization operation on each subset by using an improved balance optimization algorithm to obtain an updated adjacency matrix;
judging whether the variation value of the updated adjacent matrix is larger than or equal to a critical value;
If yes, continuing the rapid iterative optimization operation, and then carrying out clustering treatment to update a subset until the variation value is smaller than the critical value or the maximum iterative times are reached;
if not, judging the target wind power plant to be in a gentle operation stage, performing re-clustering treatment on the updated adjacent matrix, and performing optimization operation on the obtained new subset to obtain the corresponding optimal control variable.
According to a specific implementation manner of the embodiment of the present disclosure, the step of performing a re-clustering process on the updated adjacency matrix and performing a gentle optimization operation on the obtained new subset to obtain the corresponding optimal control variable includes:
Decoupling into a plurality of new subsets according to the updated adjacency matrix;
And performing control optimization on each new subset by using the improved balance optimization algorithm to obtain an optimal control variable corresponding to each new subset.
The wind farm distributed operation optimization scheme based on wake flow directed graph in the embodiment of the disclosure comprises the following steps: inputting position information and wind condition information of a fan in a target wind power plant into a wake model to obtain initial wake distribution of the target wind power plant; calculating the effective wind speed of each fan according to the relevance between the initial wake flow distribution and each fan; constructing a directed network diagram corresponding to the target wind power plant according to the coupling relation between the wake effect and the distance information of each fan; clustering the directed network graph by a preset clustering method to obtain a plurality of subsets; performing two-stage optimization adjustment operation on each subset according to the state type of the target wind power plant to obtain a new subset and an optimal control variable corresponding to the new subset; substituting all the optimal control variables and the effective wind speed into a distributed optimization function to obtain output power.
The beneficial effects of the embodiment of the disclosure are that: according to the scheme, based on the directed graph and the spectral clustering, a distributed operation optimization mechanism based on intelligent clustering is constructed according to wind power plant optimization action characteristics, so that optimal control variables of each subset are obtained, power maximization of the wind power plant is achieved through control output, and efficiency and performance of wind power plant optimization are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.
FIG. 1 is a schematic flow diagram of a distributed operation optimization method for a wind farm based on wake directed graphs provided in an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of an overall optimization flow involved in a distributed operation optimization method of a wind farm based on wake flow directed graphs according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of wake regions and impeller surfaces involved in a wake directed graph-based wind farm distributed operation optimization method according to an embodiment of the present disclosure;
fig. 4 is a schematic flow chart of a two-stage optimization adjustment operation related to a distributed operation optimization method of a wind farm based on wake flow directed graphs according to an embodiment of the present disclosure;
FIG. 5 is a schematic diagram of wind farm fan distribution provided by an embodiment of the present disclosure;
FIG. 6 is a schematic diagram of wind farm power for each wind direction provided by an embodiment of the present disclosure;
Fig. 7 to 10 are schematic diagrams of cluster grouping results under flow field coordinates according to an embodiment of the present disclosure;
Fig. 11 to 14 are schematic diagrams of output power of each fan in a wind farm under different wind directions according to an embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
Other advantages and effects of the present disclosure will become readily apparent to those skilled in the art from the following disclosure, which describes embodiments of the present disclosure by way of specific examples. It will be apparent that the described embodiments are merely some, but not all embodiments of the present disclosure. The disclosure may be embodied or practiced in other different specific embodiments, and details within the subject specification may be modified or changed from various points of view and applications without departing from the spirit of the disclosure. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure are intended to be within the scope of this disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the following claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the present disclosure, one skilled in the art will appreciate that one aspect described herein may be implemented independently of any other aspect, and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. In addition, such apparatus may be implemented and/or such methods practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should also be noted that the illustrations provided in the following embodiments merely illustrate the basic concepts of the disclosure by way of illustration, and only the components related to the disclosure are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided in order to provide a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
The embodiment of the disclosure provides a wind farm distributed operation optimization method based on a wake flow directed graph, which can be applied to a wind farm optimization process of an offshore wind power generation scene.
Referring to fig. 1, a flow diagram of a distributed operation optimization method for a wind farm based on wake directed graphs is provided in an embodiment of the present disclosure. As shown in fig. 1, the method mainly comprises the following steps:
S101, inputting position information and wind condition information of a fan in a target wind power plant into a wake model to obtain initial wake distribution of the target wind power plant;
In the specific implementation, the accurate description of the wake distribution of the fans is considered to be the basis for realizing the power calculation of the fans, and is also an important means for further analyzing the coupling relation between the fans. For example, for research of wind farm wake distribution, multizone wake models that accurately characterize wake effects and are suitable for control solution may be employed. And then, the position information of the fan in the target wind power plant and the wind condition information can be input into the Multizone wake model to obtain the initial wake distribution of the target wind power plant.
S102, calculating the effective wind speed of each fan according to the relevance between the initial wake flow distribution and each fan;
In the implementation, considering that a plurality of fans exist in one wind power plant, and the physical distance between the fans and the wind conditions are different, the effective wind speeds of different fans which can be used for generating power are different, and the effective wind speed of each fan can be calculated according to the relevance between the initial wake distribution and each fan.
S103, constructing a directed network diagram corresponding to the target wind power plant according to the coupling relation between the wake effect and the distance information of each fan;
It is contemplated that within a wind farm, a fan may draw energy from the wind while forming a wake zone downstream of the wind where the wind speed is reduced. If the downstream fan is positioned in the wake area, the input wind speed of the downstream fan is lower than that of the upstream fan. The wake effect causes uneven wind speed distribution in the wind power plant, influences the running condition of each fan set in the wind power plant, and further influences the running condition and output of the wind power plant; and the wind power plant is influenced by factors such as wind power plant topology, wind wheel diameter, thrust coefficient, wind speed, wind direction and the like, a directed network diagram corresponding to the target wind power plant can be constructed according to the coupling relation between the wake effect and the distance information of each fan.
S104, clustering the directed network graph through a preset clustering method to obtain a plurality of subsets;
In the specific implementation, considering that the prior art mostly controls the fan optimization of the whole wind power plant or the local wind power plant, and does not well consider the optimization control problem of each fan, the directed network structure of the wind power plant can be described based on the graph theory basis, so that the parameter definition of the wind power plant network is improved, then the wind power plant is decoupled into a plurality of fan clusters through a combined spectral clustering algorithm, the clustering treatment of the wind power plant network is realized, and in particular, the directed network graph can be clustered through a preset clustering method to obtain a plurality of subsets, and the distributed control is performed.
S105, performing two-stage optimization adjustment operation on each subset according to the state type of the target wind power plant to obtain a new subset and the corresponding optimal control variable;
After clustering the directed network graph to obtain a plurality of subsets corresponding to the target wind power plant, taking possible changes in the running state of the target wind power plant into consideration, performing the two-section optimization adjustment operation on the subsets according to the state type of the target wind power plant to obtain new subsets and optimal control variables corresponding to each new subset.
S106, substituting all the optimal control variables and the effective wind speed into a distributed optimization function to obtain output power.
In the implementation, after obtaining the optimal control variable corresponding to each new subset, the effective wind speed and the known parameters corresponding to each fan are substituted into the distributed optimization function to obtain the output power, wherein the output power is the maximum power after distributed operation optimization, and the overall optimization flow is shown in fig. 2.
For example, let s= {1,2,..n }, represent a cluster of fans of one wind farm, N represents the total number of fans in the wind farm. The stable output power, denoted as fan i e S using P i, is calculated as follows:
Where ρ represents air density, A i is the impeller face area, V i is the effective wind speed, and C P is the power coefficient of the fan, which is a nonlinear function of yaw angle γ i and axial induction factor α i. Under the working condition without yaw, C P(ai)＝4ai[1-ai]2 is defined, and in the operation optimization studied herein, the influence of the yaw angle gamma i on the rotor power coefficient is considered, and then the correction is as follows:
CP＝4αi[1-αi]2ηcos(γi)Pp
wherein eta is a correction coefficient comprehensively considering the efficiency and the maximum power factor of the generator, and Pp is a yaw adjustment parameter obtained by wind tunnel test. For detailed parameter settings, reference is made to the literature.
The output power P total of the wind farm full farm can be calculated by:
for centralized optimization, the entire wind farm is considered as a whole. The centralized optimization objective function of the wind farm corresponds to the output power function of the wind farm, and is represented by the following formula:
Wherein f (x) represents the sum of the powers of all fans in the wind farm, K is the number of fan clusters after the wind farm network is clustered, and N j is the number of fans of the jth fan cluster. A control variable matrix x= [ γ, α ] T is defined. The vector alpha= [ alpha 1,α2,...,αn ] is the axial induction coefficient of each fan; vector γ= [ γ 1,γ2,...,γn ] is the yaw angle of each fan relative to the incoming wind direction. Gamma min and gamma max are the minimum and maximum constraints of the yaw angle and alpha min and alpha max are the minimum and maximum constraints of the axial induction factor.
For distributed optimization, the wind power plant is decoupled into K fan clusters, the large and centralized full-field optimization problem is converted into a plurality of small and distributed cluster optimization problems, and the corresponding output power functions are as follows:
The objective function of the j-th subset in the distributed optimization of the wind farm, corresponding to the distributed output power function, is represented by the following formula:
According to the distributed operation optimization method for the wind power plant based on the wake flow directed graph, provided by the embodiment, the distributed operation optimization mechanism based on intelligent clustering is constructed according to the characteristics of wind power plant optimization actions based on the directed graph and the spectral clustering, so that the optimal control variable of each subset is obtained, the power of the wind power plant is maximized through control output, and the efficiency and the performance of wind power plant optimization are improved.
On the basis of the above embodiment, in step S101, the step of inputting the position information and the wind condition information of the fan in the target wind farm into the wake model to obtain the initial wake distribution of the target wind farm includes:
Establishing a Cartesian coordinate system transformed according to a flow field, bringing the position information and the wind condition information into the Cartesian coordinate system, and calculating a wake zone type of each fan, wherein the wake zone type comprises a near wake zone, a far wake zone and a mixing zone;
And forming the initial wake distribution according to the wake zone type of each fan.
Optionally, an x-axis of the cartesian coordinate system points to an incoming wind direction of the target wind farm, and a y-axis points to a direction orthogonal to the x-axis.
For example, to describe wake distribution after a fan, a Cartesian coordinate system (x, y) transformed according to a flow field is used. The X-axis points to the wind entering direction of the target wind power plant, and the y-axis points to the direction orthogonal to the X-axis.
In the method, in the process of the invention,And (3) taking the initial position of a fan of the target wind power plant as a fan flow field position of the target wind power plant, (X i,Yi) and phi as the wind entering direction of the target wind power plant.
Then, within the transformed coordinate system, the Multizone model defines three wake regions, namely a "near wake region", "far wake region", and a "blend region", each having a unique velocity deficiency coefficient. By adjusting the parameters to fit the actual wake effect. The effective wind speed at fan j is calculated from the overlap area of the wake area of the upstream fan i and the impeller face of the downstream fan j, as shown in fig. 3.
Further, in step S102, calculating an effective wind speed of each fan according to the correlation between the initial wake distribution and each fan includes:
calculating a speed deficiency coefficient corresponding to the wake area type of each fan, and calculating the overlapping area of different wake areas of the fans at the upstream of the target wind power plant and the impeller surface of the fan at the downstream of the target wind power plant;
And calculating the effective wind speed of each fan according to the speed deficiency coefficient and the overlapping area.
For example, the effective wind speed of the upstream fan i is equal to the wind farm wind inlet wind speed V ∞, and the effective speed of the downstream fan j is described by the effect of the wake area of each upstream fan i in the integrated fan cluster S, expressed as:
Where a i is the axial inductance of the upstream fan i, c i,q(xj) the velocity deficiency coefficient for each wake zone, Is the overlap area of the q-th wake zone of the upstream fan i and the impeller face of the downstream fan j, a j is the impeller face of the downstream fan j, and X i and X j represent the abscissa of fan i and fan j, respectively.
Based on the above embodiment, step S103 includes the step of constructing a directed network map corresponding to the target wind farm according to a coupling relationship between a wake effect and distance information of each fan, where the step includes:
Defining each fan in the target wind power plant as a node, and forming a plurality of edges between different nodes;
calculating a weight coefficient of each edge according to the overlapping area and the distance information between adjacent nodes to form an adjacent weight matrix;
And taking the adjacency weight matrix as the directed network graph.
In the specific implementation, the large-scale offshore wind farm is considered to be composed of a plurality of fans, and the fans have strong coupling performance due to the influence of wake effects. Meanwhile, the action of the upstream fan affects the downstream fan, but the action of the downstream fan does not affect the upstream fan, and the target wind power plant can be described by a directed network.
If n fans exist in the target wind power plant, n nodes exist in the corresponding network, and the association relation between the nodes can be constructed by using a weighted directed graph G. The directed network graph G is represented by an adjacency matrix W, the ith row and the jth column of the matrix representing a node, respectively, and the matrix elements representing the weights of the edges. If the node j can be affected by the node i, the node j is called a neighboring node of the node i, and the corresponding element is defined according to an association relationship between the nodes and has directivity corresponding to two nodes.
For wind farm network problems, each fan represents a node in the network, and the association between fans can be represented as edges of the directional network. Each edge is typically assigned a weight that represents the strength of interaction between nodes. Thus, the adjacency matrix W is:
Where w= (ω ij)n×n is an adjacent weight matrix, also called an associated weight matrix, and ω ij is an associated intensity value representing the relationship between fan i and fan j.
Considering that the weight definition index of each edge of the existing method is as follows: ① Downstream distance between fans x ij,② overlap area a overlap of wake and impeller face. Wherein the overlapping area plays a decisive role. This definition takes into account the coupling between upstream and downstream fans, but does not fully account for the effect of distance, may split multiple edge fans whose physical locations are completely separated into a subset, and does not directly account for the effect on fan status. A directed network graph of the wind farm based on wake effects and physical distances can be constructed in combination with parameters D W = 1/D that characterize neighboring location factors in the wind farm. For subsequent quantization analysis, normalization processing is performed on each factor to obtain the weight coefficient, the adjacent weight matrix is formed, and the improved weight coefficient omega ij is defined as:
Where i and j represent upstream and downstream fans, respectively, D is the rotor diameter of the fan, and it is generally believed that the wake effect of the upstream fan will affect the downstream fan within 15D; The overlap ratio representing the wake area of fan i and the impeller face of fan j is defined as:
In addition, the network structure of the wind farm is also affected by wind farm control under specific wind conditions. Along with the change of the state of the fan, wake flow distribution of the wind power plant can also change to a certain extent, and then the wind power plant network structure is influenced. Especially in large offshore wind farms, the fan coupling relationship is complicated, and the degree of change of the fan state to the wind farm network is also aggravated.
Further, in step S104, the step of clustering the directed network graph by a preset clustering method to obtain a plurality of subsets includes:
Constructing a similarity matrix of spectrum analysis according to the directed network graph, and constructing a main diagonal matrix according to the access degree of all the nodes;
Normalizing a Laplace matrix according to the similarity matrix and the main diagonal matrix;
Solving the Laplace matrix to obtain a feature matrix;
And clustering the feature matrix by using a K-means++ algorithm to obtain a plurality of subsets.
In specific implementation, under specific wind conditions and control conditions, the wind farm directional network diagram is provided with an adjacent weight matrix, the coupling relation among wind farm fans is parameterized, and the wind farm network can be divided into a plurality of smaller subsets according to the weights. In order to realize the analysis of the directed graph, the traditional spectral clustering method is improved; to obtain a reliable initial center, an initial screening was performed using K-means++. The directed graph-oriented spectral clustering method is combined with an initial K-means++ algorithm to realize clustering treatment on a large wind power plant network, and the specific steps are as follows:
And 1, constructing a spectrum analysis foundation. According to the wind farm network structure G= (v, epsilon, W), a similarity matrix A=W+I+W T of spectrum analysis is constructed by using an adjacency matrix W of the directed graph, and a main diagonal matrix D is constructed according to the access degree of all the nodes.
And 2, normalizing the Laplace matrix. And normalizing the Laplace matrix according to the main diagonal matrix D and the similarity matrix A.
LG＝I-D-1/2AD-1/2
Wherein I is an identity matrix with the same dimension as A, and D is a main diagonal matrix representing node access degree. Definition of ith node correspondenceWherein, AndRespectively representing the ingress and egress of node i.
And step 3, solving a feature matrix. And calculating the characteristic value of L G, sorting the characteristic values from small to large, taking characteristic vectors U 1,u2,...,uk corresponding to the first k characteristic values, and standardizing according to the rows to form an n multiplied by k characteristic matrix U, namely, each row is a characteristic row vector of a corresponding node.
And 4, selecting a clustering method to realize clustering. And clustering the feature matrix U by the K-means++ clustering method to obtain a matrix C with 1 Xn dimension representing the node number, wherein the number corresponds to K small clusters.
On the basis of the above embodiment, the state type includes an initial action phase and a gentle running phase.
Further, in step S105, performing a two-stage optimization adjustment operation on each subset according to the state type of the target wind farm to obtain a new subset and a corresponding optimal control variable thereof, including:
in the initial action stage, performing rapid iterative optimization operation on each subset by using an improved balance optimization algorithm to obtain an updated adjacency matrix;
judging whether the variation value of the updated adjacent matrix is larger than or equal to a critical value;
If yes, continuing the rapid iterative optimization operation, and then carrying out clustering treatment to update a subset until the variation value is smaller than the critical value or the maximum iterative times are reached;
If not, judging the target wind power plant to be in a gentle running stage, carrying out re-clustering treatment on the updated adjacent matrix, and carrying out gentle optimization operation on the obtained new subset to obtain the corresponding optimal control variable.
In specific implementation, the balance optimization algorithm is a meta heuristic algorithm based on a physical rule, and inspiration comes from a balance model for controlling volume and mass and is used for estimating dynamic and balance states. Compared with other meta-heuristic algorithms, such as a genetic algorithm, a particle swarm optimization algorithm, a gray wolf optimization algorithm and the like, the balance optimization algorithm has high solving speed and stronger global optimal searching capability when solving the high-dimensional optimization variable problem.
The updating rule of the balance optimization algorithm is as follows:
In the method, in the process of the invention, In order to obtain the concentration of the water,In order to balance the concentration of the liquid,Is an index term of the index number,In order to achieve a yield of the product,For turnover rate, V is control volume. The first term represents equilibrium concentration, and the second and third terms represent concentration changes.
Wherein, the first itemRandomly selecting from balance pool, one important value is average balance concentrationAffecting the direction of the search; the second itemThe ability to influence global searches is defined as:
Where a 1 is a key constant value for controlling the exploration ability. The higher the value of a 1, the better the global optimization capability and the lower the local optimization benefit.
Corresponding to average equilibrium concentrationAnd a search control constant a 1, respectively introducing elite particle lifting strategy and optimizing search self-adapting method, so as to realize the improvement of the balance optimization algorithm. The specific description is as follows:
1) Weights w are set for 4 particles based on their fitness. The effect of elite particles is effectively improved, and algorithm convergence is further improved. The weight increment of the ith particle is set to be the ratio of the particle fitness increment to the sum of the N particle fitness increments.
In the method, in the process of the invention,The weight coefficient of the particle i can be regarded as f (p i) representing the fitness of the ith particle.
2) The optimizing adjustment parameter τ is used instead of a 1. The parameter τ is associated with the subset size, correspondingly adjusting the capabilities of global optimization and local optimization. The larger the subset, the more globally optimizable the algorithm, but at the same time the effect of local optimization is reduced.
Where n i and n correspond to the number of fans in the ith subset and full field, respectively.
Elite weight adjustment plays a role of elite particles better by adjusting and optimizing balance factor weights in a balance pool, improves the convergence speed of an algorithm, performs optimizing search, and adjusts the searching capability of local and global optimization according to the size difference of clusters in the target wind power plant network.
According to the change of the state of the fan, the running state of the target wind farm can be divided into two stages: an initial motion phase and a gentle running phase. In the initial action stage, the running state of each fan is changed greatly, and the wake flow distribution of the wind power plant is changed greatly, so that the accuracy of network clustering is affected; and in the gentle running stage, the running of each fan is mainly locally regulated, and the network clustering result is relatively stable.
As shown in fig. 4, two different optimization modes are set for the different characteristics of the two stages of operation optimization: a fast iterative optimization mode and a gentle optimization mode. The fast optimization mode aims at solving a group of better wind power plant control parameters in a shorter period so as to update the running state of the wind power plant. The gentle optimization mode aims at solving a group of optimal or near-optimal control parameters under a relatively stable wind power plant network structure condition and taking the optimal or near-optimal control parameters as a stable running state of the wind power plant under a specific wind condition.
And defining an adjacency matrix variation value psi for the variation degree of the target wind power plant network, wherein the adjacency matrix variation value psi is used for reflecting the operation optimization stage of the wind power plant. In the wind farm network G, the degree of change in the wind farm network is represented using the change values of the elements of the n×n adjacency matrix W.
In the method, in the process of the invention,AndRepresenting the weight coefficients of the kth and k-1 th generation, respectively, lambda being the total number of non-zero wake coefficients in W,Representing the average of all the initial non-zero weight coefficients.
Based on the judgment of the variance value of the adjacent matrix, the wind farm operation optimization is solved in two steps, for example, the specific steps are as follows:
1) And setting subset maximum node constraint on the basis of the adjacent weight matrix W by combining a spectral clustering algorithm, and dividing the wind power plant into K fan subsets by taking network clustering decoupling of the target wind power plant as a target.
2) And (3) carrying out online control optimization on each fan subset of the wind power plant by adopting an improved balance optimization algorithm to obtain a group of better control variables, and updating W and G.
3) And (3) calculating a variation value psi of the updated adjacent matrix W, and if the variation value is larger than or equal to the critical value psi 0, cycling the steps 1) to 2) until the variation value is within the critical value range or the dynamic iteration reaches the maximum number of times, and entering the next step.
4) Performing gentle optimization operation on the updated adjacent matrix to obtain the optimal control variable corresponding to each subset
Further, the step of performing a re-clustering process on the updated adjacency matrix and performing a gentle optimization operation on the obtained new subset to obtain the corresponding optimal control variable includes:
Decoupling into a plurality of new subsets according to the updated adjacency matrix;
And performing control optimization on each new subset by using the improved balance optimization algorithm to obtain an optimal control variable corresponding to each new subset.
In specific implementation, according to the fan subset division of the target wind power plant after the rapid iterative optimization operation, the updated adjacent matrix is decoupled into a plurality of subsets, and then the balance optimization algorithm is adopted to perform gentle control optimization, so that the optimal or near-optimal control variable corresponding to each subset is solved and used as the wind power plant fan control parameter during stable operation.
The present solution will be described in connection with a specific embodiment, for example an offshore wind farm fan cluster, comprising 72 NREL5MW fans as shown in fig. 5. The arrangement of the fans in the wind power plant is set to 9 rows and 8 columns, wherein the transverse distance between the fans in the same row is 5D, the longitudinal distance between the adjacent rows is 3D, and the fans are obliquely arranged downwards at an angle of about 11.7 degrees. Let the x-axis direction be 0 ° wind direction and the counterclockwise direction be the positive direction.
The NREL5MW fan is a representative MW-level commercial fan, has the most common three-blade structure at present, can be used as a basic reference model of the offshore wind farm fan and is used for quantifying the running condition of the wind farm, the main parameters of the fan are shown in the table 1,
TABLE 1
Setting the relative yaw angle adjusting range as minus 30 degrees and 30 degrees, and setting the axial induction factor adjusting range as [0,1]. The initial state defaults to greedy operation, namely the relative yaw angles of all fans are 0 degrees, and the axial induction factor is 1/3.
Considering the rated wind speed of the NREL5MW fan and the common wind conditions of the offshore wind farm comprehensively, stable 10m/s is adopted as the case analysis wind inlet wind speed condition, and the initial power of the wind farm under each wind direction condition is calculated in a simulation mode, as shown in fig. 6.
As can be seen from fig. 6, the wind farm power at each wind direction has an approximate symmetry. Therefore, the wake effect of the incoming wind direction in the range of 0 ° to 90 ° is mainly analyzed. When the wind inlet direction is 0-10 degrees, the average power loss of the wind power plant reaches 54.10 percent; when the wind inlet direction is 30-40 degrees, the average power loss of the wind power plant reaches 38.83%; when the wind inlet direction is 60-70 degrees, the average power loss of the wind power plant reaches 19.75 percent. In order to better analyze the influence of wake effects and verify the effects of the methods presented herein, 4 wind conditions with wind directions of 0 °,5 °,30 ° and 60 ° were selected for verification and analysis, respectively.
The relevant parameters for the corresponding calculation are shown in table 2. Wherein K is the number of clusters, dy is the maximum iteration number of dynamic optimization, and psi 0 is the maximum iteration number and population size of the adjacent matrix variation value critical value which is 0.5 times of the first variation value psi 1, T1 and n1, T2 and n2, and T3 and n3 are respectively and rapidly optimized, smoothly optimized and intensively optimized.
TABLE 2
Greedy, centralized and distributed operation methods are respectively applied to the wind farm. The distributed method divides fans in the wind power plant into 4 subsets, and further distributed intelligent operation optimization of the wind power plant is achieved. The grouping of subsets is shown in fig. 7-10 under different wind conditions.
In the flow field coordinates of fig. 7 to 10, the actual wind direction is the horizontal direction from left to right. A wind power plant consisting of 72 fans is divided into 4 subsets with lower coupling degree according to the coupling relation mainly based on wake effect. Under the condition of each wind inlet wind direction, the number of each fan subset of the wind power plant is as follows:
{0°:1-27，2-29，3-8，4-8}；
{5°:1-12，2-10，3-26，4-24}；
{30°:1-12，2-24，3-12，4-24}；
{60°:1-23，2-9，3-18，4-22}。
the output power of each fan in the wind power plant is compared under three operation methods of greedy type, centralized type and distributed type, as shown in fig. 11 to 14. Taking fig. 11 as an example, when greedy operation is performed, the upstream fans are not affected by wake, for example, the output power of fans numbered 1-9 is 3.48MW; the power loss of the downstream fans is serious, for example, the output power of fans numbered 10-18 is 1.17MW, and the output power of fans numbered 19-27 is 1.02MW. After centralized operation optimization, the output power of the upstream fans 1-9 is reduced, the overall output power of the downstream fans is partially improved, and the single fan power gain is concentrated between 0.6 MW and 0.9 MW; after distributed operation optimization, the corresponding 4 subsets are greatly improved, for example, subset 1, and compared with 47.0MW after centralized optimization, higher optimized power of 49.4MW is obtained. The output power of the remaining fan subsets is shown in annex table A1.
The optimized power and the optimized time of the whole wind farm under the three operation methods are shown in table 3, and it can be seen that the optimization efficiency and the performance of the scheme of the present disclosure on the target wind farm are higher.
TABLE 3 Table 3
The foregoing is merely specific embodiments of the disclosure, but the protection scope of the disclosure is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the disclosure are intended to be covered by the protection scope of the disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
Claims (6)
1. The distributed operation optimization method for the wind farm based on the wake flow directed graph is characterized by comprising the following steps of:
inputting position information and wind condition information of a fan in a target wind power plant into a wake model to obtain initial wake distribution of the target wind power plant;
calculating the effective wind speed of each fan according to the relevance between the initial wake flow distribution and each fan;
Constructing a directed network diagram corresponding to the target wind power plant according to the coupling relation between the wake effect and the distance information of each fan;
clustering the directed network graph by a preset clustering method to obtain a plurality of subsets;
Performing two-stage optimization adjustment operation on each subset according to the state type of the target wind power plant to obtain a new subset and an optimal control variable corresponding to the new subset, wherein the state type comprises an initial action stage and a gentle operation stage;
The step of performing two-stage optimization adjustment operation on each subset according to the state type of the target wind power plant to obtain a new subset and the corresponding optimal control variable thereof comprises the following steps:
in the initial action stage, performing rapid iterative optimization operation on each subset by using an improved balance optimization algorithm to obtain an updated adjacency matrix;
judging whether the variation value of the updated adjacent matrix is larger than or equal to a critical value;
If yes, continuing the rapid iterative optimization operation, and then carrying out clustering treatment to update a subset until the variation value is smaller than the critical value or the maximum iterative times are reached;
If not, judging the target wind power plant to be in a gentle running stage, carrying out re-clustering treatment on the updated adjacent matrix, and carrying out gentle optimization operation on the obtained new subset to obtain the corresponding optimal control variable;
The step of performing a sub-clustering process on the updated adjacency matrix and performing a gentle optimization operation on the obtained new subset to obtain the corresponding optimal control variable comprises the following steps:
Decoupling into a plurality of new subsets according to the updated adjacency matrix;
performing control optimization on each new subset by using the improved balance optimization algorithm to obtain an optimal control variable corresponding to each new subset;
Substituting all the optimal control variables and the effective wind speed into a distributed optimization function to obtain output power.
2. The method of claim 1, wherein the step of inputting location information and wind condition information of fans within the target wind farm into the wake model to obtain an initial wake distribution of the target wind farm comprises:
Establishing a Cartesian coordinate system transformed according to a flow field, bringing the position information and the wind condition information into the Cartesian coordinate system, and calculating a wake zone type of each fan, wherein the wake zone type comprises a near wake zone, a far wake zone and a mixing zone;
And forming the initial wake distribution according to the wake zone type of each fan.
3. The method of claim 2, wherein an x-axis of the cartesian coordinate system points in a direction into the wind of the target wind farm and a y-axis points in a direction orthogonal to the x-axis.
4. The method of claim 2, wherein the step of calculating an effective wind speed for each of the fans from the correlation of the initial wake distribution with each of the fans comprises:
calculating a speed deficiency coefficient corresponding to the wake area type of each fan, and calculating the overlapping area of different wake areas of the fans at the upstream of the target wind power plant and the impeller surface of the fan at the downstream of the target wind power plant;
And calculating the effective wind speed of each fan according to the speed deficiency coefficient and the overlapping area.
5. The method according to claim 4, wherein the step of constructing the directed network map corresponding to the target wind farm according to the coupling relationship between the wake effect and the distance information of each fan includes:
Defining each fan in the target wind power plant as a node, and forming a plurality of edges between different nodes;
calculating a weight coefficient of each edge according to the overlapping area and the distance information between adjacent nodes to form an adjacent weight matrix;
And taking the adjacency weight matrix as the directed network graph.
6. The method of claim 1, wherein the step of clustering the directed network graph by a preset clustering method to obtain a plurality of subsets comprises:
constructing a similarity matrix of spectrum analysis according to the directed network graph, and constructing a main diagonal matrix according to the access degree of all nodes;
Normalizing a Laplace matrix according to the similarity matrix and the main diagonal matrix;
Solving the Laplace matrix to obtain a feature matrix;
And clustering the feature matrix by using a K-means++ algorithm to obtain a plurality of subsets.
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