CN114021346A - Wind power plant distributed operation optimization method based on wake directed graph - Google Patents
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Abstract
The embodiment of the disclosure provides a distributed operation optimization method for a wind power plant based on a wake directed graph, which belongs to the crossing field of new energy and automation technology and specifically comprises the following steps: obtaining initial wake distribution of a target wind power plant; calculating the effective wind speed of each fan; constructing a directed network graph corresponding to a target wind power plant; 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; and substituting all the optimal control variables and the effective wind speed into a distributed optimization function to obtain the output power quantity. According to the scheme, a distributed operation optimization mechanism based on intelligent clustering is constructed according to the optimization action characteristics of the wind power plant based on directed graphs and spectral clustering, the optimal control variable is obtained, the power maximization of the wind power plant is realized by controlling output, and the optimization efficiency and performance of the wind power plant are improved.
Description
Technical Field
The embodiment of the disclosure relates to the field of crossing of new energy and automation technologies, in particular to a distributed operation optimization method for a wind power plant based on a wake directed graph.
Background
At present, offshore wind power is developed rapidly by virtue of various advantages such as high wind speed, small turbulence, small wind cut and the like, and keeps a stable rising trend, so that the offshore wind power becomes a key direction of new energy planning. The wind speed and the wind direction of the offshore wind power plant are relatively stable, the wake effect is prolonged, and the coupling relation between the fans is highly complicated. The traditional control method is to carry out greedy control on 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 influenced.
In order to improve the power generation efficiency of the wind power plant, one effective way is to adopt wake control on the wind power plant to reduce the wake effect among the fans, the traditional wind power plant operation control adopts a centralized control architecture, and a centralized controller is relied on to communicate and optimize all the fans. The centralized control method needs higher communication capacity, is easy to sink into local optimization, has lower optimization effect, and is suitable for small wind power plants. In a large offshore wind farm, as the number of wind turbines increases, the control variables of the wind farm also increase in a nearly exponential fashion. Based on this, distributed control architectures and optimization algorithms have received extensive attention from researchers. At present, a distributed control architecture of a wind power plant is mainly divided into: the neighborhood communication solving efficiency based on the free model and the network division based on the graph theory are high, but the consideration factors are few, and the method is more suitable for the small wind power plant with a simple coupling relation.
Therefore, an efficient and high-optimization-performance wind power plant distributed operation optimization method based on the wake oriented graph is needed.
Disclosure of Invention
In view of this, the embodiment of the present disclosure provides a distributed operation optimization method for a wind farm based on a wake directed graph, which at least partially solves the problem in the prior art that optimization efficiency and optimization performance are poor.
The embodiment of the disclosure provides a distributed operation optimization method for a wind power plant based on a wake directed graph, 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 of the initial wake distribution and each fan;
constructing a directed network graph 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;
and substituting all the optimal control variables and the effective wind speed into a distributed optimization function to obtain the output power quantity.
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 wind turbine 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 the wake zone type of each fan, wherein the wake zone type comprises a near wake zone, a far wake zone and a mixed zone;
and forming the initial wake distribution according to the wake area type of each fan.
According to a specific implementation manner of the embodiment of the present 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 of the wind turbines according to the association between the initial wake distribution and each of the wind turbines includes:
calculating a speed deficit coefficient corresponding to the type of the wake area 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 fans at the downstream of the target wind power plant;
and calculating the effective wind speed of each fan according to the speed deficit coefficient and the overlapping area.
According to a specific implementation manner of the embodiment of the present disclosure, the step of constructing the directed network graph corresponding to the target wind farm according to the coupling relationship between the wake effect and the distance information of each of the fans includes:
defining each fan in the target wind power plant as a node, and forming a plurality of edges among different nodes;
calculating the weight coefficient of each edge according to the overlapping area and the distance information between adjacent nodes to form an adjacent weight matrix;
taking the adjacency weight matrix as the directed network graph.
According to a specific implementation manner of the embodiment of the present disclosure, the clustering the directed network graph by a preset clustering method to obtain a plurality of subsets includes:
constructing a similar matrix of spectral analysis according to the directed network graph, and constructing a main diagonal matrix according to the in-out degrees 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 manner of the embodiment of the present disclosure, the state types include an initial action phase and a slow running phase.
According to a specific implementation manner of the embodiment of the present disclosure, the step of performing two-stage optimization adjustment operation on each subset according to the state type of the target wind farm to obtain a new subset and an optimal control variable corresponding to the new subset includes:
in the initial action stage, performing fast 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 adjacency matrix is larger than or equal to a critical value;
if so, continuing to perform the rapid iterative optimization operation, and performing clustering processing to update the subsets until the variation value is smaller than the critical value or the maximum iteration number is reached;
and if not, judging that the target wind power plant is in a gentle operation stage, performing 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 clustering process on the updated adjacency matrix and performing 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 the optimal control variable corresponding to each new subset.
The distributed operation optimization scheme of the wind power plant based on the wake 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 of the initial wake distribution and each fan; constructing a directed network graph corresponding to the target wind power plant according to a coupling relation between a 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; and substituting all the optimal control variables and the effective wind speed into a distributed optimization function to obtain the output power quantity.
The beneficial effects of the embodiment of the disclosure are: according to the scheme, a distributed operation optimization mechanism based on intelligent clustering is constructed according to the optimization action characteristics of the wind power plant based on directed graphs and spectral clustering, the optimal control variable of each subset is obtained, the power maximization of the wind power plant is realized by controlling output, and the efficiency and the performance of the optimization of the wind power plant are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used 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 it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flow chart of a distributed operation optimization method for a wind farm based on a wake directed graph according to an embodiment of the present disclosure;
fig. 2 is a schematic overall optimization flow diagram related to a distributed operation optimization method for a wind farm based on a wake directed graph according to an embodiment of the present disclosure;
fig. 3 is a schematic view of a wake region and a vane surface involved in a distributed operation optimization method for a wind farm based on a wake directed graph according to an embodiment of the present disclosure;
fig. 4 is a schematic flow chart of a two-stage optimization adjustment operation involved in a distributed operation optimization method for a wind farm based on a wake directed graph according to an embodiment of the present disclosure;
FIG. 5 is a schematic view of a wind farm fan distribution provided by an embodiment of the present disclosure;
FIG. 6 is a schematic power diagram of a wind farm at each wind direction provided by the embodiments of the present disclosure;
fig. 7 to fig. 10 are schematic diagrams illustrating cluster grouping results in flow field coordinates according to an embodiment of the present disclosure;
fig. 11 to 14 are schematic diagrams of output power of each wind turbine in wind farms in different wind directions according to the embodiment of the present disclosure.
Detailed Description
The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure in the specification. It is to be understood that the described embodiments are merely illustrative of some, and not restrictive, of the embodiments of the disclosure. The disclosure may be embodied or carried out in various other specific embodiments, and various modifications and changes may be made in the details within the description without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
It is noted that various aspects of the embodiments are described below which are within the scope of the appended 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 disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects 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 an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present disclosure, and the drawings only show the components related to the present disclosure rather than the number, shape and size of the components in actual implementation, and the type, number and ratio of the components in actual implementation may be changed arbitrarily, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided to facilitate 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 distributed operation optimization method for a wind power plant based on a wake directed graph, and the method can be applied to a wind power plant optimization process in an offshore wind power generation scene.
Referring to fig. 1, a flow chart of a wind farm distributed operation optimization method based on a wake directed graph is provided for the 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;
during specific implementation, the accurate description of the wake flow distribution of the wind turbine is considered to be the basis for realizing the power calculation of the wind turbine and is also an important means for further analyzing the coupling relation between the wind turbines. For example, for the study of the wind farm wake distribution, a Multizone wake model that accurately characterizes the wake effect and is suitable for control solution may be employed. And then, inputting the position information and the wind condition information of the wind turbine in the target wind power plant 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 of the initial wake distribution and each fan;
in specific implementation, considering the problems that a plurality of fans exist in a 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 power generation are different, and the effective wind speed of each fan can be calculated according to the relevance of the initial wake distribution and each fan.
S103, constructing a directed network graph corresponding to the target wind power plant according to the coupling relation between the wake effect and the distance information of each fan;
consider a wind farm in which the wind turbine extracts energy from the wind while downstream thereof forming a wake zone of decreasing wind speed. If the downstream fan is located in the wake zone, 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 operation condition of each fan set in the wind power plant, and further influences the operation condition and output of the wind power plant; and influenced by factors such as wind power plant topology, wind wheel diameter, thrust coefficient, wind speed and wind direction, the directed network graph corresponding to the target wind power plant can be constructed according to the coupling relationship 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 specific implementation, considering that the existing technologies mostly control the whole wind farm or local optimal wind turbines, and do not well consider the problem of optimal control of each wind turbine, a directed network structure of the wind farm can be described based on a graph theory basis, so that parameter definition of the wind farm network is improved, then the wind farm is decoupled into a plurality of wind turbine clusters by combining a spectral clustering algorithm, clustering processing on the wind farm network is realized, and specifically, the directed network graph can be clustered by a preset clustering method to obtain a plurality of subsets, so as to perform distributed control.
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 an optimal control variable corresponding to the new subset;
after the directed network graph is clustered to obtain a plurality of subsets corresponding to the target wind power plant, the two-stage optimization adjustment operation can be performed on the subsets according to the state type of the target wind power plant in consideration of possible changes of the operating state of the target wind power plant to obtain new subsets and optimal control variables corresponding to each new subset.
And S106, substituting all the optimal control variables and the effective wind speed into a distributed optimization function to obtain the output power quantity.
In specific implementation, after the optimal control variable corresponding to each new subset is obtained, the optimal control variable is substituted into the distributed optimization function by combining the effective wind speed and the known parameters corresponding to each fan, so as to obtain an output power quantity, wherein the output power quantity is the maximum power optimized through distributed operation, and the overall optimization flow is shown in fig. 2.
For example, let S ═ {1, 2., N }, denote a cluster of wind turbines of a wind farm, and N denotes the total number of wind turbines in the wind farm. Using PiThe stable output power, expressed as fan i ∈ S, is calculated as follows:
where ρ represents the air density, AiIs the area of the impeller surface, ViFor effective wind speed, CPIs the power coefficient of the fan, and is related to the yaw angle gammaiAnd axial induction factor alphaiIs a non-linear function of (a). Under the condition of no yaw, define CP(ai)＝4ai[1-ai]2In the optimization of the operation studied here, the yaw angle γ is taken into accountiThe influence on the rotor power coefficient is further corrected to be:
CP＝4αi[1-αi]2ηcos(γi)Pp
in the formula, eta is a correction coefficient which comprehensively considers the efficiency of the generator and the maximum power factor, and Pp is a yaw adjustment parameter obtained by wind tunnel test. For detailed parameter setting, reference can be made to the literature.
Output power P of whole wind power planttotalCan be calculated from the following formula:
for centralized optimization, the whole wind farm is considered as a whole. The output power function corresponding to the whole wind power plant and the centralized optimization objective function of the wind power plant are represented by the following formula:
in the formula, f (x) represents the sum of the powers of all the fans in the wind power plant, K is the number of fan clusters after the network of the wind power plant is clustered, and N isjThe number of fans of the jth fan cluster. Defining a control variable matrix x ═ γ, α]T. Wherein the vector α ═ α1,α2,...,αn]The axial inductance of each fan; vector γ ═ γ1,γ2,...,γn]And the yaw angle of each fan relative to the wind direction of the incoming wind. Gamma rayminAnd gammamaxFor minimum and maximum constraints on yaw angle, αminAnd alphamaxThe minimum and maximum constraints of the axial induction factor.
For distributed optimization, a wind power plant is decoupled into K fan clusters, a large and centralized full-field optimization problem is converted into a plurality of small and distributed cluster optimization problems, and a corresponding output power function is as follows:
the objective function of the jth subset in the distributed optimization of the wind farm, corresponding to the distributed output power function, is represented by the following equation:
according to the distributed operation optimization method for the wind power plant based on the wake directed graph, a distributed operation optimization mechanism based on intelligent clustering is constructed according to the optimization action characteristics of the wind power plant based on the directed graph and spectral clustering, the optimal control variable of each subset is obtained, the power maximization of the wind power plant is realized by controlling output, and the efficiency and the performance of the optimization of the wind power plant are improved.
On the basis of the above embodiment, step S101, inputting the position information and wind condition information of the wind turbine 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 the wake zone type of each fan, wherein the wake zone type comprises a near wake zone, a far wake zone and a mixed zone;
and forming the initial wake distribution according to the wake area 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 of the cartesian coordinate system points to a direction orthogonal to the x-axis.
For example, to describe the wake distribution behind the fan, a cartesian coordinate system (x, y) according to the flow field transformation is used.
And the x axis points to the wind inlet direction of the target wind power plant, and the y axis points to the direction orthogonal to the x axis.
In the formula (I), the compound is shown in the specification,
(X) is the initial position of the wind turbine of the target wind farmi,Yi) And phi is the position of a fan flow field of the target wind power plant, and phi is the wind direction of the target wind power plant.
Then, within the transformed coordinate system, the Multizone model defines three wake zones, namely a "near wake zone", a "far wake zone", and a "mixed zone", each having a unique velocity deficit coefficient. The parameters are adjusted to fit the actual wake effect. The effective wind speed at fan j is calculated by the overlapping area of the wake zone 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 of the wind turbines according to the association between the initial wake distribution and each of the wind turbines includes:
calculating a speed deficit coefficient corresponding to the type of the wake area 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 fans at the downstream of the target wind power plant;
and calculating the effective wind speed of each fan according to the speed deficit coefficient and the overlapping area.
E.g. upstreamThe effective wind speed of the fan i is equal to the wind inlet wind speed V of the wind power plant∞The effective speed of the downstream fan j is described by the influence of the wake zone of each upstream fan i in the integrated fan cluster S, and is represented as:
in the formula, alphaiIs the axial inductance of the upstream fan i, ci,q(xj) Corresponding to the speed deficit coefficient of each wake zone,
is the overlapping area of the qth wake zone of the upstream fan i and the impeller surface of the downstream fan j, AjIs the impeller surface of the downstream fan j, XiAnd XjRespectively, the abscissa of fan i and fan j.
On the basis of the foregoing embodiment, the step S103 of constructing the directed network graph corresponding to the target wind farm according to the coupling relationship between the wake effect and the distance information of each of the fans includes:
defining each fan in the target wind power plant as a node, and forming a plurality of edges among different nodes;
calculating the weight coefficient of each edge according to the overlapping area and the distance information between adjacent nodes to form an adjacent weight matrix;
taking the adjacency weight matrix as the directed network graph.
In specific implementation, the large offshore wind farm is formed by a plurality of fans, and the fans are strongly coupled due to the influence of wake effect. Meanwhile, the function of the upstream fan affects the downstream fan, but the function of the downstream fan does not affect the upstream fan, so that 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 incidence relation between the nodes can be constructed by using the weighted directed graph G. The directed network graph G is represented by an adjacency matrix W, the ith row and the jth column of which respectively represent a node, and the matrix elements represent the weights of the edges. If the node j can be influenced by the node i, the node j is called a neighboring node of the node i, and the corresponding element is defined according to the incidence relation between the nodes and has the directivity corresponding to the two nodes.
For wind farm network problems, each fan represents a node in the network, and the associations between fans can be represented as edges of the directional network. Each edge is typically assigned a weight, representing the strength of the interaction between the nodes. Thus, the adjacency matrix W is:
wherein W is (ω)ij)n×nAs a neighboring weight matrix, also known as a correlation weight matrix, omegaijIs a value representing the strength of association of fan i to fan j.
Considering the existing method, the weight definition index for each edge is as follows: (ii) downstream distance x between fans ij② the overlapping area A of the wake and the impeller surfaceoverlap. Wherein the overlapping area plays a decisive role. This definition takes into account the coupling between the upstream and downstream fans, but does not adequately account for the effects of distance, may group multiple edge fans that are physically completely separated into a subset, and may not directly account for the effects on fan status. Then the parameter D can be combined to be a parameter for representing adjacent position factors in the wind power plantWAnd (4) constructing a wind power plant directed network graph based on wake effect and physical distance as 1/D. For subsequent quantitative analysis, normalization processing is carried out on each factor to obtain the weight coefficient, the adjacent weight matrix is formed, and the improved weight coefficient omega isijIs defined as:
in which i and j are respectively shown inShowing an upstream fan and a downstream fan, D being the rotor diameter of the fan, it is generally believed that the wake effect of the upstream fan will have an effect on the downstream fan within a distance of 15D;
the overlap ratio of the wake area of the fan i and the impeller surface of the fan j is defined as:
furthermore, the network structure of a wind farm is also influenced by the wind farm control under certain wind conditions. With the change of the state of the fan, the wake distribution of the wind power plant also changes to a certain degree, and further influences the network structure of the wind power plant. Particularly, in a large offshore wind farm, the coupling relation of the wind turbine is complicated, and the change degree of the wind turbine state to the wind farm network is also aggravated.
Further, the step S104 of clustering the directed network graph by a preset clustering method to obtain a plurality of subsets includes:
constructing a similar matrix of spectral analysis according to the directed network graph, and constructing a main diagonal matrix according to the in-out degrees 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 adjacent weight matrix of the wind power plant directed network graph parameterizes the coupling relation between the wind power plant fans, and the wind power plant 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; in order to obtain a reliable initial center, K-means + + is used for initial screening. Combining a directed graph-oriented spectrum clustering method with an initial K-means + + algorithm to realize clustering processing of a large wind power plant network, the method comprises the following specific steps:
step 1, establishing a spectrum analysis basis. According to the wind power plant network structure G ═ (v, epsilon, W), a similarity matrix A ═ W + I + W of spectral analysis is constructed by using an adjacency matrix W of a directed graphTAnd constructing a main diagonal matrix D according to the degree of entrance and exit of all the nodes.
And 2, standardizing the Laplace matrix. And normalizing the Laplace matrix according to the main diagonal matrix D and the similar matrix A.
LG＝I-D-1/2AD-1/2
In the formula, I is an identity matrix with the same dimension as A, and D is a main diagonal matrix representing the entrance and exit degree of the node. Defining an ith node correspondence
Wherein the content of the first and second substances,
and
respectively representing the in-degree and out-degree of the node i.
And 3, solving the feature matrix. Calculating LGThe characteristic values are sorted from small to large, and the characteristic vectors u corresponding to the first k characteristic values are taken1,u2,...,ukAnd forming an n multiplied by k dimensional feature matrix U after line normalization, namely each line is a feature line vector of a corresponding node.
And 4, selecting a clustering method to realize clustering. And selecting the K-means + + clustering method to perform clustering processing on the characteristic matrix U to obtain a1 xn-dimensional matrix C representing the node number, wherein the number corresponds to K small clusters.
On the basis of the above embodiment, the state types include an initial action phase and a slow running phase.
Further, the step S105 of performing two-stage optimization adjustment operation on each subset according to the state type of the target wind farm to obtain a new subset and an optimal control variable corresponding to the new subset includes:
in the initial action stage, performing fast 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 adjacency matrix is larger than or equal to a critical value;
if so, continuing to perform the rapid iterative optimization operation, and performing clustering processing to update the subsets until the variation value is smaller than the critical value or the maximum iteration number is reached;
and if not, judging that the target wind power plant is in a smooth operation stage, performing clustering treatment on the updated adjacent matrix, and performing smooth optimization operation on the obtained new subset to obtain the corresponding optimal control variable.
In specific implementation, the balance optimization algorithm is a metaheuristic algorithm based on physical laws, flexibly 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 algorithm, a wolf optimization algorithm and the like, the balance optimization algorithm is high in solving speed, and has stronger capability of finding global optimum when solving a high-dimensional optimization variable problem.
The updating rule of the balance optimization algorithm is as follows:
in the formula (I), the compound is shown in the specification,
in order to be the concentration of the water,
in order to balance the concentration of the water,
in the form of an exponential term, the term,
in order to obtain a high yield of the product,
for turnover, V is the control volume. The first term represents equilibrium concentration, and the second and third terms represent concentration changes.
In the formula, the first item
Randomly selected from the equilibrium pool, and one important value is the average equilibrium concentration
Influence the direction of the search; of the second item
The ability to affect a global search is defined as:
in the formula, a1Is a key constant value that controls the exploratory ability. a is1The higher the value, the better the global optimization capability and the lower the local optimization benefit.
Corresponding to average equilibrium concentration
And search for a control constant a1Respectively introducing an elite particle lifting strategy and an optimization search self-adaptive method to realize the improvement of a balance optimization algorithm. The specific description is as follows:
1) weights w are set for 4 particles based on their fitness. Effectively improving the function of the elite particles and further improving the algorithm convergence. The weight increment of the ith particle is set as the ratio of the particle fitness increment to the sum of the N particle fitness increments.
In the formula (I), the compound is shown in the specification,
can be regarded as the weight coefficient of the particle i, f (p)i) The fitness of the ith particle is shown.
2) Using the optimization tuning parameter tau instead of a1. The parameter τ is associated with the subset size, corresponding to the ability to adjust global and local optimizations. The larger the subset is, the stronger the global optimization capability of the algorithm is, but at the same time, the effect of local optimization is reduced.
In the formula, niAnd n respectively corresponds to the number of fans of the ith subset and the whole field.
The elite weight adjustment can better play the role of elite particles by adjusting the weight of balance factors in an optimization balance pool, improve the convergence rate of the algorithm, search for optimization, and adjust the search 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 wind turbine, the operation state of the target wind power plant can be divided into two stages: an initial action phase and a gentle operation phase. In the initial action stage, the running state of each fan is changed greatly, the wake flow distribution of the wind power plant is changed greatly, and the accuracy of network clustering is further influenced; in the gentle operation stage, the operation of each fan is mainly adjusted locally, and the network clustering result is relatively stable.
As shown in fig. 4, two different optimization modes are set for different characteristics of the two phases of the above operation optimization: a fast iterative optimization mode and a flat optimization mode. In the fast optimization mode, the aim is to solve a group of more optimal wind power plant control parameters in a shorter period so as to update the running state of the wind power plant. And in the mild optimization mode, the aim is to solve a group of optimal or near optimal control parameters under the condition of a relatively stable wind power plant network structure and use the optimal or near optimal control parameters as the stable running state of the wind power plant under the condition of a specific wind condition.
And aiming at the change degree of the target wind power plant network, defining an adjacency matrix variation value psi for reflecting the operation optimization stage of the wind power plant. In the wind farm network G, the wind farm network change degree is expressed by using the change values of the elements of the n × n adjacency matrix W.
In the formula (I), the compound is shown in the specification,
and
representing the weight coefficients of the kth and k-1 respectively, λ being the total number of non-zero wake coefficients in W,
representing the average of all initial non-zero weight coefficients.
Based on the judgment of the size of the variation value of the adjacency matrix, performing two-step solution on the operation optimization of the wind power plant, for example, the specific steps are as follows:
1) and (3) combining a spectral clustering algorithm, setting subset maximum node constraint on the basis of the adjacent weight matrix W, 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 more optimal control variables, and updating W and G.
3) Calculating a variance ψ of the updated adjacency matrix W if the variance is greater than or equal to the critical value ψ0And then, the step 1) to the step 2) are circulated until the variation value is within the critical value range or the dynamic iteration reaches the maximum number of times, and then the next step is carried out.
4) Performing gentle optimization operation on the updated adjacency matrix to obtain the optimal control variable corresponding to each subset
Further, the step of performing clustering process on the updated adjacency matrix and performing smooth 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 the 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 adjacency matrix is decoupled into a plurality of subsets, then the balance optimization algorithm is adopted to carry out gentle control optimization, and the optimal or near optimal control variable corresponding to each subset is solved and used as the wind power plant fan control parameter in stable operation.
The present solution will be described with reference to a specific embodiment, for example, a wind turbine cluster of an offshore wind farm, which includes 72 NREL 5MW wind turbines as shown in fig. 5. The arrangement of the fans in the wind farm is arranged in 9 rows and 8 columns, wherein the transverse distance between the fans in the same row is 5D, the longitudinal distance between adjacent rows is 3D, and the fans are arranged obliquely downwards at an angle of about 11.7 degrees. The x-axis direction is assumed to be 0-degree wind direction, and the counterclockwise direction is taken as the positive direction.
The NREL 5MW wind turbine is a representative MW-grade commercial wind turbine, has the most common three-blade structure at present, can be used as a basic reference model of an offshore wind farm wind turbine for quantifying the operation condition of a wind farm, and the main parameters of the NREL 5MW wind turbine are shown in a table 1,
TABLE 1
Setting the relative yaw angle adjustment range as [ -30 degrees, 30 degrees ], and the axial induction factor adjustment range as [0,1 ]. The initial state defaults to greedy operation, namely the relative yaw angles of all the fans are 0 degrees, and the axial induction factor is 1/3.
Comprehensively considering the rated wind speed of the NREL 5MW wind turbine and the common wind condition conditions of the offshore wind farm, the stable 10m/s is used as the wind inlet wind speed condition for case analysis, and the initial power of the wind farm under each wind direction condition is subjected to simulation calculation, 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 flow influence of the wind direction of the inlet wind in the range of 0-90 degrees is mainly analyzed. When the wind direction is 0-10 degrees, the average power loss of the wind power plant reaches 54.10 percent; when the wind direction of the wind is 30-40 degrees, the average power loss of the wind power plant reaches 38.83 percent; when the wind 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 the wake effect and verify the effect of the method, 4 wind conditions with the wind direction of 0 degrees, 5 degrees, 30 degrees and 60 degrees are selected respectively for verification and analysis.
The relevant parameters for the corresponding examples are shown in table 2. Wherein K is the number of clusters, Dy is the maximum iteration number of dynamic optimization, psi0Taking a first variance psi for the threshold of the variance of the adjacency matrix10.5 times of the total population size, maximum iteration number and population size of T1 and n1, T2 and n2, T3 and n3 respectively for quick optimization, gentle optimization and centralized optimization。
TABLE 2
And applying greedy, centralized and distributed operation methods to the wind power plant respectively. The distributed method divides the fans in the wind power plant into 4 subsets, and further realizes distributed intelligent operation optimization of the wind power plant. In different wind conditions, the grouping of subsets is as shown in fig. 7 to 10.
In the flow field coordinates of fig. 7 to 10, the actual wind direction is horizontal 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 the wake effect. Under each wind inlet direction condition, the number of each fan subset of the wind power plant is respectively 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}。
output power of each fan in the wind power plant is compared under three operation methods, namely greedy operation method, centralized operation method and distributed operation method, as shown in fig. 11 to 14. Taking fig. 11 as an example, when operating in greedy mode, the upstream fan is not affected by wake flow, for example, the output powers of fans numbered 1 to 9 are all 3.48 MW; the power loss of the downstream fan is serious, for example, the output power of the fan numbered 10-18 is 1.17MW, and the output power of the fan numbered 19-27 is 1.02 MW. 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 power gain of a single fan is centralized between 0.6 and 0.9 MW; after the distributed operation optimization, the corresponding 4 subsets are greatly improved, for example, subset 1, and a higher optimized power of 49.4MW is obtained compared with 47.0MW after the centralized optimization. The output power of the remaining subsets of fans is shown in appendix A1.
The optimized power and the optimized time of the whole wind farm under the three operation methods are shown in the table 3, and it can be seen that the optimized efficiency and the optimized performance of the scheme disclosed by the invention on the target wind farm are higher.
TABLE 3
The above description is only for the specific embodiments of the present disclosure, but the scope of the present 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 present disclosure should be covered within the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
Claims (9)
1. A distributed operation optimization method for a wind power plant based on a wake directed graph is characterized by comprising 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 of the initial wake distribution and each fan;
constructing a directed network graph 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;
and substituting all the optimal control variables and the effective wind speed into a distributed optimization function to obtain the output power quantity.
2. The method of claim 1, wherein the step of inputting position information and wind condition information of wind turbines in the target wind farm into a 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 the wake zone type of each fan, wherein the wake zone type comprises a near wake zone, a far wake zone and a mixed zone;
and forming the initial wake distribution according to the wake area type of each fan.
3. The method of claim 2, wherein an x-axis of the cartesian coordinate system points in a direction of an incoming 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 wind turbines based on the association of the initial wake distribution with each of the wind turbines comprises:
calculating a speed deficit coefficient corresponding to the type of the wake area 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 fans at the downstream of the target wind power plant;
and calculating the effective wind speed of each fan according to the speed deficit coefficient and the overlapping area.
5. The method according to claim 4, wherein the step of constructing the directed net graph corresponding to the target wind farm according to the coupling relationship between the wake effect and the distance information of each wind turbine comprises the following steps:
defining each fan in the target wind power plant as a node, and forming a plurality of edges among different nodes;
calculating the weight coefficient of each edge according to the overlapping area and the distance information between adjacent nodes to form an adjacent weight matrix;
taking the adjacency weight matrix as the directed network graph.
6. The method according to claim 1, wherein the step of clustering the directed network graph by a predetermined clustering method to obtain a plurality of subsets comprises:
constructing a similar matrix of spectral analysis according to the directed network graph, and constructing a main diagonal matrix according to the in-out degrees 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.
7. The method of claim 6, wherein the status types include an initial action phase and a smooth run phase.
8. The method according to claim 7, wherein the step of performing two-stage optimization adjustment operation on each subset according to the state type of the target wind farm to obtain a new subset and an optimal control variable corresponding to the new subset comprises:
in the initial action stage, performing fast 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 adjacency matrix is larger than or equal to a critical value;
if so, continuing to perform the rapid iterative optimization operation, and performing clustering processing to update the subsets until the variation value is smaller than the critical value or the maximum iteration number is reached;
and if not, judging that the target wind power plant is in a smooth operation stage, performing clustering treatment on the updated adjacent matrix, and performing smooth optimization operation on the obtained new subset to obtain the corresponding optimal control variable.
9. The method according to claim 8, wherein the step of performing a re-clustering process on the updated adjacency matrix and performing a gradual optimization operation on the obtained new subset to obtain the corresponding optimal control variable comprises:
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 the optimal control variable corresponding to each new subset.
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