Mu PMU optimization layout method, system and device for micro-grid distribution and medium

Abstract

The invention discloses a mu PMU optimization layout method, system and device for a micro-grid and a medium, and belongs to the technical field of power systems. According to the method, a power grid data acquisition model, a layout generation model, a layout updating model and a layout evaluation model are constructed, a target function with the maximum comprehensive association information degree as a target is established, and mu PMU initial layout position information is obtained; on the basis of the observability and the mutual information amount, updating the initial layout position information of the mu PMU to obtain optimized layout position information of the mu PMU; and then on the basis of the state estimation precision of the micro-power distribution grid, the optimal layout configuration information of the mu PMU is determined, and the optimal layout of the mu PMU of the micro-power distribution grid is completed, so that the installation number and the installation position of the mu PMU can be comprehensively considered, the configuration number of the mu PMU is effectively reduced, the layout cost of the mu PMU is reduced, the maximum economic benefit is realized, and the popularization and the use of the mu PMU configuration method are facilitated. And meanwhile, the state estimation precision of the active power distribution network (ADN) can be effectively improved by a mu PMU optimal configuration method considering the node association information similarity.

Description

Technical Field

[0001] The present invention relates to a μPMU optimization layout method, system, equipment and medium for a distribution microgrid, and belongs to the technical field of power systems. Background Art

[0002] Large-scale distributed power sources, new loads and microgrids are incorporated into traditional distribution networks, transforming traditional distribution networks into active distribution networks containing distributed energy. Active distribution networks (ADNs) are characterized by flexibility, high automation and heterogeneous energy supply, which can greatly improve the consumption of new energy and solve problems such as power flow exceeding the limit. In order to enable operators to regulate and manage more accurately, state estimation is an important measure for the precise regulation and management of active distribution networks (ADNs). It can realize online monitoring and analysis of distribution microgrids to ensure their stable and optimized operation. At present, the monitoring data of distribution networks mainly comes from supervisory control and data acquisition (SCADA) systems, advanced metering infrastructure (AMI) systems and a small number of micro-phase measurement units (μPMUs). μPMUs use the global positioning system as the time reference to synchronously collect analog voltage and current signals at the sub-second level and provide voltage and current amplitudes and phases with time scales. Because the data collected has phasor characteristics, real-time and synchronization, it has gradually become a new means of dynamic monitoring of power grids. Table 1 shows the characteristics of the three measurement systems.

[0003] Table 1 Comparison of SCADA, AMI, and μPMU

[0004]

[0005] At present, μPMU devices are more expensive than other measurement devices, and large-scale μPMU deployment is costly. However, reasonable deployment of μPMU and combining it with the existing active distribution network ADN measurement system will greatly improve the state estimation accuracy and control efficiency of the distribution network.

[0006] Therefore, the configuration of monitoring equipment in the distribution network has recently attracted the attention of scholars at home and abroad. Some literature takes the optimization of the unobservability of nodes as the goal, uses integer programming models to solve, and places PMU devices at the nodes with the largest errors. The solution model is demanding and cannot adapt to today's active distribution network ADN. Another literature takes the observability of the distribution network as a constraint and introduces the injection power of the branch into the solution model in order to reduce the number of PMU configurations, but its ability to resist data loss is poor. There are also literatures that consider the radial structure of the distribution network and divide it into two grid structures, the main network and the subnet, and optimize the configuration of μPMU respectively, which improves the optimization speed but weakens the problem of measurement data accuracy. Other literatures propose to add phase angle information to optimize the layout of μPMU, simplify the solution process, and make the real-time estimation of the distribution network state stronger.

[0007] However, none of the above literatures comprehensively analyzes the number and location of μPMU installation, resulting in failure to maximize economic benefits, which is not conducive to the promotion and use of the μPMU configuration method.

[0008] The information disclosed in this Background Art is only for understanding the background of the inventive concept and therefore it may include information that does not constitute the prior art. Summary of the invention

[0009] In view of the above problem or one of the above problems, an object of the present invention is to provide a μPMU optimization layout method for a distribution microgrid. By constructing a power grid data acquisition model, a layout generation model, a layout update model, and a layout evaluation model, an objective function with the goal of maximizing the comprehensive correlation information is established to obtain the μPMU initial layout position information; and based on the observability and the mutual information, the μPMU initial layout position information is updated to obtain the μPMU optimized layout position information; then based on the state estimation accuracy of the distribution microgrid, the μPMU optimal layout configuration information is determined to complete the μPMU optimization layout of the distribution microgrid, so that the installation quantity and installation location of the μPMU can be comprehensively considered, thereby effectively reducing the number of μPMU configurations, reducing the μPMU layout cost, maximizing the economic benefit, and facilitating the promotion and use of the μPMU configuration method; at the same time, the μPMU optimization configuration method that considers the similarity of node correlation information can effectively improve the state estimation accuracy of the active distribution network ADN.

[0010] In response to the above problem or one of the above problems, the second object of the present invention is to provide a μPMU optimization layout method, system, device and medium for a distribution microgrid, which can maximize economic benefits and effectively improve the state estimation accuracy of the active distribution network ADN.

[0011] To achieve one of the above purposes, the first technical solution of the present invention is:

[0012] A μPMU optimization layout method for a distribution microgrid includes the following contents:

[0013] Through the pre-built grid data acquisition model, the grid structure information of the distribution microgrid is obtained as the graph model of the distribution microgrid

[0014] Using the pre-built layout generation model, according to the distribution microgrid graph model Establish an objective function with the maximum comprehensive correlation information as the goal; and based on the objective function, obtain the initial layout position information of μPMU; the maximum comprehensive correlation information means that the observability of the node where μPMU is placed and the mutual information of the node are the maximum; the initial layout position information of μPMU includes at least the position information of μPMU and the number of μPMU;

[0015] The pre-built layout update model is used to calculate the observability and mutual information of the nodes in the μPMU initial layout position information, and based on the observability and mutual information, the μPMU initial layout position information is updated to obtain the μPMU optimized layout position information;

[0016] Using the pre-built layout evaluation model, based on the μPMU optimized layout location information, the state estimation accuracy of the distribution microgrid at this time is calculated. Based on the state estimation accuracy of the distribution microgrid, the μPMU optimal layout configuration information is determined to complete the μPMU optimized layout of the distribution microgrid.

[0017] After continuous exploration and experimentation, the present invention constructs a power grid data acquisition model, a layout generation model, a layout update model, and a layout evaluation model, establishes an objective function with the goal of maximizing the comprehensive correlation information, and obtains the initial layout position information of μPMU; and based on the observability and the mutual information, updates the initial layout position information of μPMU to obtain the optimized layout position information of μPMU; then based on the state estimation accuracy of the distribution microgrid, determines the optimal layout configuration information of μPMU, and completes the μPMU optimized layout of the distribution microgrid, so that the installation quantity and installation location of μPMU can be comprehensively considered, thereby effectively reducing the number of μPMU configurations, reducing the layout cost of μPMU, maximizing economic benefits, and facilitating the promotion and use of the μPMU configuration method; at the same time, the μPMU optimization configuration method that considers the similarity of node correlation information can effectively improve the state estimation accuracy of the active distribution network ADN.

[0018] As the preferred technical measures:

[0019] Through the pre-built grid data acquisition model, the grid structure information of the distribution microgrid is obtained as the graph model of the distribution microgrid The method is as follows:

[0020] Obtain information of nodes 1 to n in radial order in the power grid structure, as well as information of branches between adjacent nodes;

[0021] Construct a graph model based on the information of nodes 1 to n and the branch information between adjacent nodes Its expression is as follows:

[0022]

[0023] Where: v=(v1,v2,…,v n ), ε=(e1,e2,…,e n-1 ), v represents the node set in the distribution network model, where (v1, v2, …, v n ) represent the node numbers from 1 to n in the radial order in the power grid structure; ε represents the branch set in the distribution network model, that is, the branches between different adjacent nodes.

[0024] As the preferred technical measures:

[0025] Using the pre-built layout generation model, according to the distribution microgrid graph model An objective function with the goal of maximizing the comprehensive correlation information is established; and based on the objective function, the method for obtaining the initial layout position information of the μPMU is as follows:

[0026] According to the graphical model of the distribution microgrid Get the μPMU placement status of node i in the distribution network;

[0027] Based on the μPMU placement of node i in the distribution network, the observability calculation algorithm is used to calculate the observability of each node, and the mutual information coefficient calculation algorithm is used to calculate the maximum mutual information coefficient MIC between each node;

[0028] According to the μPMU placement, the observability of node i and the maximum mutual information coefficient MIC between nodes, an objective function with the goal of maximizing the comprehensive correlation information F is constructed, and its expression is as follows:

[0029]

[0030] I i =s i,1 +s i,2 +,…,+s i,j

[0031] Where: I i represents the maximum mutual information coefficient MIC of node i, δ i represents the μPMU placement of node i in the distribution network. When δ i =1, it means that μPMU is placed on the i-th node; when δi = 0, it means that the i-th node does not place μPMU; i is the observability of node i; s i,j Represents the maximum mutual information coefficient MIC between the variables of the i-th node and the j-th node;

[0032] The greedy search algorithm is used to solve the objective function, and the μPMU optimization layout scheme with the largest comprehensive correlation information is selected to obtain the initial layout position information of the μPMU.

[0033] As the preferred technical measures:

[0034] Using the observability calculation algorithm, the method for calculating the observability of a node is as follows:

[0035] Based on the μPMU placement of node i in the distribution network, the connection relationship between nodes is obtained;

[0036] According to the connection relationship between nodes, determine several adjacent elements α i,j , and its calculation formula is as follows:

[0037]

[0038] Where: α i,j =1, it means that the i-th node and the j-th node in the power grid structure are connected by a branch; i,j =0, it means that there is no branch connecting the i-th node and the j-th node in the power grid structure;

[0039] Based on several adjacent elements α i,j , establish the adjacency matrix A, which is used to characterize the network topology of the distribution network;

[0040] The rows and columns of the adjacency matrix A represent the node numbers. By converting the 0-1 variables in the matrix elements, the direct mapping of the topological operation structure of the distribution network is completed.

[0041] The adjacency matrix A in the distribution network is used to calculate the observability of the node. The calculation formula is as follows:

[0042] O i =α i,1 +α i,2 +,…,+α i,n

[0043] Where: O i is the observability of node i.

[0044] As the preferred technical measures:

[0045] Using the mutual information coefficient calculation algorithm, the method for calculating the maximum mutual information coefficient MIC between each node is as follows:

[0046] Obtain at least two nodes and corresponding variables, which at least include a variable X of a first node and a variable Y of a second node;

[0047] The mutuality I(X;Y) between variables X and Y is calculated as follows:

[0048]

[0049] Where: p(x) and p(y) are the marginal distribution probabilities of variables X and Y respectively, and p(x,y) is the joint distribution probability of variables X and Y;

[0050] Then, the normalization factor is used to convert the value of the mutuality I(X; Y) into the interval (0, 1) to obtain the new mutuality I(X; Y);

[0051] According to the new mutuality I(X;Y), the maximum mutual information coefficient MIC of variable X and variable Y is calculated, and the calculation formula is as follows:

[0052] B = f(data_size) = n 0.6

[0053] Where: B is the resolution of the grid, ln(min(X,Y)) is the natural logarithm of the minimum value of variables X and Y; Among them U i and θ i are the voltage amplitude and voltage phase angle of node i respectively;

[0054] According to the maximum mutual information coefficient MIC, the maximum mutual information matrix S is constructed to represent the maximum mutual information coefficient MIC between each node in the distribution network; since the correlation between two fixed nodes remains consistent, the maximum mutual information matrix S is a symmetric matrix, and its expression is as follows:

[0055]

[0056] Where: s i,j Represents the maximum mutual information coefficient MIC between the i-th node and the j-th node variables.

[0057] As the preferred technical measures:

[0058] The pre-built layout update model is used to calculate the observability and mutual information of the nodes in the μPMU initial layout position information, and based on the observability and mutual information, the μPMU initial layout position information is updated to obtain the μPMU optimized layout position information as follows:

[0059] Based on the initial layout position information of μPMU, the observability calculation algorithm is adopted, and the observability of each node is calculated based on the iterative optimization algorithm;

[0060] In each iterative optimization, after a configuration node is determined, this node and the branches connected to it are deleted in the power network, and then the adjacency matrix A is recalculated and the observability analysis is re-performed;

[0061] At the same time, determine whether there are multiple nodes with the maximum observable degree; if so, use the mutual information coefficient calculation algorithm to calculate the mutual information of the nodes, build a mutual information matrix, then obtain the mutual information of the nodes with the same maximum observable degree, and select the node with the maximum mutual information for configuration; if not, configure it at the node with the maximum observable degree to update the initial layout position information of μPMU and obtain the optimized layout position information of μPMU.

[0062] As the preferred technical measures:

[0063] Using the pre-built layout evaluation model, based on the μPMU optimized layout location information, the state estimation accuracy of the distribution microgrid at this time is calculated, and based on the state estimation accuracy of the distribution microgrid, the method for determining the μPMU optimal layout configuration information is as follows:

[0064] Obtaining estimated values ​​of voltage amplitude and phase angle under ideal conditions, as well as error setting values ​​of maximum voltage amplitude and phase angle;

[0065] Based on the μPMU optimized layout position information, the analog calculation values ​​of voltage amplitude and phase angle are obtained;

[0066] According to the simulated calculated values ​​of the voltage amplitude and phase angle, and the estimated values ​​of the voltage amplitude and phase angle under ideal conditions, the state estimation accuracy J is calculated, and its calculation formula is as follows:

[0067]

[0068] Construct the state estimation accuracy constraint, whose expression is as follows:

[0069]

[0070] Where: U i ,θ i are the simulated calculated values ​​of voltage amplitude and phase angle respectively, are the estimated values ​​of voltage amplitude and phase angle under ideal conditions, are the error setting values ​​of the maximum voltage amplitude and phase angle respectively;

[0071] In order to meet the requirements for the accuracy of power network state estimation, the number of μPMUs is constrained within the range of 1%n to 30%n, which can be expressed as follows:

[0072] N set =|K in |1%n≤|K in |≤30%n

[0073] Where: N set is the number of μPMU placements, |K in | is the limit on the number of μPMUs placed in the distribution network, and n is the number of distribution network nodes;

[0074] According to the state estimation accuracy constraint and μPMU quantity constraint, determine whether the μPMU optimized layout position information meets the constraint; that is, determine whether the simulated calculated values ​​of the voltage amplitude and phase angle of the μPMU optimized layout position information meet the maximum voltage amplitude and phase angle error. set Cannot be greater than the limit on the number of μPMUs placed in the distribution network |K in |;

[0075] When the state estimation accuracy constraint and μPMU quantity constraint of μPMU optimization layout position information are satisfied, the μPMU placement number N is output. set , at this time, it is the optimal μPMU layout configuration, and the μPMU optimal layout configuration information is obtained;

[0076] When one of the state estimation accuracy constraint and the μPMU quantity constraint of the μPMU optimization layout position information is not satisfied, the layout generation model is reused to obtain new μPMU initial layout position information.

[0077] To achieve one of the above purposes, the second technical solution of the present invention is:

[0078] A μPMU optimization layout system for a distribution microgrid includes a power grid data acquisition module, a layout generation module, a layout update module and a layout evaluation module:

[0079] The grid data acquisition module is used to obtain the grid structure information of the distribution microgrid and serve as a graph module for the distribution microgrid.

[0080] Layout generation module, used to generate the microgrid diagram Establish an objective function with the maximum comprehensive correlation information as the goal; and based on the objective function, obtain the initial layout position information of μPMU; the maximum comprehensive correlation information is the maximum observability of the node where μPMU is placed and the maximum mutual information of the node;

[0081] A layout update module is used to calculate the observability and mutual information of the nodes in the μPMU initial layout position information, and based on the observability and mutual information, update the μPMU initial layout position information to obtain the μPMU optimized layout position information;

[0082] The layout evaluation module is used to calculate the state estimation accuracy of the distribution microgrid based on the μPMU optimization layout position information, and determine the μPMU optimal layout configuration information based on the state estimation accuracy of the distribution microgrid to complete the μPMU optimization layout of the distribution microgrid.

[0083] To achieve one of the above purposes, the third technical solution of the present invention is:

[0084] An electronic device comprising:

[0085] one or more processors;

[0086] A storage device for storing one or more programs;

[0087] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned μPMU optimization layout method for a distribution microgrid.

[0088] To achieve one of the above purposes, the fourth technical solution of the present invention is:

[0089] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned μPMU optimization layout method for a distribution microgrid.

[0090] Compared with the prior art solutions, the present invention has the following beneficial effects:

[0091] After continuous exploration and experimentation, the present invention constructs a power grid data acquisition model, a layout generation model, a layout update model, and a layout evaluation model, establishes an objective function with the goal of maximizing the comprehensive correlation information, and obtains the initial layout position information of μPMU; and based on the observability and the mutual information, updates the initial layout position information of μPMU to obtain the optimized layout position information of μPMU; then based on the state estimation accuracy of the distribution microgrid, determines the optimal layout configuration information of μPMU, and completes the μPMU optimized layout of the distribution microgrid, so that the installation quantity and installation location of μPMU can be comprehensively considered, the economic benefit can be maximized, and the promotion and use of the μPMU configuration method can be achieved; at the same time, the μPMU optimization configuration method that considers the similarity of node correlation information can effectively improve the state estimation accuracy of the active distribution network ADN.

[0092] Furthermore, the present invention first analyzes the topological structure of the current distribution microgrid, and then takes the maximum degree of comprehensive node association information as the objective function, takes the number of μPMU installations and the state estimation accuracy error as constraints, and adopts an optimization algorithm based on greedy search to solve the problem, thereby obtaining the optimal layout solution, which can effectively reduce the number of μPMU configurations, reduce the μPMU layout cost, and improve the state estimation accuracy of the distribution microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] Figure 1 A power network and an adjacency matrix for the present invention operating under different topological structures;

[0094] Figure 2 A process for optimal μPMU configuration of the present invention;

[0095] Figure 3 An IEEE-33 node distribution microgrid system modified by the present invention;

[0096] Figure 4 A modified IEEE-33 node distribution microgrid load curve used for simulation in the present invention;

[0097] Figure 5 It is the real voltage amplitude of the present invention and a voltage estimation amplitude measured without μPMU;

[0098] Figure 6 It is a voltage estimation phase angle between the real voltage phase angle of the present invention and the voltage phase angle measured without μPMU;

[0099] Figure 7 It is an IEEE-33 node voltage phase angle thermal diagram modified by the present invention;

[0100] Figure 8 is a state estimation error under different μPMU configuration numbers of the present invention;

[0101] Fig. 9 The invention discloses an IEEE-33 node including a μPMU configuration layout after modification. DETAILED DESCRIPTION

[0102] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0103] On the contrary, the present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention as defined by the claims. Further, in order to make the public have a better understanding of the present invention, some specific details are described in detail in the detailed description of the present invention below. Those skilled in the art can fully understand the present invention without the description of these details.

[0104] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention pertains. The terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "or / and" as used herein includes any and all combinations of one or more of the associated listed items.

[0105] The first specific embodiment of the μPMU optimization layout method of the microgrid of the present invention is as follows:

[0106] A μPMU optimization layout method for a distribution microgrid includes the following steps:

[0107] Includes the following:

[0108] Through the pre-built grid data acquisition model, the grid structure information of the distribution microgrid is obtained as the graph model of the distribution microgrid

[0109] Using the pre-built layout generation model, according to the distribution microgrid graph model Establish an objective function with the maximum comprehensive correlation information as the goal; and based on the objective function, obtain the initial layout position information of μPMU; the maximum comprehensive correlation information is the maximum observability of the node where μPMU is placed and the maximum mutual information of the node;

[0110] The pre-built layout update model is used to calculate the observability and mutual information of the nodes in the μPMU initial layout position information, and based on the observability and mutual information, the μPMU initial layout position information is updated to obtain the μPMU optimized layout position information;

[0111] Using the pre-built layout evaluation model, based on the μPMU optimized layout location information, the state estimation accuracy of the distribution microgrid at this time is calculated. Based on the state estimation accuracy of the distribution microgrid, the μPMU optimal layout configuration information is determined to complete the μPMU optimized layout of the distribution microgrid.

[0112] The second specific embodiment of the μPMU optimization layout method of the microgrid of the present invention:

[0113] A μPMU optimization layout method for distribution microgrid is a μPMU optimization configuration method considering the similarity of node association information, which can effectively improve the state estimation accuracy of active distribution network ADN, and includes the following contents:

[0114] For distribution networks, the general To represent its graph model:

[0115]

[0116] Where: v=(v1,v2,…,v n ), ε=(e1,e2,…,e n-1 ), v represents the node set in the distribution network model, where (v1, v2, …, v n ) represent the node numbers 1 to n in the grid structure in radial order; ε represents the branch set in the distribution network model, that is, the branches between different adjacent nodes. At the same time, the connection relationship between nodes can be represented by the adjacency matrix A. A also represents the network topology of the distribution network. The element α in the adjacency matrix A is i,j The definition is as follows:

[0117]

[0118] Where: α i,j =1, it means that the i-th node and the j-th node in the power grid structure are connected by a branch; i,j When =0, it means that there is no branch connecting the i-th node and the j-th node in the power grid structure.

[0119] In this embodiment: Figure 1 The topological structure of a power network with 5 nodes under different operating conditions and its corresponding adjacency matrix A are shown. The rows and columns of the adjacency matrix A represent the node numbers. It can be analyzed from the corresponding matrix that the direct mapping of the topological operation structure of the distribution network can be completed by converting the 0-1 variables in the matrix elements.

[0120] Generally speaking, when the voltage amplitude and phase angle of a node in a distribution network are measurable or can be obtained through measurement or calculation, the node is considered observable. When all nodes in the distribution network can obtain these values, the power network is said to be globally observable.

[0121] At present, topological observability analysis and numerical observability analysis are generally used to evaluate the observability of distribution networks. Topological observability quantifies the observability of the network by combining graph theory. First, the topological structure of the power network is analyzed, and then the characteristics of the μPMU measurement equipment are combined to finally obtain the observability of the power network. According to the measured Jacobian matrix, the numerical observability analysis is performed. Based on the matrix observability analysis, when the dimension of the measured data is greater than or equal to the dimension of the state variable, the matrix is ​​full rank, and the power network is observable.

[0122] μPMU can measure the voltage amplitude and phase angle of the installed node and adjacent nodes. Combining Kirchhoff's law and Ohm's law, its observability analysis rules can be obtained:

[0123] 1) When a node in the distribution network is equipped with a μPMU, its adjacent nodes can also be observed;

[0124] 2) If there is only one unknown node among the adjacent nodes of the zero-injected node, but the zero-injected node is observable, then the unknown node is observable;

[0125] 3) If the adjacent node sets of the zero-injected node are all observable, then the zero-injected node is observable.

[0126] According to the above rules of μPMU observability analysis, in order to increase the number of observable nodes in the active distribution network ADN and achieve global observability, the present invention proposes a method that considers the similarity of data association information to optimize the μPMU layout, and uses a greedy search algorithm to solve it, which includes the following contents:

[0127] First, it is necessary to calculate the observability of each node in the distribution network, evaluate the μPMU access nodes to be configured in each optimization layout, and select the node with the largest observability for μPMU configuration. The adjacency matrix A in the distribution network can be used to calculate the observability of the node. In each iterative optimization, after a configuration node is determined, this node and the branch connected to the node are deleted in the power network, and then the adjacency matrix A is recalculated and the observability analysis is performed again. The analysis formula is as follows:

[0128] O i =α i,1 +α i,2 +,…,+α i,n (3)

[0129] Where: O i is the observability of node i.

[0130] When there are sets of nodes with the same observability, these nodes need to be further quantified to determine the configuration order of their μPMUs. In order to further quantify the degree of correlation between data of different nodes, the present invention introduces the maximum mutual information coefficient (MIC), which was first proposed by David N. Reshef et al. in Science. It is a statistic used to measure the correlation between two variables. It expands the concepts of traditional mutual information (MI) and conditional mutual information (CMI) and has a wider range of applications and fields. MIC can capture a wide range of functional and non-functional associations, and has universality and fairness. Universality means that with sufficient sample capacity, MIC can obtain a wide range of related relationships, rather than being limited to specific functional types (such as linear, exponential or periodic), and even including all functional relationships; fairness means that MIC can give similar scores for different types of relationships containing equal noise, which includes the following:

[0131] First, let's introduce the meaning of MI. MI is a common method for computational linguistics model analysis. It measures the mutuality between two variables (X and Y). Its definition is similar to cross entropy. According to the definition of entropy, we can get the MI of variables X and Y, denoted as I(X; Y), as shown in the following formula:

[0132]

[0133] Where: p(x) and p(y) are the marginal distribution probabilities of variables X and Y respectively, and p(x,y) is the joint distribution probability of variables X and Y.

[0134] When a set of data points with two attributes is distributed in a two-dimensional space, the data space is divided into m×n grids, and the frequency of the data falling in the (x, y)th grid is recorded as P(x, y), as follows:

[0135]

[0136] Where: N i (x,y) is the number of data points in the (x,y)th grid, and N is the total number of data points.

[0137] Because there is more than one way to divide data points into an n×n grid, we need to divide the grid to maximize the MI. Then use the normalization factor to convert the MI value into the (0,1) interval. Finally, calculate the MIC of variables X and Y. The specific calculation process is as follows:

[0138]

[0139] Where: B is the resolution of the grid, ln(min(X,Y)) is the natural logarithm of the minimum value of variables X and Y. Among them U i and θ i are the voltage amplitude and voltage phase angle of node i respectively.

[0140] This patent introduces the maximum mutual information matrix S to represent the MIC between each node in the distribution network. Because the degree of association between two fixed nodes remains consistent, S is a symmetric matrix, as shown below:

[0141]

[0142] Where: s i,j To represent the MIC between the variables of the i-th node and the j-th node, the present invention selects the voltage phase angle as the variable.

[0143] Active distribution networks can achieve situational awareness and optimized management of power grid operation status through data analysis, and can solve the output uncertainty problem caused by large-scale multi-heterogeneous resources accessing the distribution network. In order to ensure that staff can better dispatch and operate the distribution network, it is necessary to study the state estimation accuracy and layout cost of the active distribution network. Therefore, the present invention proposes a μPMU optimization configuration method considering the similarity of node association information of distribution microgrids.

[0144] The present invention first analyzes the topological structure of the current distribution microgrid, then takes the maximum degree of integrated node association information as the objective function, establishes a μPMU optimization configuration model with the number of μPMU installations and the state estimation accuracy error as constraints, and finally solves the model using an optimization algorithm based on greedy search to obtain the optimal layout scheme, which can effectively reduce the number of μPMU configurations, reduce layout costs, and improve the state estimation accuracy of the distribution microgrid.

[0145] The third specific embodiment of the μPMU optimization layout method of the microgrid of the present invention:

[0146] A μPMU optimization layout method for a distribution microgrid includes the following contents:

[0147] 1. Objective function: The goal is to maximize the comprehensive association information of the node. The formula is as follows:

[0148]

[0149] I i =s i,1 +s i,2 +,…,+s i,j (9)

[0150] Where: I i represents the MIC of node i, δ i represents the μPMU placement of node i in the distribution network. When δ i =1, it means that μPMU is placed on the i-th node; when δ i = 0, it means that the i-th node does not place μPMU; i is the observability of node i, calculated by formula (3).

[0151] 2. Constraints: The state estimation accuracy and the number of μPMUs are constrained. The formula is as follows:

[0152] 1) State estimation accuracy constraints

[0153]

[0154] Where: U i ,θ i are the true values ​​of voltage amplitude and phase angle respectively, are the estimated values ​​of voltage amplitude and phase angle under the optimal solution, They are the error setting values ​​of the maximum voltage amplitude and phase angle respectively.

[0155] 2) μPMU quantity constraints

[0156] When the number of PMUs in the distribution network reaches one-third of the total number of nodes, the global visibility of the power network can be achieved. Since the measurement accuracy of μPMU is higher than that of PMU, setting the number of μPMUs within the range of 1%n to 30%n can meet the requirements for the accuracy of power network state estimation, which can be expressed as follows:

[0157] N set =|K in | 1%n≤|K in |≤30%n (11)

[0158] Where: N set is the number of μPMU placements, |K in | is the limit on the number of μPMUs placed in the distribution network, and n is the number of nodes in the distribution network.

[0159] 3. Solution process

[0160] This patent uses a greedy search algorithm to optimize the μPMU layout in the distribution microgrid and select the μPMU optimization layout scheme with the largest comprehensive correlation information. The specific steps are as follows;

[0161] 1) First, input the grid structure of the distribution microgrid, that is, the graphical model of the distribution microgrid Set the initial number of μPMUs Nset is 1.

[0162] 2) Set the objective function (8) as the fitness function.

[0163] 3) First, use formula (3) to calculate the observability of nodes in the distribution microgrid, form the maximum observability set, and determine whether there are nodes with the same observability. If so, use formulas (6) and (7) to calculate the mutual information of such nodes and construct the matrix S at the same time, and select the node with the maximum mutual information for configuration. If not, configure it at the node with the maximum observability.

[0164] 4) Calculate the state estimation accuracy of the μPMU installation position obtained in step 3), that is, whether the maximum voltage amplitude and phase angle error are met, and N set Cannot be greater than |K in If not satisfied, according to the observability analysis of μPMU, delete the node where μPMU is placed and its adjacent nodes and return to step 3) to continue iteration.

[0165] 5) When the state estimation accuracy of the μPMU layout after an iterative optimization satisfies the constraint, output N set This is the optimal μPMU layout configuration. For the optimal μPMU configuration process, see Figure 2 .

[0166] The present invention uses the modified IEEE-33 node example for simulation analysis, considering the unchanged topological structure of the distribution microgrid. The modified IEEE-33 node distribution microgrid system can be found in Figure 3 shown.

[0167] In the microgrid system of this example, each node model is a comprehensive load model, that is, the constant current load is 30%, the constant power load is 50%, and the constant impedance load is 20%. In the example, the microgrid has distributed units such as photovoltaics and wind turbines. The specific configuration is shown in Table 2.

[0168] Table 2 Configuration and access status of IEEE-33 node system after transformation

[0169] Microgrid access node location 18 21 33 Load active power / kW 90 90 60 Transmission capacity of tie line / kW 100 80 80

[0170] In the calculation example used in this invention, the load curves of all nodes are scaled and processed, and the real load curve of a certain urban area in my country is used. According to the load proportion of each node in the IEEE-33 node standard system distribution network, different scaling factors are set for each node P. For specific load curves, see Figure 4 .

[0171] First, the load curve of a certain sampling point is used to calculate the power flow. Then, the voltage amplitude and voltage phase angle of each node in the distribution microgrid are intercepted after the calculation results are obtained, and compared with the estimated values ​​without μPMU measurement. The results are as follows: Figure 5 , Figure 6 shown.

[0172] It can be concluded from the figure that when the distribution microgrid is not assisted by μPMU measurement, the errors in the read voltage amplitude and voltage phase angle will be large, which will have a great impact on the state estimation accuracy of the distribution microgrid.

[0173] The present invention sets the upper limit of the number of iterations of greedy search to 100 times. All are set to 1×10 -1 , the number of μPMUs is constrained to 1%n~30%n, n=33.

[0174] Combined with the μPMU measurement characteristics analyzed in the previous content, the present invention considers analyzing the correlation information degree between each node from the voltage phase angle of each node in the distribution microgrid system, and represents it with a heat map. According to equations (4)-(7), the MI matrix of the voltage phase angle at different nodes in the distribution network can be obtained, that is, the mutual information matrix S. The MI heat map of the IEEE-33 node distribution network test system is shown in Figure 7 shown. Figure 7 In this example, both the horizontal and vertical axes are the node numbers of the distribution network. The heat map visualizes the strength of association between nodes. The MI between nodes ranges from 0 to 1, and a lower MI value indicates a weaker correlation between two nodes. In particular, the MI between node 1 and other nodes in the figure is 0, which means that there is no correlation between node 1 and other nodes. This is because node 1 is a relaxed bus with a given voltage. At the same time, it can be observed that the MI on the diagonal is 1, because each node has an absolute correlation with itself. In addition, it can be seen that since the voltages of each node and its adjacent nodes are similar, the MI between the node and its adjacent nodes is very high.

[0175] The simulation results are given by Figure 8 As shown in the figure, when the number of μPMU in the system increases, the state estimation accuracy of the distribution microgrid will also increase accordingly. However, for the case of the IEEE-33 node distribution microgrid in the example of the present invention, when N set When the value reaches 8, the improvement of the state estimation accuracy of the distribution microgrid becomes smaller. Considering the cost of μPMU, 8 μPMUs are placed to realize the state estimation of the distribution microgrid. The specific placement positions are as follows: Fig. 9 shown.

[0176] Therefore, further, the present invention uses the modified IEEE-33 node system to perform case simulation, and the results show that after considering the node data association information, the number of μPMU configurations can be reduced, the layout cost can be reduced, and the state estimation accuracy of the distribution microgrid can be improved.

[0177] An embodiment of a device applying the method of the present invention:

[0178] An electronic device comprising:

[0179] one or more processors;

[0180] A storage device for storing one or more programs;

[0181] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned μPMU optimization layout method for a distribution microgrid.

[0182] A computer medium embodiment of the method of the present invention is applied:

[0183] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned μPMU optimization layout method for a distribution microgrid.

[0184] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, and computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0185] The present application is described by flowcharts or / and block diagrams of the methods, devices (systems), and computer program products of the embodiments of the present application. It should be understood that each process or / and block in the flowchart or / and block diagram and the combination of the processes or / and blocks in the flowchart or / and block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart or / and block diagram. Figure 1 Process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0186] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 Process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0187] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 Process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0188] The model in this application is an object that objectively describes the morphological structure with the help of physical or virtual representation. The object is not equal to the object and is not limited to physical and virtual. It can be a data processing function, software program, processing mode, usage method, operation method, workflow, application process, electronic hardware, circuit module, processing system, system imitation or simulation object.

[0189] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field can still modify or replace the specific implementation methods of the present invention with equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A μPMU optimization layout method for a microgrid, characterized by: Includes the following: Through the pre-built grid data acquisition model, the grid structure information of the distribution microgrid is obtained as the graph model of the distribution microgrid Using the pre-built layout generation model, according to the distribution microgrid graph model Establish an objective function with the goal of maximizing the comprehensive correlation information; and based on the objective function, obtain the initial layout position information of μPMU; The maximum comprehensive correlation information degree is the maximum observability of the node where the μPMU is placed and the maximum mutual information of the node; the initial layout position information of the μPMU includes at least the position information of the μPMU and the number of the μPMU; The pre-built layout update model is used to calculate the observability and mutual information of the nodes in the μPMU initial layout position information, and based on the observability and mutual information, the μPMU initial layout position information is updated to obtain the μPMU optimized layout position information; Using the pre-built layout evaluation model, based on the μPMU optimized layout location information, the state estimation accuracy of the distribution microgrid at this time is calculated. Based on the state estimation accuracy of the distribution microgrid, the μPMU optimal layout configuration information is determined to complete the μPMU optimized layout of the distribution microgrid.

2. The μPMU optimization layout method for a distribution microgrid according to claim 1, characterized in that: Through the pre-built grid data acquisition model, the grid structure information of the distribution microgrid is obtained as the graph model of the distribution microgrid The method is as follows: Obtain information of nodes 1 to n in radial order in the power grid structure, as well as information of branches between adjacent nodes; Construct a graph model based on the information of nodes 1 to n and the branch information between adjacent nodes Its expression is as follows: Where: v=(v1,v2,…,v n ), ε=(e1,e2,…,e n-1 ), v represents the node set in the distribution network model, where (v1, v2, …, v n ) represent the node numbers from 1 to n in the radial order in the power grid structure; ε represents the branch set in the distribution network model, that is, the branches between different adjacent nodes.

3. The μPMU optimization layout method for a distribution microgrid according to claim 1, characterized in that: Using the pre-built layout generation model, according to the distribution microgrid graph model An objective function with the goal of maximizing the comprehensive correlation information is established; and based on the objective function, the method for obtaining the initial layout position information of the μPMU is as follows: According to the graphical model of the distribution microgrid Get the μPMU placement status of node i in the distribution network; Based on the μPMU placement of node i in the distribution network, the observability calculation algorithm is used to calculate the observability of each node, and the mutual information coefficient calculation algorithm is used to calculate the maximum mutual information coefficient MIC between each node; According to the μPMU placement, the observability of node i and the maximum mutual information coefficient MIC between nodes, an objective function with the goal of maximizing the comprehensive correlation information F is constructed, and its expression is as follows: I i =s i,1 +s i,2 +,…,+s i,j Where: I i represents the maximum mutual information coefficient MIC of node i, δ i represents the μPMU placement of node i in the distribution network. When δ i =1, it means that μPMU is placed on the i-th node; when δ i = 0, it means that the i-th node does not place μPMU; i is the observability of node i; s i,j Represents the maximum mutual information coefficient MIC between the variables of the i-th node and the j-th node; The greedy search algorithm is used to solve the objective function, and the μPMU optimization layout scheme with the largest comprehensive correlation information is selected to obtain the initial layout position information of the μPMU.

4. The μPMU optimization layout method for a distribution microgrid as claimed in claim 3, characterized in that: Using the observability calculation algorithm, the method for calculating the observability of a node is as follows: Based on the μPMU placement of node i in the distribution network, the connection relationship between nodes is obtained; According to the connection relationship between nodes, determine several adjacent elements α i,j , and its calculation formula is as follows: Where: α i,j =1, it means that the i-th node and the j-th node in the power grid structure are connected by a branch; i,j =0, it means that there is no branch connecting the i-th node and the j-th node in the power grid structure; Based on several adjacent elements α i,j , establish the adjacency matrix A, which is used to characterize the network topology of the distribution network; The rows and columns of the adjacency matrix A represent the node numbers. By converting the 0-1 variables in the matrix elements, the direct mapping of the topological operation structure of the distribution network is completed. The adjacency matrix A in the distribution network is used to calculate the observability of the node. The calculation formula is as follows: O i ＝α i,1 +α i,2 +,…,+α i,n Where: O i is the observability of node i.

5. The μPMU optimization layout method for a distribution microgrid as claimed in claim 3, characterized in that: Using the mutual information coefficient calculation algorithm, the method for calculating the maximum mutual information coefficient MIC between each node is as follows: Obtain at least two nodes and corresponding variables, which at least include a variable X of a first node and a variable Y of a second node; The mutuality I(X;Y) between variables X and Y is calculated as follows: Where: p(x) and p(y) are the marginal distribution probabilities of variables X and Y respectively, and p(x,y) is the joint distribution probability of variables X and Y; Then, the normalization factor is used to convert the value of the mutuality I(X; Y) into the interval (0, 1) to obtain the new mutuality I(X; Y); According to the new mutuality I(X;Y), the maximum mutual information coefficient MIC of variable X and variable Y is calculated, and the calculation formula is as follows: Where: B is the resolution of the grid, ln(min(X,Y)) is the natural logarithm of the minimum value of variables X and Y; Among them U i and θ i are the voltage amplitude and voltage phase angle of node i respectively; According to the maximum mutual information coefficient MIC, the maximum mutual information matrix S is constructed to represent the maximum mutual information coefficient MIC between each node in the distribution network; since the correlation between two fixed nodes remains consistent, the maximum mutual information matrix S is a symmetric matrix, and its expression is as follows: Where: s i,j Represents the maximum mutual information coefficient MIC between the i-th node and the j-th node variables.

6. The μPMU optimization layout method for a distribution microgrid according to claim 1, characterized in that: The pre-built layout update model is used to calculate the observability and mutual information of the nodes in the μPMU initial layout position information, and based on the observability and mutual information, the μPMU initial layout position information is updated to obtain the μPMU optimized layout position information as follows: Based on the initial layout position information of μPMU, the observability calculation algorithm is adopted, and the observability of each node is calculated based on the iterative optimization algorithm; In each iterative optimization, after a configuration node is determined, this node and the branches connected to it are deleted in the power network, and then the adjacency matrix A is recalculated and the observability analysis is re-performed; At the same time, determine whether there are multiple nodes with the maximum observable degree; if so, use the mutual information coefficient calculation algorithm to calculate the mutual information of the nodes, build a mutual information matrix, and then obtain the mutual information of nodes with the same maximum observable degree, and select the node with the maximum mutual information for configuration; If not, it is configured at the node with the maximum observability to update the initial layout position information of the μPMU and obtain the optimized layout position information of the μPMU.

7. The μPMU optimization layout method for a distribution microgrid according to claim 6, characterized in that: Using the pre-built layout evaluation model, based on the μPMU optimized layout location information, the state estimation accuracy of the distribution microgrid at this time is calculated, and based on the state estimation accuracy of the distribution microgrid, the method for determining the μPMU optimal layout configuration information is as follows: Obtaining estimated values ​​of voltage amplitude and phase angle under ideal conditions, as well as error setting values ​​of maximum voltage amplitude and phase angle; Based on the μPMU optimized layout position information, the analog calculation values ​​of voltage amplitude and phase angle are obtained; According to the simulated calculated values ​​of the voltage amplitude and phase angle, and the estimated values ​​of the voltage amplitude and phase angle under ideal conditions, the state estimation accuracy J is calculated, and its calculation formula is as follows: Construct the state estimation accuracy constraint, whose expression is as follows: Where: U i ,θ i are the simulated calculated values ​​of voltage amplitude and phase angle respectively, are the estimated values ​​of voltage amplitude and phase angle under ideal conditions, are the error setting values ​​of the maximum voltage amplitude and phase angle respectively; In order to meet the requirements for the accuracy of power network state estimation, the number of μPMUs is constrained within the range of 1%n to 30%n, which can be expressed as follows: N set =|K in |1%n≤|K in |≤30%n Where: N set is the number of μPMU placements, |K in | is the limit on the number of μPMUs placed in the distribution network, and n is the number of distribution network nodes; According to the state estimation accuracy constraint and μPMU quantity constraint, determine whether the μPMU optimized layout position information meets the constraint; that is, determine whether the simulated calculated values ​​of the voltage amplitude and phase angle of the μPMU optimized layout position information meet the maximum voltage amplitude and phase angle error. set Cannot be greater than the limit on the number of μPMUs placed in the distribution network |K in |; When the state estimation accuracy constraint and μPMU quantity constraint of μPMU optimization layout position information are satisfied, the μPMU placement number N is output. set , at this time, it is the optimal μPMU layout configuration, and the μPMU optimal layout configuration information is obtained; When one of the state estimation accuracy constraint and the μPMU quantity constraint of the μPMU optimization layout position information is not satisfied, the layout generation model is reused to obtain new μPMU initial layout position information.

8. A μPMU optimization layout system for a microgrid, characterized by: It includes power grid data acquisition module, layout generation module, layout update module and layout evaluation module: The grid data acquisition module is used to obtain the grid structure information of the distribution microgrid and serve as a graph module for the distribution microgrid. Layout generation module, used to generate the microgrid diagram Establish an objective function with the goal of maximizing the comprehensive correlation information; and based on the objective function, obtain the initial layout position information of μPMU; The maximum comprehensive correlation information degree is the node where μPMU is placed with the largest observability and node mutual information; A layout update module is used to calculate the observability and mutual information of the nodes in the μPMU initial layout position information, and based on the observability and mutual information, update the μPMU initial layout position information to obtain the μPMU optimized layout position information; The layout evaluation module is used to calculate the state estimation accuracy of the distribution microgrid based on the μPMU optimization layout position information, and determine the μPMU optimal layout configuration information based on the state estimation accuracy of the distribution microgrid to complete the μPMU optimization layout of the distribution microgrid.

9. An electronic device, characterized in that: It includes: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the μPMU optimization layout method for a distribution microgrid as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, a μPMU optimization layout method for a distribution microgrid is implemented as described in any one of claims 1-8.

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