A power quality comprehensive evaluation method for distribution network based on complex network theory

Abstract

The application discloses a power quality comprehensive evaluation method of a power distribution network based on a complex network theory, which comprises the following steps: S1, drawing a directed network diagram of the power distribution network; S2, acquiring complex network indexes of each node in the directed network diagram of the power distribution network, and weighting the complex network indexes by using a CRITIC algorithm to calculate a node importance comprehensive decision value of each node in the power distribution network; S3, combining actual measurement values of power quality indexes of each node in the power distribution network with the node importance comprehensive decision value, calculating subjective weight values and objective weight values by using a G1 method and a CRITIC method respectively, obtaining a comprehensive weight value through subjective and objective comprehensive weight calculation, and performing two-stage weight optimization to obtain the most weight value; and S4, quantitatively evaluating the power quality of each node in the power distribution network by using a fuzzy comprehensive evaluation method according to the most weight value. The evaluation method can accurately evaluate the power quality of the complex network power distribution network and is convenient for improving the power quality.

Description

Technical Field

[0001] This invention relates to the field of power distribution network technology, and specifically to a comprehensive evaluation method for power quality of power distribution networks based on complex network theory. Background Technology

[0002] In recent years, with the development of new power systems, new distribution networks will connect to more and more distributed resources. The large number of distributed photovoltaic, distributed wind power and other distributed new energy sources connected to the new distribution networks will effectively increase the capacity and power generation of new energy sources and enhance the dominant position of new energy sources in the new power system.

[0003] Currently, with the integration of distributed power sources and the application of nonlinear loads, the power quality of new distribution networks is becoming increasingly complex and challenging. On the one hand, the increase in power quality-sensitive appliances is leading to higher demands for power quality from users; on the other hand, the integration of more distributed renewable energy sources is further complicating the network structure of the distribution network, and this complexity also has a certain impact on the power quality. Therefore, in order to ensure power supply quality and improve the user experience, it is necessary to improve the power quality of new distribution networks with a large number of distributed renewable energy sources.

[0004] However, research has found that traditional power quality assessment indicators generally only consider narrow power quality metrics, failing to reflect the importance characteristics of each node in a complex distribution network. This makes it impossible to differentiate nodes of different importance in the scoring. Therefore, there is an urgent need to develop a method for accurately assessing the power quality of new distribution networks with complex structures, in order to subsequently address power quality issues in these new networks. Summary of the Invention

[0005] The technical problem to be solved by this invention is to design a comprehensive power quality assessment method for distribution networks based on complex network theory, so as to solve the problem that the existing traditional power quality assessment indicators only consider narrow power quality indicators and cannot reflect the importance characteristics of each node in a complex distribution network.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a comprehensive evaluation method for power quality of distribution networks based on complex network theory, which includes the following steps:

[0007] S1: Draw the directed network diagram of the power distribution network;

[0008] S2: Obtain the complex network indicators of each node in the directed network graph of the distribution network, and use the CRITIC algorithm to assign weights to the complex network indicators in order to calculate the comprehensive decision value of the node importance of each node in the distribution network.

[0009] S3: Combine the actual measured values ​​of power quality indicators of each node in the distribution network with the comprehensive decision value of the node importance, calculate the subjective weight value and objective weight value using the G1 method and CRITIC method respectively, and then perform a comprehensive weight calculation to obtain the comprehensive weight value. Perform a two-stage weight optimization on the comprehensive weight value to obtain the final weight value.

[0010] S4: Based on the final weight values, the power quality of each node in the distribution network is quantitatively evaluated using the fuzzy comprehensive evaluation method.

[0011] In the above technical solution of the present invention, the comprehensive power quality assessment method for distribution networks based on complex network theory can first draw a simplified directed network diagram of the distribution network according to the actual structure and power flow direction of the distribution network; then, based on complex network theory, obtain the complex network indicators of each node in the directed network diagram of the distribution network, such as node degree centrality, node betweenness centrality, eigenvector centrality, etc., and construct the complex network indicator matrix of this distribution network according to the node number; then, obtain the comprehensive decision value of node importance of each node based on the CRITIC (indicator importance correlation method) algorithm; finally, combine the comprehensive decision value of node importance with the measured value of the selected power quality indicators as a data matrix, perform subjective and objective comprehensive weight calculation based on the G1 (order relation analysis method) and CRITIC method, and then perform two-stage weight optimization to obtain the final weight. Finally, the power quality of each node is quantitatively assessed by the fuzzy comprehensive evaluation method.

[0012] Furthermore, in the comprehensive power quality assessment method for distribution networks described in this invention, in step S1, the directed network diagram of the distribution network has multiple nodes, which are: power plants, distributed power sources, substations, converter stations, or load equipment.

[0013] In the above technical solution of the present invention, a directed network diagram of the distribution network can be drawn according to the topology and power flow direction of the distribution network. Furthermore, during the drawing process, multiple nodes are set according to the types of facilities in the distribution network. These nodes are generally facilities such as power plants, distributed power sources, substations, converter stations, and loads, which can obtain detailed power quality measurement data. In the directed network diagram constructed above, the connecting lines between nodes can be understood as transmission lines between nodes, and the direction of the connecting lines can be understood as the power flow direction on the corresponding transmission lines.

[0014] Furthermore, in the comprehensive power quality assessment method for distribution networks described in this invention, in step S2, the complex network indicators include: node degree centrality, node betweenness centrality, and eigenvector centrality.

[0015] Research has found that nodes in a complex power distribution network are distinguished by their different positions in the network and the nodes they are connected to. Therefore, considering the directional flow of power in the power distribution network, the power quality problems of a node will affect other connected nodes through the power connection lines. In complex network theory, node degree is used to describe the number of nodes directly connected to a certain node. Therefore, node degree centrality is selected as a complex network index.

[0016] In addition to nodes directly connected to each other, the position of a node in the distribution network of a complex network also directly reflects its importance. Therefore, the influence of a node in the whole network and the utilization of the node can be reflected by the shortest path algorithm and by examining the number of paths passing through a certain node. Thus, node betweenness centrality is selected as an indicator of complex networks.

[0017] Furthermore, the importance of a node in a distribution network depends not only on its own degree but also on the importance of its neighboring nodes. In a distribution network, the more edges a node receives, the higher its importance becomes, as this node will transmit its importance to its connected neighboring nodes through directed edges. Therefore, eigenvector centrality is chosen as an indicator for complex networks.

[0018] In summary, in the above technical solution of the present invention, the degree centrality, betweenness centrality, and eigenvector centrality of nodes are selected as complex network indicators for each node in the directed network graph of the distribution network. These indicators can all be calculated based on the directed network graph of the distribution network, thus ensuring the practicality of the complex network indicators.

[0019] Furthermore, in the comprehensive power quality assessment method for distribution networks described in this invention, in step S2, the degree centrality, betweenness centrality, and eigenvector centrality of each node in the directed network graph of the distribution network are obtained, specifically as follows:

[0020] The degree centrality of the nodes is:

[0021] in, For nodes The degree centrality of nodes is represented by the degree of nodes. The number of directly connected nodes; The out-degree of a node is represented by the node's out-degree. The number of links originating from this point; The in-degree of a node is represented by the degree of its nodes. The number of links leading to the destination;

[0022] The node betweenness centrality is:

[0023] Among them, nodes The node betweenness centrality is B. i ;δ jk For nodes To the node The number of paths under the shortest path algorithm; δ jk (i) is δ jk Passing through nodes The number of paths;

[0024] The centrality of the eigenvector is:

[0025]

[0026] in, For nodes eigenvector centrality; For nodes eigenvector centrality; For nodes out-degree; D i The directed edge points to the node in the directed network graph. The set of all nodes, where d is the damping coefficient.

[0027] Furthermore, in the comprehensive power quality assessment method for distribution networks described in this invention, in step S2, the CRITIC algorithm is used to assign weights to the complex network indicators to calculate the comprehensive decision value of the node importance of each node in the distribution network, specifically as follows:

[0028] S21: Based on the values ​​of complex network indicators of each node in the distribution network, establish a complex network indicator matrix;

[0029] S22: Perform initial data processing on the complex network index matrix to obtain a standardized matrix of complex network indicators;

[0030] S23: Calculate the coefficient of variation of the a-th indicator.

[0031] S24: Calculate the correlation coefficient r between the a'th indicator and the a”th indicator based on the standardized matrix of the complex network indicators. a'a” Next, the conflict quantification value A is calculated. a ;

[0032] S25: Based on the coefficient of variation And conflict quantification value A a The calculated information content E a ;

[0033] S26: Regarding the information quantity E a Normalization is performed to obtain the weight of the a-th indicator.

[0034] S27: Based on the calculations in S23-S26 above, the weight set of the node importance index is θ. * The formula for calculating the node importance decision value for each node is as follows:

[0035]

[0036] in, y represents the weight value of the a-th complex network metric; ia This represents the standardized data of the a-th indicator of the i-th node in the complex network indicator standardization matrix.

[0037] Furthermore, in the comprehensive power quality assessment method for distribution networks described in this invention, in step S3, the power quality indicators include: voltage deviation, voltage fluctuation, voltage sag, three-phase imbalance, harmonics, and frequency deviation.

[0038] Furthermore, in the comprehensive power quality assessment method for distribution networks described in this invention, in step S3, the actual measured values ​​of the six power quality indicators selected from each node in the distribution network are combined with the comprehensive decision value of the node importance, and a power quality data matrix X is established based on the above seven assessment indicators. m×l :

[0039]

[0040] Where i = (1,2,3,…,m), b = (1,2,3,…,7), m is the number of nodes in the distribution network, and l is the number of evaluation indicators, and l is 7.

[0041] Furthermore, in the comprehensive power quality assessment method for distribution networks described in this invention, in step S3:

[0042] Subjective weights are calculated using the G1 method, specifically by obtaining multiple evaluation indicators, including the power quality index and the comprehensive decision value of node importance; first, determining the order of the evaluation indicators; and then determining the relative importance ratio r among the evaluation indicators. k And finally based on the relative importance ratio r among the evaluation indicators. k The set of subjective weight values ​​for each node is calculated as w;

[0043] The objective weight calculation based on the CRITIC method is as follows: for the power quality data matrix X m×l Data processing is performed to obtain a standardized matrix of node power quality data; the coefficient of variation of the indicators is calculated; based on the standardized matrix, the correlation coefficient between the b'-th evaluation indicator and the b”-th evaluation indicator is calculated to obtain the conflict quantification value A. b According to the conflict quantification value Ab The information content is obtained by calculating the coefficient of variation of the indicators; the information content is then normalized to obtain the objective weight value θ of the b-th indicator. b .

[0044] The comprehensive weight calculation is as follows: The formula for calculating the subjective and objective comprehensive weight λ is:

[0045]

[0046] Where λ is the overall weight, w1~w l θ represents the subjective weights of the first to lth indicators calculated using the G1 method. b This represents the objective weight of the b-th indicator, calculated using the CRITIC method.

[0047] Furthermore, in the comprehensive power quality assessment method for distribution networks described in this invention, in step S3, the comprehensive weight value is subjected to two-stage weight optimization to obtain the final weight value, specifically as follows:

[0048] Using the comprehensive weight value λ as the initial weight, the expected weight value ω of the b-th evaluation index of the i-th node is calculated. b for:

[0049]

[0050] Based on the objectives of the two-stage method, the optimization model for the weights of the evaluation indicators is established using the least squares method as follows:

[0051]

[0052] ω b ≥0

[0053] Based on the above optimization model, W = [ω1, ω2, ..., ω] is obtained. l ], which is the final weight value of each evaluation indicator.

[0054] Furthermore, in the comprehensive power quality assessment method for distribution networks described in this invention, in step S4, the assessment indicators are first quantified to obtain a set of comments for power quality quantification and grading. Then, the membership degree of the judgment set corresponding to each node data is calculated through the membership function. Finally, a weighted average fuzzy comprehensive operator is used to perform fuzzy product operation on the judgment matrix and quantify the assessment results.

[0055] The beneficial effects of this invention are as follows: The inventors have optimized and designed a comprehensive power quality assessment method for distribution networks based on complex network theory. Compared with the power quality indicators selected in traditional methods, this method incorporates complex network indicators, namely, the comprehensive decision value of the importance of each node in the distribution network. Furthermore, the quantitative assessment score reflects differentiated scoring for nodes with different importance. Simultaneously, by employing a two-stage weight optimization based on a one-stage subjective and objective weighting process, the rationality of the final weight values ​​is improved. This avoids the mutual influence between subjective and objective weights during the one-stage weighting process, allowing for a more reasonable integration of subjective and objective weight values. This provides a scoring reference for subsequent quantitative assessment of the power quality of the distribution network, facilitating improvements in power supply quality and enhancing the user's electricity experience. It has good prospects for promotion and application value. Attached Figure Description

[0056] Figure 1 This is a flowchart illustrating the steps of one embodiment of the comprehensive power quality assessment method for power distribution networks described in this invention.

[0057] Figure 2 The diagram schematically illustrates the architecture of a distribution network under one embodiment of the comprehensive power quality assessment method for distribution networks described in this invention.

[0058] Figure 3 The diagram schematically illustrates a directed network diagram of a distribution network under one embodiment of the comprehensive power quality assessment method for distribution networks described in this invention.

[0059] Figure 4 The diagram schematically illustrates the power quality evaluation index set of the comprehensive power quality evaluation method for power distribution networks described in this invention under one implementation method.

[0060] Figure 5 The illustration schematically shows the application of the power quality comprehensive assessment method for distribution networks according to the present invention. Figure 3 The final calculated comprehensive decision value of the importance of the 23 nodes in the directed network diagram of the distribution network shown.

[0061] Figure 6 The illustration schematically demonstrates the difference between the comprehensive power quality assessment method for distribution networks using the present invention and traditional power quality assessment methods. Figure 3 The evaluation results of 23 nodes in the directed network diagram of the distribution network shown. Detailed Implementation

[0062] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0063] Currently, in response to the increasing complexity of new power system networks, traditional evaluation indicators only consider conventional power quality indicators and do not reflect the importance characteristics of each node in complex distribution networks. This is not conducive to subsequent improvement of the power quality of the distribution network. Therefore, this invention proposes a comprehensive evaluation method for power quality of distribution networks based on complex network theory.

[0064] See Figure 1 It can be seen that, Figure 1 In the embodiments shown, the comprehensive power quality assessment method for distribution networks designed in this invention specifically includes the following steps:

[0065] S1: Draw a directed network diagram of the distribution network based on its topology and power flow direction.

[0066] S2: Obtain the complex network indicators of each node in the directed network graph of the distribution network, and use the CRITIC algorithm to assign weights to the complex network indicators in order to calculate the comprehensive decision value of the node importance of each node in the distribution network.

[0067] S3: Combine the actual measured values ​​of power quality indicators of each node in the distribution network with the comprehensive decision value of the node importance, calculate the subjective weight value and objective weight value using the G1 method and CRITIC method respectively, and then perform a comprehensive weight calculation to obtain the comprehensive weight value. Perform a two-stage weight optimization on the comprehensive weight value to obtain the final weight value.

[0068] S4: Based on the final weight values, the power quality of each node in the distribution network is quantitatively evaluated using the fuzzy comprehensive evaluation method.

[0069] As can be seen from the above, the comprehensive power quality assessment method for distribution networks designed in this invention, in addition to the selected power quality indicators, also incorporates complex network indicators, namely, the comprehensive decision value of the importance of each node in the distribution network. Furthermore, the quantitative assessment score reflects differentiated scoring for nodes with different importance levels. Simultaneously, because a two-stage weight optimization is adopted based on the first-stage subjective and objective weighting, the rationality of the final weight value is improved. This avoids the mutual influence of subjective and objective weights during the first-stage weighting process, allowing for a more reasonable integration of subjective and objective weight values. This provides a scoring reference for subsequent quantitative assessment of the power quality of the distribution network.

[0070] like Figure 3 As shown in the present invention, the directed network diagram of the above-mentioned power distribution network has multiple nodes, which are generally power plants, distributed power sources, substations, converter stations and load equipment on the power distribution network.

[0071] See Figure 2 and Figure 3As shown, in step S1 of the above-mentioned comprehensive power quality assessment method for distribution networks, when drawing a directed network diagram of the distribution network based on its topology and power flow direction, the nodes in the directed network diagram can be specifically set according to the types of facilities in the distribution network; wherein, Figure 3 The connecting lines between the nodes shown are the power transmission lines between the two facilities. Figure 3 The direction between the nodes shown represents the power flow direction on the transmission line between the two corresponding facilities.

[0072] Accordingly, in this invention, when performing step S2 above, it is necessary to first calculate the complex network index values ​​of each node in the directed network diagram of the distribution network to obtain the complex network index matrix of all nodes in the network; then, through a weighting algorithm, the correlation between each complex network index is considered, and each complex network index is weighted; finally, the comprehensive decision value of the node importance of each node is calculated based on the complex network index values ​​of each node and the weight values ​​of each complex network index.

[0073] In this invention, node degree centrality, node betweenness centrality, and eigenvector centrality can be specifically selected as complex network indicators for each node in the directed network graph of the distribution network, so as to reflect the properties, functions, and relationships of each node in the distribution network. These indicators can all be calculated based on the directed network graph of the distribution network, ensuring the practicality of the complex network indicators.

[0074] In this invention, the following calculation method is specifically used to obtain the above three complex network indicators:

[0075] (1) Node degree centrality: [This refers to the degree centrality of nodes.] The degree centrality of a node is denoted as Its representation is related to nodes The number of directly connected nodes;

[0076]

[0077] in, The out-degree of a node is represented by the node's out-degree. The number of links originating from this point; The in-degree of a node is represented by the degree of its nodes. The number of links to the endpoint.

[0078] It should be noted that the aforementioned "link" refers to the connecting line in the directed network diagram of this power distribution network, i.e. Represented by node Starting from and connecting with that node The number of connecting wires; similarly, Represented by node As the endpoint, and with that node The number of connecting wires.

[0079] (2) Node Betweenness Centrality: Node betweenness centrality accurately characterizes the degree of influence of a node in a network and the utilization of the node. It defines a node... The node betweenness centrality is B. i ;

[0080]

[0081] Among them, nodes The node betweenness centrality is B. i ;δ jk For nodes To the node The number of paths under the shortest path algorithm; δ jk (i) is δ jk Passing through nodes The number of paths.

[0082] (3) Eigenvector centrality: Based on the PageRank algorithm, the nodes in the directed network graph can be calculated. eigenvector centrality Eigenvector centrality The formula for calculation is:

[0083]

[0084] in, For nodes eigenvector centrality; For nodes eigenvector centrality; For nodes out-degree; D i The directed edge points to the node in the directed network graph. The set of all nodes, where d is the damping coefficient, and the damping coefficient d is generally taken as 0.85.

[0085] Accordingly, further reading Figure 1 As can be seen, based on the obtained complex network index values ​​of each node, this invention also requires the use of the CRITIC algorithm to assign weights to the complex network indexes in order to calculate the comprehensive decision value of the node importance of each node in the distribution network. The use of the CRITIC method for node importance assessment reflects its objectivity. The CRITIC method can eliminate the influence between highly correlated indicators, reduce information overlap between indicators, and obtain more scientific and reliable evaluation results.

[0086] In practical applications, the following steps S21-S27 can be included:

[0087] S21: Based on the values ​​of multiple complex network indicators for each node in the distribution network, obtain the complex network indicator matrix for all nodes in the distribution network:

[0088] Let m power quality monitoring points form a sample set S = {S} i}(i=1,2,…,m), and a set of n complex network indicators G={g a Given a network of nodes (a = 1, 2, ..., n), the complex network index matrix Ψ for all nodes in the distribution network is... m×n for:

[0089]

[0090] in, Let be the value of the a-th complex network metric of the i-th node.

[0091] S22: Regarding the aforementioned complex network indicator matrix Ψ m×n Initial data processing is performed to obtain the standardized matrix Y = (y ia ) m×n :

[0092]

[0093] Where: ψ ia Let $\mathbf{a}$ be the complex network index value of the $i$-th node object, $i = 1, ..., m$, $a = 1, 2, 3$, $m$ is the total number of nodes, and $maxψ$ is the complex network index value of the $i$-th node object. a ,minψ a y represents the maximum and minimum values ​​of the feature data at different nodes under the a-th indicator. ia The standardized data value is the processed value of the a-th complex network index of the i-th node.

[0094] S23: Calculate the coefficient of variation of complex network metrics. The coefficient of variation is a quantitative representation of the comparative strength of metrics in complex networks. The calculation formula is:

[0095]

[0096] Among them, s a Let be the standard deviation of the 'a'-th complex network metric. is the average value of the a-th complex network metric.

[0097] S24: Based on the standardized matrix Y = (y... ia ) m×n The correlation coefficient r between the a'th complex network index and the a”th complex network index was calculated. a'a” Next, the conflict quantification value A is calculated.a :

[0098]

[0099] in, The standardized matrix Y = (y ia ) m×n The covariance between column a' and column a”.

[0100] S25: Based on the coefficient of variation And conflict quantification value A a The calculated information content E a :

[0101]

[0102] S26: Regarding the information quantity E a Normalization is performed to obtain the weight of the a-th indicator.

[0103]

[0104] S27: Based on the calculations in S23-S26 above, the weight set of the node importance index is θ. * The formula for calculating the node importance decision value for each node is as follows:

[0105]

[0106] Where, θ a y represents the weight value of the a-th complex network metric; ia This represents the standardized data of the a-th indicator of the i-th node in the complex network indicator standardization matrix.

[0107] Accordingly, further reading Figure 1 As can be seen, in step S3 of the present invention, it is necessary to combine the actual measured values ​​of the power quality indicators of each node in the distribution network with the comprehensive decision value of node importance calculated in step S27 above to establish a power quality data matrix.

[0108] For example, in some embodiments, specific power quality indicators can be selected as: voltage deviation, voltage fluctuation, voltage sag, three-phase imbalance, harmonics, and frequency deviation, combined with the node importance comprehensive decision value, resulting in a total of 7 power quality assessment indicators, which constitute the power quality data matrix X. m×l :

[0109]

[0110] Where i = (1,2,3,…,m), b = (1,2,3,…,7), m is the number of nodes in the distribution network, and l is the number of evaluation indicators, and l is 7.

[0111] At the same time, such as Figure 1 As shown, in step S3, the power quality data matrix X is constructed. m×l Then, the G1 method and CRITIC method can be used to perform subjective and objective weighting, and the comprehensive weight value can be obtained by combining subjective and objective weights.

[0112] The G1 method is a method that determines the weight of each evaluation indicator based on the researcher's subjective experience and the degree of importance of each indicator. Researchers can rank and quantify the importance of indicators based on the characteristics of active distribution networks and their knowledge and experience. Specifically, the G1 method is used for subjective weighting to obtain the subjective weights of the evaluation indicators, including:

[0113] (1) Determine the order relationship of evaluation indicators: Let the set of evaluation factors consist of l evaluation indicators, T = {T1, T2, ..., T...} l If the evaluation index T l' The importance of T is greater than T l” Then it is denoted as T. l' >T l” The evaluation indicators are rearranged in descending order of importance to determine their ordinal relationship. Where T l * This represents the l-th evaluation indicator after being sorted by importance.

[0114] (2) Determine the relative importance ratio among evaluation indicators: Determine adjacent evaluation indicators and Importance than r k :

[0115]

[0116] in: and These represent the evaluation indicators. and The weighting coefficient, r k The value of r is determined as follows: k =1.0 indicates the evaluation index and The importance is the same, r k =1.2 indicates the evaluation index and Slightly more important, r k =1.4 indicates the evaluation index and Clearly important, rk =1.6 indicates the evaluation index and Strongly important, r k =1.8 indicates the evaluation index and Extremely important, and r k The values ​​1.1, 1.3, 1.5, and 1.7 represent the intermediate values ​​of the above adjacent judgments.

[0117] (3) Calculate the subjective weights of the evaluation indicators: based on the determined relative importance ratios r among the evaluation indicators. k The subjective weight value of the l-th evaluation indicator is calculated using the following formula:

[0118]

[0119] The subjective weight value of the (k-1)th evaluation indicator is:

[0120]

[0121] The CRITIC method is used to assign weights to each evaluation indicator in the data matrix. The CRITIC method can eliminate the influence between highly correlated evaluation indicators, reduce information overlap between indicators, and obtain more scientific and reliable evaluation results. The process of objectively assigning weights to the evaluation indicators using CRITIC includes:

[0122] (1) Data standardization: for power quality data matrix X m×l Data processing is performed to obtain the standardized matrix Y = (y ib ) m×l The specific data processing methods are as follows:

[0123]

[0124] Among them, in the power quality data matrix X m×l x in ib Let x be the b-th complex network metric value of the i-th node object, i = 1, ..., m, b = 1, 2, ..., 7, where m is the number of nodes, and max(x) = ... b ), min(x b ) represent the maximum and minimum values ​​of feature data of different nodes under the same complex network index, y ib The standardized data value after processing the b-th power quality index value of the i-th evaluation object.

[0125] (2) Calculate the coefficient of variation of the evaluation indicators: The coefficient of variation is a quantitative representation of the comparative strength of the indicators. b The calculation formula is:

[0126]

[0127] Among them, s b Let b be the standard deviation of the evaluation indicator. This represents the average value of the b-th evaluation indicator.

[0128] (3) Calculation of conflict of evaluation indicators: Based on the standardized matrix Y = (y ib ) m×l Calculate the correlation coefficient r between the b'th evaluation indicator and the b”th evaluation indicator. b'b” Next, the conflict quantification value A is calculated. b :

[0129]

[0130]

[0131] in: Let be the covariance between the b'th and b”th columns of the standardized matrix.

[0132] (4) Information content calculation: The information content E is calculated using the following formula. b :

[0133] E b =Vσ b ×A b

[0134] (5) Calculation of objective weights for evaluation indicators: Normalize the information content to obtain the weight θ of the b-th evaluation indicator. b for:

[0135]

[0136] To avoid the problems of over-reliance on expert opinions caused by a single subjective weighting method and over-reliance on quantitative analysis of sample data and neglect of subjective qualitative analysis caused by a single objective weighting method, a combined weighting method that combines subjective weighting (G1 method) and objective weighting (CRITIC method) is adopted to calculate the comprehensive weight λ. The formula for calculating the comprehensive subjective and objective weight value λ is as follows:

[0137]

[0138] Where λ is the overall weight, w1~w l θ represents the subjective weights of the first to lth indicators calculated using the G1 method. b This represents the objective weight of the b-th indicator, calculated using the CRITIC method.

[0139] Based on the comprehensive weight value set of the corresponding evaluation indicators obtained in the first stage through subjective weighting method, objective weighting method and comprehensive subjective and objective weighting method, the comprehensive weight value is finally optimized by the second stage weighting method to calculate the final weight value.

[0140] For example, using the comprehensive weight value λ obtained by the subjective and objective weighting method as the initial weight, the expected weight value of the b-th evaluation indicator of the i-th node is calculated. for:

[0141]

[0142] Evaluation object node S i The comprehensive decision value f i The result obtained by weighted calculation is:

[0143] Based on the purpose of the two-stage method, let there exist weights ω for the final evaluation index. b (b = 1, 2, ..., l) can make the comprehensive decision value of all samples under the final weights Combined decision value f of all nodes i sum The optimization model for the evaluation index weights, which minimizes the difference, is established using the least squares method as follows:

[0144]

[0145] ω b ≥0

[0146] Based on the above optimization model, W = [ω1, ω2, ..., ω] is obtained. l ], which is the final weight value of each evaluation indicator.

[0147] It should be noted that in this invention, when performing the optimization solution, the corresponding code is written in Matlab based on the objective function and constraints of the optimization model, and the result is obtained by directly running the program. Therefore, there is no need to elaborate on the calculation of the optimization model here.

[0148] Accordingly, further reading Figure 1 As can be seen, in step S4 of the present invention, it is necessary to first quantify the power quality evaluation index to obtain a set of comments for power quality quantification and grading, then calculate the membership degree of the evaluation set corresponding to each node data through the membership function, and finally use the weighted average fuzzy comprehensive operator to perform fuzzy product operation on the final weight value and the evaluation matrix, and quantify the evaluation result.

[0149] S41. Quantification of Power Quality Evaluation Indicators:

[0150] To perform fuzzy comprehensive evaluation, the factor set and the evaluation set are first determined. The factor set T, which is composed of the indicators confirmed in step S3, is: {T1 (voltage deviation), T2 (voltage fluctuation), T3 (voltage sag), T4 (three-phase imbalance), T5 (harmonics), T6 (frequency deviation), T7 (node ​​importance comprehensive decision value)}.

[0151] Power quality is divided into 5 different levels, forming a 5-level fuzzy evaluation set V: {V1 (excellent power quality), V2 (good power quality), V3 (average power quality), V4 (poor power quality), V5 (bad power quality)}.

[0152] By quantifying the evaluation indicators using fuzzy evaluation sets, the correspondence between the quantitative classification and scoring range of the power quality level of the distribution network is obtained as follows: Level V1 indicates excellent power quality, corresponding to a scoring range of (85, 100], with a quantitative score of 95; Level V2 indicates good power quality, corresponding to a scoring range of (75, 85], with a quantitative score of 80; Level V3 indicates average power quality, corresponding to a scoring range of (60, 75], with a quantitative score of 70; Level V4 indicates poor power quality, corresponding to a scoring range of (50, 60], with a quantitative score of 55; Level V5 indicates poor power quality, corresponding to a scoring range of (0, 50], with a quantitative score of 40.

[0153] Therefore, the comment set V is:

[0154] V = [Excellent, Good, Average, Poor, Poor]

[0155] =[95 80 70 55 40].

[0156] S42. Quantification of Power Quality Evaluation Indicators:

[0157] The membership degree of data corresponding to different comments in the power quality assessment index matrix can be calculated using a membership function. A Gaussian membership function is used, and the Gaussian membership function f(y) is expressed as follows:

[0158]

[0159] Where: y ib Let σ and c be the standardized data for the j-th power quality evaluation index of the i-th node. σ and c are two parameters of the Gaussian membership function. In this paper, σ is set to 0.3, and the value of c represents the center position of the membership function. Five c values ​​are used: c1 = 1, c2 = 0.75, c3 = 0.5, c4 = 0.25, and c5 = 0, to ensure that each index has five evaluation membership degrees.

[0160] Substituting the parameters σ and c into the membership function yields the membership degree calculation formulas for the five evaluation sets. The index y in the standardized matrix... ib Substituting these values ​​into the membership functions of the five evaluation levels, we obtain the evaluation matrix J as follows:

[0161]

[0162] in: This represents the index y ib For rating level V t The degree of subordination.

[0163] S43. Fuzzy Comprehensive Evaluation of Power Quality:

[0164] use The operator (weighted average fuzzy synthesis operator) performs fuzzy product operation on the weights and evaluation matrix to obtain the overall evaluation of the new power quality assessment system for distribution networks as follows:

[0165] B i =[b i (V1)b i (V2)b i (V3)b i (V4)b i (V5)]

[0166] Among them, B i Let b represent the set of membership degrees of the five comments corresponding to the i-th node object; i (V t ) represents the power quality index of the i-th node relative to the t-th comment V. t The membership degree of is calculated as follows: Where ω b The final weight of the b-th indicator

[0167] Finally, the evaluation results are quantified, and the quantization score Z of the i-th node is obtained. i The calculation formula is:

[0168]

[0169] Based on the calculated power quality assessment results and the corresponding quantitative grading and scoring range of the power quality of the above distribution network, the power quality evaluation of the distribution network is obtained.

[0170] Example 1:

[0171] To verify the superiority and reliability of the comprehensive power quality assessment method for power distribution networks designed in this invention, the inventors also conducted specific tests and listed the actual assessment results of Example 1 for verification.

[0172] This embodiment is based on Figures 1-4 The distribution network, directed network diagram, and evaluation method shown in Example 1 are implemented as follows: Figure 3 As shown, the directed network diagram of this distribution network has 23 nodes. Each node can monitor the power quality index data. The monitored power quality index data are shown in Table 1 below.

[0173] Table 1 lists the data for six power quality indicators—voltage deviation, voltage fluctuation, voltage sag, three-phase imbalance, harmonics, and frequency deviation—for 23 nodes in the directed network diagram of the distribution network in Example 1.

[0174] Table 1.

[0175]

[0176]

[0177] Based on the content in Table 1 above, the scheme of step S2 of the comprehensive power quality assessment method for distribution networks of the present invention is used to calculate the node degree centrality, betweenness centrality, and eigenvector centrality of each node, and the comprehensive decision value of node importance of each node is calculated based on the CRITIC weighting method. The final comprehensive decision value of node importance of the 23 nodes is as follows: Figure 5 The bar chart is shown below.

[0178] Based on the calculated comprehensive decision value of the importance of the 23 nodes and the actual measured values ​​of the six power quality indicators (voltage deviation, voltage fluctuation, voltage sag, three-phase imbalance, harmonics and frequency deviation) of the 23 nodes listed in Table 1 above, the subjective weight value and objective weight value are calculated by the G1 method and the CRITIC method respectively, and then the comprehensive weight value is obtained by performing a comprehensive subjective and objective weight calculation.

[0179] Then, a two-stage weighting method is used to assign weights to obtain the final weight values. Finally, a fuzzy comprehensive evaluation method is used to quantitatively evaluate the power quality of each node in the distribution network to obtain the final quantitative evaluation results.

[0180] For ease of comparison, this invention also provides a quantitative evaluation structure for evaluating the distribution network of Example 1 using traditional power quality assessment methods that do not consider the importance characteristics of each node in a complex distribution network.

[0181] Table 2 lists the evaluation scores of the 23 nodes of the distribution network under the evaluation set V, based on the evaluation level of each node using the comprehensive power quality evaluation method of the present invention (considering the importance of each node in the distribution network) and the traditional power quality evaluation method (not considering the importance of each node in the distribution network).

[0182] Table 2.

[0183]

[0184]

[0185] In the evaluation set V, level V1 indicates excellent power quality, with a scoring range of (85, 100] and a quantitative score of 95; level V2 indicates good power quality, with a scoring range of (75, 85] and a quantitative score of 80; level V3 indicates average power quality, with a scoring range of (60, 75] and a quantitative score of 70; level V4 indicates poor power quality, with a scoring range of (50, 60] and a quantitative score of 55; and level V5 indicates poor power quality, with a scoring range of (0, 50] and a quantitative score of 40.

[0186] Accordingly, Figure 6 This paper presents a comparison between the evaluation results of the power quality comprehensive assessment method for distribution networks using the present invention, which considers node importance, and the evaluation results of traditional power quality assessment methods that do not consider node importance. Specifically, referring to nodes 6, 8, and 15, the scoring results of the power quality comprehensive assessment method for distribution networks using the present invention are lower than those of traditional power quality assessment methods that do not consider node importance. However, for nodes with lower node importance, such as nodes 7, 10, and 13, the scores do not show a significant decrease or increase. This achieves the purpose of the present invention to differentiate the power quality assessment for nodes with higher importance.

[0187] As can be seen from Table 2 above, the comprehensive power quality assessment method for distribution networks that considers node importance, adopted in this invention, yields assessment results that are basically consistent with those of the assessment method that does not consider node importance, demonstrating high adaptability. For node 6, which has high node importance, the rating decreases by one level during the fuzzy comprehensive evaluation process due to the constraint of its high node importance comprehensive decision value, thus meeting a more stringent evaluation standard.

[0188] In summary, the comprehensive power quality assessment method for distribution networks based on complex network theory of this invention can solve the problem that existing traditional power quality assessment indicators only consider narrow power quality indicators and cannot reflect the importance characteristics of each node in a complex distribution network. In practical application, this comprehensive power quality assessment method not only considers traditional power quality indicators but also incorporates complex network parameters, so that the quantitative assessment score reflects the differentiated scoring of nodes with different importance. Therefore, this comprehensive power quality assessment method can accurately assess the power quality of complex distribution networks, facilitating subsequent improvements in power supply quality and enhancing the user's electricity experience. It has good prospects for promotion and application value.

[0189] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A power quality comprehensive evaluation method for a power distribution network based on complex network theory, characterized in that, The method comprises the following steps: S1: drawing a directed network diagram of the power distribution network; S2: obtaining complex network indexes of each node in the directed network diagram of the power distribution network, and weighting the complex network indexes by using a CRITIC algorithm to calculate a node importance comprehensive decision value of each node in the power distribution network; S3: combining an actual measurement value of an electric energy quality index of each node in the power distribution network with the node importance comprehensive decision value, calculating a subjective weight value and an objective weight value of the electric energy quality index of each node in the power distribution network by using a G1 method and a CRITIC method respectively, and then performing a subjective and objective comprehensive weight calculation to obtain a comprehensive weight value, and performing a two-stage weight optimization on the comprehensive weight value to obtain an optimal weight value; S4: quantitatively evaluating the electric energy quality of each node in the power distribution network by using a fuzzy comprehensive evaluation method according to the optimal weight value; In step S2, the complex network indexes comprise a node degree centrality, a node betweenness centrality and a characteristic vector centrality. In step S2, the node degree centrality, the node betweenness centrality and the characteristic vector centrality of each node in the directed network diagram of the power distribution network are obtained, and specifically: The node degree centrality is: ; in, For nodes The degree centrality of nodes is represented by the degree of nodes. The number of directly connected nodes; The out-degree of a node is represented by the node's out-degree. The number of links originating from this point; The in-degree of a node is its degree to the number of nodes. The number of links leading to the endpoint; The node betweenness centrality is: ; wherein the node has a node betweenness centrality of ; is the number of shortest paths from the node to the node under the shortest path algorithm; is the number of paths passing through the node in the path ; The characteristic vector centrality is: ； wherein, is the eigenvector centrality of the node ; is the eigenvector centrality of the node ; is the out-degree of the node ; is the set of all nodes in the directed network graph to which a directed edge points , d is a damping coefficient; In step S2, the complex network indexes are weighted by using the CRITIC algorithm to calculate the node importance comprehensive decision value of each node in the power distribution network, and specifically: S21: based on the numerical values of the complex network indexes of each node in the power distribution network, a complex network index matrix is established; S22: initial data processing is performed on the complex network index matrix to obtain a complex network index standardization matrix; S23: Calculate the coefficient of variation of the a-th index ; S24: According to the complex network index standardization matrix, the correlation coefficient between the first index and the second index is calculated, and then the conflict quantitative value is calculated . . . ; S25: Calculate the coefficient of variation according to the formula and the conflicting quantification values Calculate the information content ; S26: normalizing the information quantity to obtain the weight of the first a index ; S27: Based on the calculations in S23-S26 above, the weight set of the node importance index is as follows: The formula for calculating the node importance decision value for each node is as follows: wherein, represents the weight value of the αth complex network index; represents the standardized data of the αth index of the αth node in the complex network index standardized matrix. i represents the standardized data of the αth index of the αth node in the complex network index standardized matrix.

2. The power distribution network power quality comprehensive evaluation method according to claim 1, characterized in that, In the step S1, the directed network diagram of the power distribution network has a plurality of nodes, and the nodes are power stations, distributed power sources, transformer substations, converter stations or load devices.

3. The power distribution network power quality comprehensive evaluation method according to claim 1, characterized in that, In step S3, the electric energy quality index comprises a voltage deviation, a voltage fluctuation, a voltage sag, a three-phase imbalance, a harmonic and a frequency deviation.

4. The power distribution network power quality comprehensive evaluation method according to claim 3, characterized in that, In step S3, the actual measured values of the six power quality indexes selected by each node in the power distribution network are combined with the node importance comprehensive decision value, and a power quality data matrix is established based on the six power quality indexes and the node importance comprehensive decision value : wherein, , , m is the number of nodes in the power distribution network, l is the number of evaluation indices, and l is 7.

5. The power distribution network power quality comprehensive evaluation method according to claim 4, characterized in that, In step S3: The subjective weight is calculated based on the G1 method, specifically: a plurality of evaluation indexes including the power quality indexes and the node importance comprehensive decision values are obtained, the evaluation index sequence relationship is determined first, and then the relative importance degree ratio between the evaluation indexes is determined , and finally the subjective weight value set of each node is calculated based on the relative importance degree ratio between the evaluation indexes ;​ Based on the objective weight calculation of CRITIC method, specifically: for power quality data matrix Data processing, get the node power quality data standardization matrix; Calculate the coefficient of variation of the index; calculate the first standardization matrix based on the standardization matrix. The first evaluation indicator and the first The correlation coefficients among the evaluation indicators are used to calculate the quantitative value of conflict. Based on conflict quantification value The information content is obtained by calculating the coefficient of variation of the indicators; the information content is then normalized to obtain the first... b Objective weight values ​​of each indicator ; The comprehensive weight calculation is specifically as follows: the calculation formula of the subjective and objective comprehensive weight is as follows: wherein, is a comprehensive weight, is a subjective weight of the first to the l th index calculated based on the G1 method, is an objective weight of the first to the b th index calculated based on the CRITIC method.

6. The power distribution network power quality comprehensive evaluation method according to claim 5, characterized in that, In step S3, the two-stage weight optimization is performed on the comprehensive weight value to obtain the optimal weight value, and specifically: Based on the aforementioned comprehensive weight value Using the initial weights, the calculation yields the... i The node of the first b Expected weight values ​​of each evaluation indicator for: According to the purpose of the two-stage method, an optimization model of the evaluation index weight is established by using a least square method: Based on the above optimization model to obtain That is, the maximum weight value of each evaluation index.

7. The power distribution network power quality comprehensive evaluation method according to claim 4, characterized in that, In step S4, the evaluation indexes are quantified to obtain an evaluation set for quantitatively classifying the electric energy quality, then the membership degrees of the node data corresponding to the evaluation set are calculated by using a membership function, and finally a weighted average type fuzzy comprehensive operator is used to perform a fuzzy product operation on the optimal weight value and the evaluation matrix, and the evaluation result is quantitatively calculated.

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