Power distribution network power quality partitioning method and system based on electrical distance and time sequence

Abstract

The invention belongs to the field of power distribution network power quality management, and provides a power distribution network power quality partitioning method and system based on electrical distance and time sequence, and the method comprises the steps: obtaining the power quality time sequence of each node in a power distribution network, and carrying out the preprocessing; standardization processing is carried out based on the preprocessed power quality time sequence, and a power quality comprehensive index sequence is constructed; according to the electrical distance between the nodes, whether grey correlation degree calculation is carried out between the two nodes is judged, if yes, the grey correlation degree between the two nodes is calculated based on the electric energy quality comprehensive index sequence of the two nodes, and therefore an electric energy quality comprehensive correlation degree matrix is obtained; and according to the electric energy quality comprehensive correlation degree matrix, utilizing a hierarchical clustering method to formulate a partition judgment rule, performing electric energy quality region division on the power distribution network, and dividing nodes with high electric energy quality correlation coupling degree into the same region to obtain a region division result. According to the invention, electric energy quality partitioning can be comprehensively and accurately realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of power quality management of distribution networks, and specifically relates to a method and system for power quality zoning of distribution networks based on electrical distance and timing. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the expansion of the distribution network and the diversification of loads, power quality issues are becoming increasingly prominent. In addition, the output of new energy power generation systems is affected by the environment, temperature, etc., and waveforms or nonlinear loads produce distorted power and propagate within the power grid, which further increases the risk of more serious power grid pollution. The increase in the penetration rate of new energy power generation makes the power quality operation scenario of the power grid more complex and more difficult to manage. In order to reasonably evaluate the overall power quality of the power grid, it is necessary to consider multiple aspects and obtain a comprehensive evaluation result. At the same time, it is necessary to analyze and process the power quality data of each node, decouple and comprehensively manage the dispersed and superimposed complex power quality relationships, so as to improve the power quality level and the utilization rate of management equipment, and ensure the stable operation of the power grid.

[0004] With the rapid development of power monitoring systems, a large amount of monitoring data of power systems has emerged, which contains rich power quality information. By mining the characteristics of power quality data and perceiving its development trend, we can explore power quality management solutions by mining the potential coupling relationships. However, there is a certain correlation between different power qualities, which may lead to information loss during perception. If the correlation between various types of power quality cannot be processed, it may lead to the inability to fully and accurately perceive the power quality situation of the power grid, which in turn affects the power quality monitoring and management results of the power grid.

[0005] Therefore, it is urgent to study the power quality zoning method of distribution network under complex power grid application scenarios, so that the power quality problems of new power systems based on new energy can be comprehensively evaluated, and the differences in management requirements between different nodes can be met, thereby improving the power quality of the power grid. However, traditional methods often only partition based on electrical distance or a single power quality indicator, while ignoring the correlation and coupling between different indicators. This makes the power quality zoning unreasonable or the number of zoning too large, resulting in low utilization of power quality management devices, and it is difficult to achieve a comprehensive improvement in the power quality of the distribution network. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a distribution network power quality zoning method and system based on electrical distance and timing. The present invention studies the distribution network power quality zoning method under complex power grid application scenarios, so that the power quality problems of new power systems based on renewable energy can be comprehensively evaluated. At the same time, considering the actual electrical distance between nodes in the power grid system, the power quality of the distribution network is zoned so that the differences in governance requirements between different nodes can be met, thereby improving the power quality of the power grid.

[0007] According to some embodiments, a first solution of the present invention provides a method for power quality zoning of a distribution network based on electrical distance and timing, using the following technical solution: The power quality zoning method of distribution network based on electrical distance and timing includes: Obtain the power quality time series of each node in the distribution network and preprocess it using piecewise linear representation; Based on the preprocessed power quality time series, the standardization is performed, and then the power quality comprehensive index series is constructed using the grey correlation weight method; Calculate the electrical distance between two nodes and set the distance threshold; According to the relationship between the electrical distance between the nodes and the distance threshold, it is judged whether the grey correlation degree between the two nodes is calculated. If yes, the grey correlation degree between the two nodes is calculated based on the power quality comprehensive index sequence of the two nodes; otherwise, the grey correlation degree between the two nodes is zero; according to the judgment result, the power quality comprehensive correlation matrix is ​​obtained; Based on the comprehensive correlation matrix of power quality, the hierarchical clustering method is used to formulate the zoning criteria, and the nodes with high degree of power quality correlation coupling are divided into the same area to obtain the regional division results.

[0008] According to some embodiments, a second solution of the present invention provides a distribution network power quality zoning system based on electrical distance and timing, which adopts the following technical solution: The power quality zoning system of the distribution network based on electrical distance and timing includes: The sequence acquisition and processing module is configured to obtain the power quality time series of each node in the distribution network and perform preprocessing using a piecewise linear representation method; The comprehensive index sequence construction module is configured to perform standardization based on the preprocessed power quality time series, and then construct the power quality comprehensive index sequence using a grey correlation weight method; An electrical distance calculation module is configured to calculate the electrical distance between two nodes and set a distance threshold; The comprehensive correlation matrix determination module is configured to determine whether the gray correlation calculation is performed between two nodes according to the relationship between the electrical distance between the nodes and the distance threshold. If yes, the gray correlation between the two nodes is calculated based on the power quality comprehensive index sequence of the two nodes; otherwise, the gray correlation between the two nodes is zero; and the power quality comprehensive correlation matrix is ​​obtained according to the judgment result; The distribution network area division module is configured to formulate a partitioning criterion based on the comprehensive power quality correlation matrix using a hierarchical clustering method, and divide the nodes with a high degree of power quality correlation coupling into the same area to obtain a regional division result.

[0009] According to some embodiments, a third aspect of the present invention provides a computer-readable storage medium.

[0010] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the method for power quality zoning of a distribution network based on electrical distance and timing as described in the first aspect above.

[0011] According to some embodiments, a fourth aspect of the present invention provides a computer device.

[0012] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the method for power quality zoning of a distribution network based on electrical distance and timing as described in the first aspect above are implemented.

[0013] Compared with the prior art, the present invention has the following beneficial effects: In view of the complexity of the original power quality data, the present invention adopts piecewise linear representation to preprocess the data, extract and construct a time series data set of important feature points. At the same time, the time series of important feature points of each node of different lengths are processed to be equal in length, and a time series data set of equal length is constructed to facilitate the subsequent data association relationship analysis.

[0014] The present invention combines electrical distance with power quality index data. The electrical distance reflects the tightness of the electrical connection between nodes, while the power quality index directly reflects the power quality level of the node. By combining different power quality indicators, the situation where the number of power quality partitions in the distribution network is too large is reduced, the utilization rate of the management equipment is improved, and the management effect is guaranteed to meet the requirements. When performing node coupling analysis, combined with the electrical distance condition restriction, nodes with similar coupling degrees but too far actual electrical distances can be further eliminated, and node areas with high power quality correlation coupling degrees can be divided more scientifically. On this basis, more reasonable operation strategies and maintenance plans can be formulated, thereby enhancing the robustness of the system and its ability to respond to emergencies. Provide more accurate targeting for subsequent management measures. Compared with the prior art, it can more comprehensively and accurately reflect the actual correlation degree and power quality status between nodes in the distribution network.

[0015] This application uses the sensitivity method to calculate the electrical distance. By constructing a reactive power / voltage sensitivity matrix and analyzing the change relationship between the electrical quantities of the nodes, the electrical distance is derived. This method can more accurately reflect the degree of electrical connection between nodes and provide a reliable basis for the division of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0017] Figure 1 It is a flow chart of a method for power quality zoning of a distribution network based on electrical distance and timing in an embodiment of the present invention; Figure 2 It is a distance metric intuitive diagram in an embodiment of the present invention; Figure 3 It is a standard IEEE 14-node power distribution system model in an embodiment of the present invention; Figure 4 1 is a schematic diagram of harmonic content sampling data in an embodiment of the present invention; Figure 5 is a schematic diagram of voltage deviation sampling data in an embodiment of the present invention; Figure 6 Schematic diagram of harmonic control effect in an embodiment of the present invention; Figure 7 It is a schematic diagram of the voltage deviation control effect in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0019] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0020] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0021] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0022] Embodiment 1 The present embodiment provides a method for power quality zoning of a distribution network based on electrical distance and timing. The present embodiment takes the application of the method to a server as an example. It is understandable that the method can also be applied to a terminal, and can also be applied to a system including a terminal, a server, and a server, and is implemented through the interaction between the terminal and the server. The server can be an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communications, middleware services, domain name services, security services CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in this application. In the present embodiment, the method includes the following steps: Obtain the power quality time series of each node in the distribution network and preprocess it using piecewise linear representation; Based on the preprocessed power quality time series, the standardization is performed, and then the power quality comprehensive index series is constructed using the grey correlation weight method; Calculate the electrical distance between two nodes and set the distance threshold; According to the relationship between the electrical distance between the nodes and the distance threshold, it is judged whether the grey correlation degree between the two nodes is calculated. If yes, the grey correlation degree between the two nodes is calculated based on the power quality comprehensive index sequence of the two nodes; otherwise, the grey correlation degree between the two nodes is zero; according to the judgment result, the power quality comprehensive correlation matrix is ​​obtained; Based on the comprehensive correlation matrix of power quality, the hierarchical clustering method is used to formulate the zoning criteria, and the nodes with high degree of power quality correlation coupling are divided into the same area to obtain the regional division results.

[0023] Combining the power quality time series data processing and the power quality time series correlation analysis method based on grey correlation weight and the electrical distance index between nodes, the power quality partitioning method of the distribution network based on electrical distance and time series is finally developed as follows: Figure 1 shown.

[0024] Step 1: Collect power quality data for each node in the distribution network and select the power quality time series data set from the monitoring network; Through the long-term and continuous monitoring and sampling of power quality index data by the power quality monitoring platform, a large amount of power quality data is obtained, which constitutes a time series with time series. Due to the characteristics of these time series such as large data volume, frequent fluctuations in data values, and noise interference, these characteristics make it possible to produce large analysis errors by directly mining the original time series. Therefore, in order to ensure the efficiency and accuracy of time series data mining, it is necessary to preprocess the original series in advance.

[0025] Step 2: Preprocess the power quality time series data of each node. Use the piecewise linear representation method to extract and construct the important feature point time series data set; and treat the important feature point time series of each node with different lengths as equal length.

[0026] Select the first endpoint of the power quality time series as the first important feature point, and determine whether the remaining data points are local extreme points or local inflection points one by one; Based on the determined local extreme points and local inflection points, important feature points are selected using vertical distance and time span respectively; The end point of the power quality time series is taken as the last important feature point, and a piecewise linear representation is adopted. All the important feature points are connected adjacently with straight line segments to form a power quality segmented sequence. The power quality segmented sequence corresponding to each node is adjusted to a time dimension of equal length to obtain the preprocessed power quality time series, which is: Take any power quality segment sequence corresponding to two nodes and extract the time point set corresponding to the two nodes; Take the union of the time point sets of the two nodes and sort them in ascending order to obtain a comprehensive time point set; According to the comprehensive time point set, two power quality segmented sequences of equal time dimensions are fitted to obtain the preprocessed power quality time series.

[0027] 2.1 The piecewise linear representation can retain the key information in the original time series, and has the advantages of simple and effective calculation, high data compression rate, good dimensionality reduction effect, and can effectively reduce noise interference. It meets the research objectives of this paper. Therefore, this paper decides to use the piecewise linear representation method to preprocess the power quality time series of each node sampled from the monitoring network. The following is a detailed explanation of the piecewise linear representation method.

[0028] Definition 1 Piecewise linear representation Assume that the one-dimensional time series of power quality is , after piecewise linear representation, the expressions of each segment sequence are shown in formula (1): (1); Where: Is a sequence These points divide the original time series into Line segment, Represents the linear function of the e-th segment.

[0029] Definition 2: Fitting error Assume the time series is , the time series is represented piecewise linearly to obtain XL, and the fitting sequence of the original time series is obtained from this , then the error between the fitting curve obtained by the piecewise linear representation and the original time series is as shown in formula (2): (2); Where: represents the fitting error of the line segment obtained by piecewise linear representation, Represent the e-th data point of the original sequence and its corresponding segmented sequence respectively.

[0030] Definition 3: Feature Points Assume the time series is , the characteristic points of the original time series can be obtained by extracting local extreme points and local inflection points. The characteristic point time series of the original time series is shown in formula (3): (3); Where: Indicated in The data value of the feature point at the moment, is the number of data points in the series.

[0031] In the process of feature point selection, the sequence points at the beginning and end of the original time series are first used as the beginning and end points of the reduced dimensionality sequence, and then other feature points are extracted through local extreme points and local inflection points. The specific conditions are shown in formula (4): (4); Where: , , are three consecutive data points in the time series.

[0032] Definition 4: Important feature points Suppose the feature point time series of the original time series is ,in, , , are three consecutive data points in the feature point time series, such as Figure 2 Using the distance measurement method, the important feature points with obvious pollution information are further screened out according to the vertical distance and time span. The important feature point time series of the original time series is shown in formula (5): (5); when When it is a local extreme feature point, the larger the vertical distance is, the The higher the possibility of becoming an important feature point, the selection condition is shown in formula (6) (6); Where: d for , , The vertical distance of the area formed by connecting three points, is the distance threshold.

[0033] when When it is a local inflection point feature point, the larger the time span, the longer the duration of the power quality mutation. The greater the probability of becoming an important feature point, the selection condition is shown in formula (7): (7); Where: It is a feature point The time span of adjacent feature points on both sides, ε is the time threshold.

[0034] Combined with the above definition, the specific steps of piecewise linear representation and feature extraction are: Firstly, the first endpoint of the original power quality time series is selected as the first important feature point, and the remaining data points are judged one by one whether they are local extreme points or local inflection points; secondly, based on the local extreme points and local inflection points, the vertical distance and time span are used to screen out the important feature points to be extracted respectively; finally, the end point of the original sequence is taken as the last important feature point, and a piecewise linear representation is adopted, and the obtained important feature points are connected adjacently with straight line segments to form a power quality segmented sequence.

[0035] 2.2 Equal-length processing of power quality time series Since the positions and numbers of important feature points extracted from different power quality time series are different, the lengths of the processed power quality segmented sequences, i.e., the time dimensions, are inconsistent. However, data analysis usually requires that each row of power quality segmented sequence has an equal time dimension for comparative analysis. For example, the calculation of indicators such as grey correlation requires that the power quality segmented sequences have equal lengths and synchronization time. Therefore, in order to meet this requirement, it is necessary to process the power quality segmented sequences with different time dimensions to be equal in length.

[0036] Take any two key characteristic value power quality segmentation sequences and , as shown in formulas (8) and (9): (8); (9); Where: , ,and a ≠ b .

[0037] Time point collection and Take the union and sort it in ascending order to get the set ,in Finally, based on the union, two important characteristic point power quality segment sequences of equal length are fitted, as shown in formulas (10) and (11): (10); (11); After equal length processing, the k The time series of important feature points is shown in formula (12): (12); Among them, k Corresponding nodes k , the sequence of the two numbers corresponds one to one.

[0038] The important feature point sequences of each node are processed with equal length in turn to construct a time series data set of equal length, which facilitates the analysis of the data association relationship of each node.

[0039] Specifically, suppose there are a total of zThrough long-term and continuous monitoring and sampling of power quality index data, a large amount of sampled data can form a time series of power quality with time sequence. z The length of the bar is n The data set is composed of a one-dimensional power quality time series, which is expressed as shown in formula (13): (13); Where: n Indicates the length of the power quality time series, k Indicates the number of the monitored node. Indicates that the t-th retrieval time is at the node k The sampled data value, node k Corresponding to k Since the power quality time series is a set of values ​​recorded in the order of time series, the k The expression of the power quality time series is shown in formula (14): (14); Reasonable preprocessing of power quality data is the premise of grey correlation analysis. The power quality time series piecewise linear pattern representation method is used to reduce the dimension and denoise the power quality data, remove unimportant data to reduce the amount of calculation, and highlight the local pollution information characteristics of the power quality time series to improve the accuracy of data analysis. Then, in order to solve the problem of different dimensions of the power quality segmented sequence of important feature points of each node, the time series is processed with equal length to construct a power quality time series of equal length. , forming a data set as shown in formula (15): (15); Step 3: Construct a comprehensive power quality index sequence. Data mining is performed based on the preprocessed data set. According to the power quality index attributes, the min-max standardization method is used to standardize the time series data, and the grey correlation weight method is used to construct a comprehensive power quality index sequence.

[0040] Based on the preprocessed power quality time series, the standardization is performed, and then the grey correlation weight method is used to construct the power quality comprehensive index series, which is as follows: The preprocessed power quality time series is standardized to obtain the standardized power quality time series of each node; Select the standardized power quality time series with the best power quality as the reference series, and take the sequence point data value of each indicator of each node at different sampling times as the comparison series based on the standardized power quality time series of each node; For a certain indicator, calculate the correlation coefficient between a certain node comparison sequence and the reference sequence at each sampling time; At the same time, the grey correlation weight of a certain indicator of a certain node is determined according to the ratio of the correlation coefficient of a certain indicator of a certain node to the sum of the correlation coefficients of all indicators; The comprehensive power quality index sequence is determined based on the sum of the products of the grey relational weights of all indicators and the comparison sequence of the indicators.

[0041] 3.1 Standardization processing.

[0042] When conducting grey correlation analysis, we first need to determine the data sequence that can reflect the system behavior characteristics as the reference sequence, and set the sequence composed of factors that affect the system behavior as the comparison sequence. Secondly, the physical meanings of different power quality index data are different, so the corresponding dimensions may be different. In order to better compare and analyze the time series that require correlation analysis, it is usually necessary to standardize the preprocessed power quality time series data sets of equal length to reflect the data to the [0, 1] interval. Since the power quality indicators contain both positive and negative indicators, the min-max standardization method is used for processing. k Take the preprocessed power quality time series as an example: If the indicator is a positive indicator, that is, the larger the indicator, the better, then formula (16) is used for standardization.

[0043] (16); If the indicator is a negative indicator, that is, the smaller the indicator, the better, then formula (17) is used for standardization.

[0044] (17); 3.2 Construct a comprehensive power quality index sequence based on the grey correlation weight method.

[0045] For the comprehensive zoning management of harmonics and voltage deviations, it is necessary to consider the severity of the pollution of the two indicators on the overall power quality level of the system. When harmonic and voltage deviation problems exist at the same time, the focus should be on the power quality problems with more serious pollution when implementing management. Therefore, the severity characteristics of the pollution level reflect the importance of each indicator variable from the variable dimension, and are another important parameter for analyzing the overall coupling relationship between harmonics and voltage deviations between system nodes.

[0046] The specific calculation process is as follows: (1) The pollution environment in which the system is located changes with the passage of time, and different power quality indicators at the same sampling time have different influences on the overall power quality level of the node. The time series with the best power quality is selected from the sampling data set of the indicator as the reference series, and the sequence point data values ​​of each indicator of each node at different sampling times are used as the comparison series.

[0047] (2) Using formula (18), for a certain indicator, calculate the correlation coefficient between a certain node comparison sequence and the reference sequence at each sampling time. For the harmonic voltage content rate, k The comparison sequence of nodes is t The sequence point data and j The correlation coefficient of the reference sequence corresponding to the data when the node is used as the reference node is For voltage deviation, k The comparison sequence of nodes is t The correlation coefficient between the sequence point data and the corresponding data of the reference sequence when the jth node is used as the reference node is: .

[0048] (18); The correlation coefficient between sequences measures the level of correlation between the reference sequence and the comparison sequence elements. Due to the huge amount of data, the obtained correlation coefficients are numerous and the information is scattered, making it difficult to comprehensively analyze the overall correlation of the sequence. To solve this problem, it is necessary to convert these scattered correlation coefficients into an effective indicator to reflect the degree of correlation between sequences. This process is achieved by calculating the average value of the correlation coefficient, that is, obtaining the gray correlation degree, as shown in formula (19): (19); Where: It is k The comparison sequence of nodes is j The gray correlation degree of each node. The larger the gray correlation degree, the greater the influence of the state of the corresponding node of the reference sequence on the state of the corresponding node of the comparison sequence. On the contrary, the influence of the state of the corresponding node of the comparison sequence on the state of the corresponding node of the reference sequence is smaller.

[0049] (3) The correlation coefficient describes the degree of correlation between the comparison series and the reference series at a certain sampling moment, and will change over time. When the correlation coefficient is small, it means that the corresponding pollution problem at that moment has a more serious impact on the power quality performance of the system, and it is more important in the comprehensive management of pollution. When analyzing the overall coupling relationship of complex pollution between nodes, this indicator is more important. Here, the gray correlation weight of each power quality indicator is used to reflect the importance of various power quality problems in the analysis at a certain moment. For example, k The comparison sequence of nodes is tGrey correlation weight of voltage deviation at each time point The calculation of is shown in formula (20): (20); Where: and Respectively represent k Comparison sequence No. t The correlation coefficient between the voltage deviation and harmonic content rate data corresponding to each time point and the corresponding data of the reference sequence.

[0050] Based on the calculations from formulas (16) to (20) , , and , the system composite pollution characteristics can be expressed by formula (21) as (twenty one); Where: Indicated in k The node t The system composite pollution characteristics at each moment, that is, the comprehensive power quality index sequence. The bigger, The larger the value is, the greater the impact of the power quality index data on the overall pollution level of the system, and vice versa.

[0051] Step 4: Take each node as a reference node and all nodes as comparison nodes in turn, calculate the electrical distance between each comparison node and the reference node, and calculate the average electrical distance E from all comparison nodes to the reference node as the distance threshold.

[0052] Electrical distance is an important indicator that reflects the degree of electrical connection between nodes. By calculating the electrical distance between each node, we can more accurately understand the electrical structure of the distribution network and provide a basis for the division of the distribution network. At present, the methods for calculating electrical distance are divided into three categories: impedance method, phase angle method and sensitivity method. This paper calculates electrical distance based on sensitivity. The sensitivity method usually includes calculating the reactive power / voltage sensitivity matrix. Through this matrix, the change relationship of electrical quantities between nodes can be analyzed, thereby deriving the electrical distance.

[0053] The Newton-Raphson power flow calculation formula is as follows: (twenty two); In the formula, Δ P and Δ Q Respectively represent the changes of active and reactive power injected at the master node; Δ θ and Δ V Respectively represent the changes in the voltage phase angle and amplitude at the node; J is the Jacobian matrix.

[0054] Active power mainly affects the frequency and energy balance of the system, rather than the voltage amplitude. In voltage control and stability analysis, reactive power is often considered, while the connection between voltage and active power is ignored. Keeping the active power injection constant, formula (23) can be derived, which is: (twenty three); In the formula, S The sensitivity matrix representing the voltage amplitude change to the reactive power change.

[0055] element S ij is a matrix S Representation Node i The voltage amplitude changes at the node j When the reactive power injection at the node changes by one unit, this change actually reflects the i and j The degree of electrical coupling between them.

[0056] The load changes and DG injection power in the distribution network are different in different time periods, so the voltage changes at the nodes vary over time. By calculating the electrical distance based on the voltage changes at the nodes at different times, the division of the distribution network can be dynamically adjusted, and the voltage and reactive power control range within the division range can be modified accordingly.

[0057] Since the node i To Node j The electrical distance should be the same, so a mapping function is applied to correct the above formula (twenty four); In the formula, d ij Representation Node i and nodes i The electrical distance between them.

[0058] Select Node r As a reference node, the electrical distances of the remaining nodes to this node can be calculated d ir , and then obtain the set of electrical distances from each node to the node D , specifically: (25); In the formula, d ir Representation Node i and nodes r The electrical distance between D is the set of electrical distances between nodes.

[0059] The process flow of electrical distance calculation is: Collect distribution network node information, calculate the relationship between reactive power and voltage changes between each node, and construct a reactive power / voltage sensitivity matrix; correct the matrix elements as the electrical distance between nodes.

[0060] The overall correlation between power quality indicators between nodes, that is, the grey correlation It can be calculated by formula (19). Select a node as a reference node, set its corresponding power quality comprehensive index sequence as the reference sequence, use all nodes as comparison nodes, set the power quality comprehensive index sequence of the comparison node as the comparison sequence, and when calculating the grey correlation degree of each node, first consider the electrical distance between the two nodes. According to formula (26), calculate the average electrical distance from all remaining nodes to the reference node. E , as the distance threshold: (26); In the formula, z is the number of monitoring nodes in the distribution network.

[0061] Step 5: Based on the relationship between the electrical distance between the comparison node and the reference node and the distance threshold, determine whether the gray correlation degree between the two nodes is calculated. If yes, calculate the gray correlation degree between the two nodes based on the power quality comprehensive index sequence of the comparison node and the reference node; otherwise, the gray correlation degree between the two nodes is zero, and continue to select the next comparison node for judgment; and obtain the power quality comprehensive correlation matrix based on the judgment result; When the electrical distance between the comparison node and the reference node is less than the average electrical distance E- When the distance threshold is (27); Then the power quality comprehensive index sequence of the comparison node is used as the comparison sequence, and the grey correlation degree between the comparison sequence and the reference sequence is obtained by grey correlation calculation. Otherwise, the grey correlation degree between the comparison node and the reference node is zero, and the next comparison node is selected for judgment; Until all comparison nodes are judged, all grey correlation degrees are arranged in node order to obtain a reference node correlation degree sequence.

[0062] Finally, the power quality comprehensive correlation matrix is ​​obtained with the reference node correlation sequence as the column and all node sequences as the row. M , as shown in formula (28): (28); Where: It refers to iWhen the sequence of nodes is used as the reference sequence corresponding to the reference node, the grey correlation degree between the comparison sequence corresponding to the first comparison node and the reference sequence is z Represents the number of all nodes, where the diagonal is the gray correlation between all nodes and themselves, which is 1.

[0063] Step 6: Perform regional division based on the comprehensive power quality correlation matrix. Based on the comprehensive power quality correlation matrix, the hierarchical clustering method is used to formulate the partitioning criteria, and the nodes with high power quality correlation coupling degree are divided into the same area to obtain the regional division results.

[0064] According to the calculated power quality comprehensive correlation matrix, the hierarchical clustering method is used to formulate the partition criteria, and the nodes with high power quality correlation coupling degree are divided into the same area. The specific division is as follows: (1) A non-unified maximum value is selected from the power quality comprehensive correlation matrix as the correlation threshold, and the corresponding two nodes are divided into one area.

[0065] (2) Regional division is performed based on the relationship between the remaining grey correlation degree and the grey correlation degree threshold. There are the following situations: If a certain grey correlation degree is greater than 60% of the matrix grey correlation degree threshold, when two nodes are not divided into any area, the two nodes are divided into one area; when one of the two nodes has been divided into a certain area, the corresponding other node is divided into this area; when two nodes are divided into different areas respectively, they can be regarded as overlapping nodes; If the grey correlation between a node and any other node is less than 60% of the matrix grey correlation threshold, it is considered an isolated node.

[0066] (3) Calculate the regional average correlation between each overlapping node and other nodes in each region to determine the affiliation of the overlapping nodes.

[0067] The higher the average regional correlation, the higher the average correlation coupling between the overlapping node and the corresponding region. Therefore, the overlapping node can be divided into the region with the highest average correlation with its region.

[0068] The regional average correlation is the average gray correlation between a node and other nodes in the same region. The formula is shown in (29): (29); Where: For the i The node and d The average value of the grey correlation degree between the regional nodes, Representative d The number of nodes in a region, yesi Node and d of the region j Grey correlation between nodes; Step 7: Based on the regional division results, calculate the dominant nodes of each region. For the region, the gray correlation between nodes in the region is strong, and the gray correlation between nodes in different regions is weak. When the power quality operating state of the dominant node in the region is changed, the power quality of the remaining nodes can also be changed accordingly to the maximum extent according to the correlation and influence relationship between the power quality of the nodes. Therefore, by comparing the comprehensive correlation of each node in the region within the region, the node with the largest average correlation in each region is taken as the dominant node of the region.

[0069] Step 8: Install corresponding control devices at the dominant node to regulate the power quality pollution at that point, thereby greatly improving the power supply quality of the entire area.

[0070] Simulation verification analysis In order to verify the effectiveness of the proposed method, a simulation verification analysis is carried out using the standard IEEE 14-bus distribution system as a reference model. The simulation model is built using Matlab / Simulink software and mainly includes: 3 generators, 3 transformers, and 14 busbars, such as Figure 3 shown.

[0071] Considering that the power quality level at the power supply is very little affected by the pollution source, nodes 1, 2, and 3 are not monitored. Nodes 4 to 14 are used as sampling data monitoring points. The sampling time is one week, the sampling interval is 10 minutes, and the sample data totals 22,176. Figure 4 and Figure 5 shown.

[0072] It can be seen from the figure that under complex pollution scenarios, the power quality of each node in the system is affected to varying degrees, the monitoring sampling data changes and fluctuates, and the overall trend is periodic.

[0073] The partition results based on a single power quality index are shown in Table 1.

[0074] Table 1. Results of partitioning of single power quality index

[0075] The partition results of the comprehensive power quality index and node electrical distance index considered in this application are shown in Table 2, as follows: Table 1 Partition results

[0076] For the zoning of single power quality indicators, although the treatment results can meet the limit requirements of various power indicators, there are problems such as a large number of overall regional divisions, a large workload for actual treatment, and insufficient utilization of treatment equipment.

[0077] This application comprehensively divides and manages the power quality management area according to the severity of different types of power quality problems and the node electrical distance index, solving the problem that the number of areas divided by a single power quality zoning management method is large and the utilization efficiency of power quality management equipment is low. The simulation results show that this method reduces the number of divided areas, and at the same time reduces the accidental situation where the power quality situation coupling between nodes is high but the actual electrical distance is too far, and the power quality management device installed at the dominant node can take into account the harmonic and voltage deviation problems, improve the utilization efficiency of the power quality management device, realize the comprehensive management of power quality, and verify the effectiveness of the zoning management method.

[0078] The simulation results are as follows Figure 6 and Figure 7 shown.

[0079] Nodes 4 and 5 meet the power quality standard limits, and the harmonic content of the remaining dominant nodes 11, 7, and 13 is improved from 6.71%, 4.80%, and 6.95% to 0.97%, 0.92%, and 1.63%, respectively. The harmonic content of the remaining nodes in the region meets the limits. The voltage deviations of the dominant nodes 11, 7, and 13 are improved from -11.71%, -9.89%, and -12.56% to -2.94%, -2.56%, and -3.13%, respectively. The voltage deviations of the remaining nodes in the system meet the limits.

[0080] Embodiment 2 This embodiment provides a distribution network power quality zoning system based on electrical distance and time sequence, including: The sequence acquisition and processing module is configured to obtain the power quality time series of each node in the distribution network and perform preprocessing using a piecewise linear representation method; The comprehensive index sequence construction module is configured to perform standardization based on the preprocessed power quality time series, and then construct the power quality comprehensive index sequence using a grey correlation weight method; An electrical distance calculation module is configured to calculate the electrical distance between two nodes and set a distance threshold; The comprehensive correlation matrix determination module is configured to determine whether the gray correlation calculation is performed between two nodes according to the relationship between the electrical distance between the nodes and the distance threshold. If yes, the gray correlation between the two nodes is calculated based on the power quality comprehensive index sequence of the two nodes; otherwise, the gray correlation between the two nodes is zero; and the power quality comprehensive correlation matrix is ​​obtained according to the judgment result; The distribution network area division module is configured to formulate a partitioning criterion based on the comprehensive power quality correlation matrix using a hierarchical clustering method, and divide the nodes with a high degree of power quality correlation coupling into the same area to obtain a regional division result.

[0081] The examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment 1. It should be noted that the above modules as part of the system can be executed in a computer system such as a set of computer executable instructions.

[0082] The description of each embodiment in the above embodiments has different emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0083] The proposed system can be implemented in other ways. For example, the system embodiment described above is only illustrative, and the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0084] Embodiment 3 This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the method for power quality zoning of a distribution network based on electrical distance and timing as described in the first embodiment above are implemented.

[0085] Embodiment 4 This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for power quality zoning of a distribution network based on electrical distance and timing as described in the first embodiment above are implemented.

[0086] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0087] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or 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 produce 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 and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0088] 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 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0089] 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 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0090] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0091] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A power quality zoning method for distribution network based on electrical distance and timing, characterized in that: include: Obtain the power quality time series of each node in the distribution network and preprocess it using piecewise linear representation; Based on the preprocessed power quality time series, the standardization is performed, and then the power quality comprehensive index series is constructed using the grey correlation weight method; Calculate the electrical distance between two nodes and set the distance threshold; According to the relationship between the electrical distance between the nodes and the distance threshold, it is judged whether the grey correlation degree between the two nodes is calculated. If yes, the grey correlation degree between the two nodes is calculated based on the power quality comprehensive index sequence of the two nodes. Otherwise, the grey correlation degree between the two nodes is zero; According to the judgment results, the comprehensive correlation matrix of power quality is obtained; Based on the comprehensive correlation matrix of power quality, the hierarchical clustering method is used to formulate the zoning criteria, and the nodes with high degree of power quality correlation coupling are divided into the same area to obtain the regional division results.

2. The method for power quality zoning of a distribution network based on electrical distance and timing as claimed in claim 1, characterized in that: The power quality time series of each node in the distribution network is obtained and preprocessed using piecewise linear representation, specifically: Select the first endpoint of the power quality time series as the first important feature point, and determine whether the remaining data points are local extreme points or local inflection points one by one; Based on the determined local extreme points and local inflection points, important feature points are selected using vertical distance and time span respectively; The end point of the power quality time series is taken as the last important feature point, and a piecewise linear representation is adopted. All the important feature points are connected adjacently with straight line segments to form a power quality segmented sequence. The power quality segmented sequence corresponding to each node is adjusted to a time dimension of equal length to obtain a preprocessed power quality time series.

3. The method for power quality zoning of distribution network based on electrical distance and timing as claimed in claim 2, characterized in that: The power quality segmented sequence corresponding to each node is adjusted into a time dimension of equal length to obtain a preprocessed power quality time series, specifically: Take any power quality segment sequence corresponding to two nodes and extract the time point set corresponding to the two nodes; Take the union of the time point sets of the two nodes and sort them in ascending order to obtain a comprehensive time point set; According to the comprehensive time point set, two power quality segmented sequences of equal time dimensions are fitted to obtain the preprocessed power quality time series.

4. The method for power quality zoning of a distribution network based on electrical distance and timing as claimed in claim 1, characterized in that: The pre-processed power quality time series is standardized, and then the grey correlation weight method is used to construct the power quality comprehensive index sequence, which is specifically: The preprocessed power quality time series is standardized to obtain the standardized power quality time series of each node; Select the standardized power quality time series with the best power quality as the reference series, and take the sequence point data value of each indicator of each node at different sampling times as the comparison series based on the standardized power quality time series of each node; For a certain indicator, calculate the correlation coefficient between a certain node comparison sequence and the reference sequence at each sampling time; At the same time, the grey correlation weight of a certain indicator of a certain node is determined according to the ratio of the correlation coefficient of a certain indicator of a certain node to the sum of the correlation coefficients of all indicators; The comprehensive power quality index sequence is determined based on the sum of the products of the grey relational weights of all indicators and the comparison sequence of the indicators.

5. The method for power quality zoning of distribution network based on electrical distance and timing as claimed in claim 1, characterized in that: The method determines whether to perform grey correlation calculation between two nodes according to the relationship between the electrical distance between the nodes and the distance threshold. If yes, the grey correlation between the two nodes is calculated based on the power quality comprehensive index sequence of the two nodes; otherwise, the grey correlation between the two nodes is zero, specifically: Take each node as the reference node and all nodes as the comparison nodes in turn, calculate the electrical distance between each comparison node and the reference node, and calculate the average electrical distance E from all comparison nodes to the reference node as the distance threshold; Based on the size relationship between the electrical distance between the comparison node and the reference node and the distance threshold, it is judged whether the gray correlation degree calculation is performed between the two nodes. If yes, the gray correlation degree between the two nodes is calculated based on the power quality comprehensive index sequence of the comparison node and the reference node; otherwise, the gray correlation degree between the two nodes is zero, and the next comparison node is selected for judgment; Until all comparison nodes are judged, all grey correlation degrees are arranged in node order to obtain the reference node correlation degree sequence; Finally, the comprehensive power quality correlation matrix is ​​obtained with the reference node correlation sequence as the column and all node sequences as the row.

6. The method for power quality zoning of distribution network based on electrical distance and timing as claimed in claim 1, characterized in that: Based on the comprehensive power quality correlation matrix, the hierarchical clustering method is used to formulate the partitioning criteria, and the nodes with high power quality correlation coupling degree are divided into the same area to obtain the regional division results, which are specifically: Selecting the maximum grey correlation value that is not equal to one from the power quality comprehensive correlation matrix as the correlation threshold, and dividing the two nodes corresponding to the maximum grey correlation value into one area; There are several cases for regional division based on the relationship between the remaining grey correlation degree and the grey correlation degree threshold: If a certain grey correlation degree is greater than the set percentage of the matrix grey correlation degree threshold, when two nodes are not divided into any area, the two nodes are divided into one area; when one of the two nodes has been divided into a certain area, the corresponding other node is divided into this area; when two nodes are divided into different areas respectively, they can be regarded as overlapping nodes; If the grey correlation between a node and any other node is less than the set percentage of the matrix grey correlation threshold, it is considered an isolated node; The regional average correlation between each overlapping node and other nodes in each region is calculated to determine the ownership of the overlapping nodes.

7. The method for power quality zoning of a distribution network based on electrical distance and timing as claimed in claim 6, characterized in that: The calculation of the regional average correlation between each overlapping node and other nodes in each region, so as to determine the ownership of the overlapping nodes, is specifically as follows: Calculate the average grey correlation degree of each overlapping node and the remaining nodes in the same region as the regional average correlation degree; The higher the average regional correlation, the higher the average correlation coupling between the overlapping node and the corresponding region, and the overlapping node is divided into the region with the highest average correlation with its region.

8. A power quality zoning system for distribution networks based on electrical distance and timing, characterized in that: include: The sequence acquisition and processing module is configured to obtain the power quality time series of each node in the distribution network and perform preprocessing using a piecewise linear representation method; The comprehensive index sequence construction module is configured to perform standardization based on the preprocessed power quality time series, and then construct the power quality comprehensive index sequence using a grey correlation weight method; An electrical distance calculation module is configured to calculate the electrical distance between two nodes and set a distance threshold; The comprehensive correlation matrix determination module is configured to determine whether the gray correlation calculation is performed between two nodes according to the relationship between the electrical distance between the nodes and the distance threshold. If yes, the gray correlation between the two nodes is calculated based on the power quality comprehensive index sequence of the two nodes; otherwise, the gray correlation between the two nodes is zero; and the power quality comprehensive correlation matrix is ​​obtained according to the judgment result; The distribution network area division module is configured to formulate a partitioning criterion based on the comprehensive power quality correlation matrix using a hierarchical clustering method, and divide the nodes with a high degree of power quality correlation coupling into the same area to obtain a regional division result.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the method for power quality zoning of a distribution network based on electrical distance and timing are implemented as described in any one of claims 1 to 7.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the method for power quality zoning of a distribution network based on electrical distance and timing are implemented as described in any one of claims 1 to 7.

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