Electric energy quality data space-time correlation analysis method based on intelligent fusion terminal

Abstract

The invention discloses an electric energy quality data space-time correlation analysis method based on an intelligent fusion terminal, and relates to the technical field of electric energy quality operation monitoring, the intelligent fusion terminal is accessed to the network, and a power grid topology network is constructed according to the distribution position of the accessed intelligent fusion terminal and the power grid monitored by each intelligent fusion terminal; determining spatial relevance among the intelligent fusion terminals based on the constructed power grid topology network, and constructing a power quality data time sequence change diagram of the intelligent fusion terminals according to the spatial relevance; performing electric energy quality data feature extraction on the constructed electric energy quality data time sequence change diagram, and obtaining time relevance between the intelligent fusion terminals according to the extracted electric energy quality data features; and according to the obtained spatial relevance and time relevance between the intelligent fusion terminals, the risk weights of the intelligent fusion terminals in the power grid topology network are judged, so that distribution of monitoring resources is facilitated, and the monitoring efficiency of the power grid is improved.

Description

Technical Field

[0001] This invention relates to the field of power quality operation monitoring technology, specifically a method for spatiotemporal correlation analysis of power quality data based on intelligent fusion terminals. Background Technology

[0002] In modern power systems, power quality is receiving increasing attention, as it directly affects the normal operation of power equipment and users' electricity experience. The importance of intelligent converged terminals, as key devices for collecting and transmitting power quality data in the power grid, is self-evident.

[0003] Traditional power quality analysis methods often focus only on data from a single terminal, neglecting the spatiotemporal correlations between data from different smart fusion terminals. However, the power grid is a complex network, and data collected by smart fusion terminals at different locations reflects the operational status of different parts of the grid. Determining the relative importance of different smart fusion terminals within the power grid is crucial for accurately assessing power quality. Therefore, this paper presents a method for analyzing the spatiotemporal correlations of power quality data based on smart fusion terminals. Summary of the Invention

[0004] The purpose of this invention is to provide a method for spatiotemporal correlation analysis of power quality data based on intelligent fusion terminals.

[0005] The objective of this invention can be achieved through the following technical solution: a method for spatiotemporal correlation analysis of power quality data based on intelligent fusion terminals, comprising: The smart converged terminals are connected to the network, and a power grid topology is constructed based on the distribution of the connected smart converged terminals and the power grid monitored by each smart converged terminal. Based on the constructed power grid topology, the spatial correlation between each smart fusion terminal is determined, and based on the spatial correlation between the smart fusion terminals, a time-series variation diagram of the power quality data of the smart fusion terminals is constructed. Power quality data features are extracted from the constructed power quality data time series variation map, and the temporal correlation between smart fusion terminals is obtained based on the extracted power quality data features. Based on the spatial and temporal correlations among the obtained intelligent fusion terminals, the risk weight of the intelligent fusion terminals in the power grid topology is determined.

[0006] Furthermore, the process of constructing the power grid topology includes: After the intelligent fusion terminal completes its network access, it constructs a topology node associated with each intelligent fusion terminal and uploads its own location information and the monitored power grid line information, which includes line number, line specifications and electrical parameter information. Associate the location information uploaded by the intelligent fusion terminal with the corresponding topology node; Construct a planar coordinate system and map the topology nodes to their corresponding positions within the planar coordinate system based on the positioning information associated with each topology node. Based on the power grid lines monitored by the intelligent fusion terminals associated with each topology node, corresponding topology links are generated, and corresponding link attributes are set for the topology links according to the power grid line information, thereby completing the construction of the power grid topology map.

[0007] Furthermore, the process of determining the spatial correlation between various smart converged terminals based on the constructed power grid topology includes: If any intelligent fusion terminal is selected as the reference terminal, the line transmission distance between the reference terminal and other intelligent fusion terminals is obtained and used as the spatial correlation coefficient.

[0008] Furthermore, the line transmission distance refers to the shortest power grid line distance required for one smart converged terminal to transmit power to another smart converged terminal. If one smart converged terminal cannot transmit power to another smart converged terminal via the power grid line, then there is no line transmission distance between the two smart converged terminals.

[0009] Furthermore, the process of constructing a time-series variation map of power quality data for intelligent converged terminals based on spatial correlation includes: Obtain each spatial correlation marker within the power grid topology map and acquire the power quality data of the corresponding smart fusion terminal; Construct a time-series coordinate system for power quality data; Based on the power quality data of the two smart fusion terminals corresponding to the spatial correlation marker, corresponding power quality data change curves are generated, and the power quality data change curves of the two smart fusion terminals are mapped to the same time-series coordinate system to obtain the time-series change map of power quality data corresponding to the spatial correlation marker.

[0010] Furthermore, the process of extracting power quality data features from the constructed power quality data time-series variation map, and obtaining the temporal correlation between intelligent fusion terminals based on the extracted power quality data features, includes: Set the sampling frequency, generate continuous sampling points in the power quality data time series variation graph according to the set sampling frequency, and obtain the sampling parameters corresponding to each sampling point in the power quality data time series variation graph. The sampling parameters include the sampling voltage change rate and the sampling current change rate. Set the voltage change rate threshold and the current change rate threshold; When the absolute value of the voltage change rate exceeds the voltage change rate threshold, or the absolute value of the current change rate exceeds the current change rate threshold; Then the voltage change rate or current change rate corresponding to the corresponding sampling point is extracted to obtain the corresponding power quality data features; Based on the transmission distance between two topology nodes, an influence coefficient between the topology nodes is set, and the power quality data characteristics between the two topology nodes are matched. Based on the matching results, the time correlation coefficient between the smart fusion terminals corresponding to the two topology nodes is obtained.

[0011] Furthermore, based on the obtained spatial and temporal correlations between intelligent converged terminals, the process of determining the risk weights of intelligent converged terminals in the power grid topology includes: Select any intelligent fusion terminal coordinate to be evaluated, and obtain other intelligent fusion terminals that have spatial and temporal correlation with the terminal to be evaluated. This yields the risk weight coefficient of the intelligent fusion terminal, denoted as Fp, where: ; in These are the weighting coefficients, where a1 and a2 are preset coefficients. This represents the number of other smart converged terminals that pass between the smart converged terminal labeled c and the terminal to be evaluated along the transmission distance. This represents the spatial correlation coefficient between the intelligent fusion terminal labeled c and the terminal to be evaluated. Let c be the time correlation coefficient between the intelligent fusion terminal and the terminal to be evaluated. The risk weight coefficients of each smart fusion terminal are sorted from high to low. The higher the ranking, the higher the weight of the corresponding smart fusion terminal in the power grid topology, that is, the higher the importance of the location monitored by the corresponding smart fusion terminal in the power grid topology.

[0012] Compared with the prior art, the beneficial effects of the present invention are: By analyzing the power grid location monitored by the intelligent fusion terminal and its line transmission relationship with other intelligent fusion terminals, the spatial correlation between the intelligent fusion terminals is determined. Based on the spatial correlation, the impact of an anomaly in the monitoring location of one intelligent fusion terminal on other intelligent fusion terminals is analyzed, thereby obtaining the temporal correlation between the intelligent fusion terminals. Based on the spatial and temporal correlation between the intelligent fusion terminal and all other intelligent fusion terminals, the importance of the monitoring location of the intelligent fusion terminal in the power grid is obtained, which facilitates the allocation of monitoring resources and improves the monitoring efficiency of the power grid. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0014] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation

[0015] like Figure 1 As shown, the method for spatiotemporal correlation analysis of power quality data based on intelligent fusion terminals includes: The smart converged terminals are connected to the network, and a power grid topology is constructed based on the distribution of the connected smart converged terminals and the power grid monitored by each smart converged terminal. Based on the constructed power grid topology, the spatial correlation between each smart fusion terminal is determined, and based on the spatial correlation between the smart fusion terminals, a time-series variation diagram of the power quality data of the smart fusion terminals is constructed. Power quality data features are extracted from the constructed power quality data time series variation map, and the temporal correlation between smart fusion terminals is obtained based on the extracted power quality data features. Based on the spatial and temporal correlations among the obtained intelligent fusion terminals, the risk weight of the intelligent fusion terminals in the power grid topology is determined.

[0016] It should be further explained that, in the specific implementation process, the management personnel register each smart converged terminal with the network. Each smart converged terminal is associated with a unique device number. The management personnel register the corresponding smart converged terminal with the device number, thereby granting the corresponding smart converged terminal permission to upload the monitored data.

[0017] It should be further explained that, in the specific implementation process, the process of constructing the power grid topology based on the distribution locations of the connected smart converged terminals and the power grid monitored by each smart converged terminal includes: After the intelligent fusion terminal completes its network access, it constructs a topology node associated with each intelligent fusion terminal and uploads its own location information and the monitored power grid line information, which includes line number, line specifications and electrical parameter information. Associate the location information uploaded by the intelligent fusion terminal with the corresponding topology node; Construct a planar coordinate system and map the topology nodes to their corresponding positions within the planar coordinate system based on the positioning information associated with each topology node. Based on the power grid lines monitored by the intelligent fusion terminals associated with each topology node, corresponding topology links are generated, and corresponding link attributes are set for the topology links according to the power grid line information, thereby completing the construction of the power grid topology map.

[0018] It should be further explained that, in the specific implementation process, the process of determining the spatial correlation between various smart converged terminals based on the constructed power grid topology includes: If any smart converged terminal is selected as the reference terminal, the line transmission distance between the reference terminal and other smart converged terminals is obtained. It should be noted that the line transmission distance refers to the shortest power grid line distance required for one smart converged terminal to transmit power to another smart converged terminal. If one smart converged terminal cannot transmit power to another smart converged terminal through the power grid line, then there is no line transmission distance between the two smart converged terminals. Based on the existing line transmission distance, obtain the corresponding node pairs of the reference terminal and other intelligent converged terminals, denoted as [K,c], where K corresponds to the reference terminal and c corresponds to other intelligent converged terminals; Based on the generated node pairs, spatial correlation markers are set for the corresponding smart fusion terminals within the power grid topology map; The transmission distance of each node to its corresponding line is denoted as . And it serves as a spatial correlation coefficient.

[0019] It should be further explained that, in the specific implementation process, the process of constructing the time-series variation diagram of power quality data for the intelligent fusion terminal based on spatial correlation includes: Obtain each spatial correlation marker within the power grid topology map, and obtain the power quality data of the corresponding smart fusion terminal. The power quality data includes voltage change rate and current change rate. Construct a time-series coordinate system for power quality data; Based on the power quality data of the two smart fusion terminals corresponding to the spatial correlation marker, corresponding power quality data change curves are generated, and the power quality data change curves of the two smart fusion terminals are mapped to the same time-series coordinate system to obtain the time-series change map of power quality data corresponding to the spatial correlation marker.

[0020] It should be further explained that, in the specific implementation process, the process of extracting power quality data features from the constructed power quality data time-series variation map, and obtaining the time correlation between intelligent fusion terminals based on the extracted power quality data features, includes: Set the sampling frequency, generate continuous sampling points in the power quality data time series variation graph according to the set sampling frequency, and obtain the sampling parameters corresponding to each sampling point in the power quality data time series variation graph. The sampling parameters include the sampling voltage change rate and the sampling current change rate. Set the voltage change rate threshold and the current change rate threshold; When the absolute value of the voltage change rate exceeds the voltage change rate threshold, or the absolute value of the current change rate exceeds the current change rate threshold; Then, the voltage change rate or current change rate corresponding to the corresponding sampling point is extracted to obtain the corresponding power quality data features. The power quality data features corresponding to the reference terminal in the power quality data time series change graph are denoted as... Additionally, the power quality data characteristics corresponding to the intelligent converged terminal are denoted as follows: ;in , This represents the rate of change of voltage or the rate of change of current. , Indicates the corresponding time; Based on the transmission distance between two topology nodes, an influence coefficient between the topology nodes is set, denoted as . It should be noted that the influence coefficient is set by technicians based on the transmission distance of the line, and is inversely correlated with the length of the transmission distance. The obtained power quality data characteristics Power quality data characteristics Perform a match to satisfy It should be noted that and For same-dimensional matching, that is, when When corresponding to the rate of change of voltage, then Also the rate of change of voltage. When corresponding to the rate of change of current, then It is also the rate of change of current; Acquisition and The closest moment The time difference between the two is obtained and used as the time correlation coefficient between the intelligent fusion terminals corresponding to the two topology nodes.

[0021] It should be noted that in practice, when the power quality data of the power grid location monitored by the smart fusion terminal changes, other locations that are spatially related to the smart fusion terminal will also be affected accordingly. However, this effect is often time-delayed. By using this time delay, the correlation between the power quality data features extracted by different smart fusion terminals can be found, which facilitates the subsequent assessment of the importance of each smart fusion terminal.

[0022] It should be further explained that, in the specific implementation process, the process of determining the risk weight of the intelligent fusion terminal in the power grid topology based on the obtained spatial and temporal correlations between the intelligent fusion terminals includes: Select any intelligent fusion terminal coordinates to evaluate the terminal, obtain other intelligent fusion terminals that have spatial and temporal correlation with the terminal to be evaluated, and label each other intelligent fusion terminal as c, where c = 1, 2, ..., n; The risk weight coefficient of the intelligent converged terminal is then obtained, denoted as Fp, where: ; in , These are weighting coefficients, where a1 and a2 are preset coefficients used to eliminate dimensions and level out orders of magnitude. This represents the number of other smart converged terminals that pass between the smart converged terminal labeled c and the terminal to be evaluated along the transmission distance. Let c be the time correlation coefficient between the intelligent fusion terminal and the terminal to be evaluated. The risk weight coefficients of each smart fusion terminal are sorted from high to low. The higher the ranking, the higher the weight of the corresponding smart fusion terminal in the power grid topology, that is, the higher the importance of the location monitored by the corresponding smart fusion terminal in the power grid topology. By analyzing the grid location monitored by the smart fusion terminal and its line transmission relationship with other smart fusion terminals, the spatial correlation between the smart fusion terminals is determined. Based on the spatial correlation, the impact of an anomaly in the monitoring location of one smart fusion terminal on other smart fusion terminals is analyzed, thereby obtaining the temporal correlation between the smart fusion terminals. Based on the spatial and temporal correlation between the smart fusion terminal and all other smart fusion terminals, the importance of the monitoring location of the smart fusion terminal in the power grid is obtained.

[0023] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications or equivalent substitutions made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for spatiotemporal correlation analysis of power quality data based on intelligent fusion terminals, characterized in that, include: The smart converged terminals are connected to the network, and a power grid topology is constructed based on the distribution of the connected smart converged terminals and the power grid monitored by each smart converged terminal. Based on the constructed power grid topology, the spatial correlation between each smart fusion terminal is determined, and based on the spatial correlation between the smart fusion terminals, a time-series variation diagram of the power quality data of the smart fusion terminals is constructed. Power quality data features are extracted from the constructed power quality data time series variation map, and the temporal correlation between smart fusion terminals is obtained based on the extracted power quality data features. Based on the spatial and temporal correlations among the obtained intelligent fusion terminals, the risk weight of the intelligent fusion terminals in the power grid topology is determined.

2. The method for spatiotemporal correlation analysis of power quality data based on intelligent fusion terminals according to claim 1, characterized in that, The process of constructing a power grid topology includes: After the intelligent fusion terminal completes its network access, it constructs a topology node associated with each intelligent fusion terminal and uploads its own location information and the monitored power grid line information, which includes line number, line specifications and electrical parameter information. Associate the location information uploaded by the intelligent fusion terminal with the corresponding topology node; Construct a planar coordinate system and map the topology nodes to their corresponding positions within the planar coordinate system based on the positioning information associated with each topology node. Based on the power grid lines monitored by the intelligent fusion terminals associated with each topology node, corresponding topology links are generated, and corresponding link attributes are set for the topology links according to the power grid line information, thereby completing the construction of the power grid topology map.

3. The method for spatiotemporal correlation analysis of power quality data based on intelligent fusion terminals according to claim 2, characterized in that, The process of determining the spatial correlation between various smart converged terminals based on the constructed power grid topology includes: If any intelligent fusion terminal is selected as the reference terminal, the line transmission distance between the reference terminal and other intelligent fusion terminals is obtained and used as the spatial correlation coefficient.

4. The method for spatiotemporal correlation analysis of power quality data based on intelligent fusion terminals according to claim 3, characterized in that, The transmission distance refers to the shortest power grid line distance required for one smart fusion terminal to transmit power to another smart fusion terminal. If one smart fusion terminal cannot transmit power to another smart fusion terminal via the power grid line, then there is no transmission distance between the two smart fusion terminals.

5. The method for spatiotemporal correlation analysis of power quality data based on intelligent fusion terminals according to claim 4, characterized in that, The process of constructing a time-series variation map of power quality data for intelligent fusion terminals based on spatial correlation includes: Obtain each spatial correlation marker within the power grid topology map and acquire the power quality data of the corresponding smart fusion terminal; Construct a time-series coordinate system for power quality data; Based on the power quality data of the two smart fusion terminals corresponding to the spatial correlation marker, corresponding power quality data change curves are generated, and the power quality data change curves of the two smart fusion terminals are mapped to the same time-series coordinate system to obtain the time-series change map of power quality data corresponding to the spatial correlation marker.

6. The method for spatiotemporal correlation analysis of power quality data based on an intelligent fusion terminal according to claim 5, characterized in that, The process of extracting power quality data features from the constructed power quality data time-series variation map and obtaining the temporal correlation between smart fusion terminals based on the extracted power quality data features includes: Set the sampling frequency, generate continuous sampling points in the power quality data time series variation graph according to the set sampling frequency, and obtain the sampling parameters corresponding to each sampling point in the power quality data time series variation graph. The sampling parameters include the sampling voltage change rate and the sampling current change rate. Set the voltage change rate threshold and the current change rate threshold; When the absolute value of the voltage change rate exceeds the voltage change rate threshold, or the absolute value of the current change rate exceeds the current change rate threshold; Then the voltage change rate or current change rate corresponding to the corresponding sampling point is extracted to obtain the corresponding power quality data features; Based on the transmission distance between two topology nodes, an influence coefficient between the topology nodes is set, and the power quality data characteristics between the two topology nodes are matched. Based on the matching results, the time correlation coefficient between the smart fusion terminals corresponding to the two topology nodes is obtained.

7. The method for spatiotemporal correlation analysis of power quality data based on intelligent fusion terminals according to claim 6, characterized in that, The process of determining the risk weight of intelligent fusion terminals in the power grid topology based on the obtained spatial and temporal correlations among intelligent fusion terminals includes: Select any intelligent fusion terminal coordinate to be evaluated, and obtain other intelligent fusion terminals that have spatial and temporal correlation with the terminal to be evaluated. This yields the risk weight coefficient of the intelligent fusion terminal, denoted as Fp, where: ； in , These are the weighting coefficients, where a1 and a2 are preset coefficients. This represents the number of other smart converged terminals that pass between the smart converged terminal labeled c and the terminal to be evaluated along the transmission distance. This represents the spatial correlation coefficient between the intelligent fusion terminal labeled c and the terminal to be evaluated. Let c be the time correlation coefficient between the intelligent fusion terminal and the terminal to be evaluated. The risk weight coefficients of each smart fusion terminal are sorted from high to low. The higher the ranking, the higher the weight of the corresponding smart fusion terminal in the power grid topology, that is, the higher the importance of the location monitored by the corresponding smart fusion terminal in the power grid topology.

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