A dual-mode HPLC integrated terminal substation area topology intelligent recognition system

Abstract

The present invention discloses a dual-mode HPLC fusion terminal substation area topology intelligent recognition system, which relates to the technical field of smart grids. It solves the technical problems that it is difficult to understand the relationships between nodes from multiple perspectives relying on a single index. At the same time, it is difficult to group and manage node data through dual-mode HPLC communication and effectively integrate the information of subordinate nodes. By combining the analysis of electricity consumption characteristics and environmental characteristics, it is convenient to understand the relationships between nodes from multiple perspectives, rather than relying solely on a single index. This comprehensive method helps to better identify potential problems. The Pearson correlation coefficient can quantify the correlation relationships between electricity consumption characteristics and provide a clear degree of correlation. Combining the DTW algorithm for the analysis of environmental characteristics between nodes can capture complex dynamic patterns. Through dual-mode HPLC communication, high-level nodes can receive and process node data from different levels in real time.

Description

Technical Field

[0001] The present invention belongs to the field of smart grids, and specifically relates to a dual-mode HPLC fusion terminal substation area topology intelligent recognition system. Background Art

[0002] With the rapid development of smart grid and Internet of Things (IoT) technologies, traditional distribution networks are facing increasing power consumption demands and complex load management challenges. To improve the reliability, flexibility, and efficiency of the power supply system, various new communication and data processing technologies have emerged. Among them, the dual-mode HPLC (High-Performance Liquid Chromatography) communication technology, as an efficient data transmission method, can achieve high-speed information transfer on existing power lines, providing the possibility for intelligent recognition of the substation area topology. In modern distribution systems, information interaction between nodes is crucial. By real-time monitoring the power consumption characteristics and environmental characteristics of each node and effectively integrating them, accurate data support can be provided for decision-makers, thereby optimizing resource allocation, enhancing fault response capabilities, and strengthening overall power supply security. Therefore, it is very necessary to establish a dual-mode HPLC communication-based intelligent recognition system for the substation area topology.

[0003] Most of the existing intelligent recognition systems for substation area topology analyze the correlation between nodes through power consumption characteristics, relying on a single index, making it difficult to understand the relationship between nodes from multiple perspectives. At the same time, it is difficult to manage node data in groups through dual-mode HPLC communication and effectively integrate the information of subordinate nodes. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; for this purpose, the present invention proposes a dual-mode HPLC fusion terminal substation area topology intelligent recognition system to solve the technical problems of relying on a single index, making it difficult to understand the relationship between nodes from multiple perspectives, and at the same time, making it difficult to manage node data in groups through dual-mode HPLC communication and effectively integrate the information of subordinate nodes.

[0005] To solve the above problems, the first aspect of the present invention provides a dual-mode HPLC fusion terminal substation area topology intelligent recognition system, including:

[0006] A data acquisition module: collects the power consumption data and environmental data of nodes through smart meters and environmental sensors arranged at substation area nodes, and extracts the power consumption characteristics and environmental characteristics of the time series of nodes according to time series factors;

[0007] Transformer Area Topology Structure Detection Module: According to the electricity consumption characteristics and environmental characteristics of the time series of nodes, through the correlation analysis of Pearson correlation coefficient, the correlation analysis of electricity consumption characteristics between each node is carried out. Through the DTW dynamic time warping algorithm of the environmental characteristics between each node, the correlation analysis of the environmental characteristics between each node is carried out. According to the correlation analysis of electricity consumption characteristics and environmental characteristics, the transformer area topology structure is determined;

[0008] Node Classification Module: According to the levels of nodes in the transformer area topology, by analyzing the correlation between nodes at different levels, the nodes at different levels are grouped, and through the position data in the node environmental characteristics, the data of the nodes are stored in the storage mechanism of the high-level nodes;

[0009] Dual-mode HPLC Communication Module: The high-level nodes in the transformer area pack and store the data of different-level node groups received. The high-level nodes perform data transmission between nodes through dual-mode HPLC communication.

[0010] As a further solution of the present invention: It further includes:

[0011] Node Fault Analysis Module: For the grouping results of nodes at different levels, for the grouping of nodes at different levels, a fault diagnosis model is established to diagnose the faults of nodes in the transformer area topology.

[0012] As a further solution of the present invention: The data acquisition module collects the electricity consumption data and environmental data of nodes through smart meters and environmental sensors arranged at the transformer area nodes, and extracts the electricity consumption characteristics and environmental characteristics of the time series of nodes according to the time series factors, including the following steps:

[0013] The smart meters and environmental sensors arranged at the transformer area nodes collect the electricity consumption data and environmental data of the nodes. The electricity consumption data includes: power, current, voltage, electricity consumption time. The environmental data includes: environmental temperature, environmental humidity and node position data;

[0014] Add a data acquisition timestamp to the electricity consumption data and set it as the index of the corresponding data to obtain a power time series graph and a voltage time series graph. According to the obtained fluctuation graph, obtain the daily average load, peak-valley difference and load factor of each node. According to the daily average load data of the nodes, count the power time series data of the nodes whose daily average load exceeds the threshold range for a continuous preset number of days. Take the daily average load, peak-valley difference, load factor and power time series data of the nodes as the electricity consumption characteristics of the nodes;

[0015] According to the daily average load of each node, obtain the environmental data of the nodes whose daily average load exceeds the threshold range for a continuous preset number of days, and according to the corresponding environmental data, obtain the environmental temperature time series data and the environmental humidity time series data;

[0016] Perform grid division on the area. According to the node position data, calculate the distances from the nodes in the area to the central nodes of each substation area, and divide the nodes in the area into the substation areas corresponding to the central nodes with the shortest distances.

[0017] As a further solution of the present invention: The substation area topology detection module performs correlation analysis on the electricity consumption characteristics between each node through Pearson correlation coefficient correlation analysis, including the following steps:

[0018] Form electricity consumption characteristic array data from the daily average load, peak-valley difference, load factor, and power time series data of the nodes;

[0019] Through the calculation formula of the Pearson correlation coefficient, the calculation formula of the Pearson correlation coefficient is: , where xi and yi are the values of the i-th corresponding data in the electricity characteristic arrays of two nodes, and x and are respectively the averages of the corresponding data in the electricity characteristic arrays of the two nodes in the sample;

[0020] Calculate the correlation coefficients of all the electricity characteristic array data between any two nodes in each grid, and use the average value of the correlation coefficients of all the electricity characteristic array data as the electricity consumption correlation coefficient between any two nodes in the grid.

[0021] As a further solution of the present invention: Through the DTW dynamic time warping algorithm for the environmental characteristics between each node, perform correlation analysis on the environmental characteristics between each node, including the following steps:

[0022] According to the environmental temperature time series data and the environmental humidity time series data, divide the time series into several detection time intervals, and draw the corresponding time series diagrams;

[0023] Let d(a j , b j ) represent the distance between the time series data of node a in the detection time interval j and the time series data of node b in the detection time interval j. The value of d(a, b) is the difference between the averages of the time series data of node a and node b in the detection time interval j. The DTW dynamic time warping equation is set as:

[0024] dp(a j , b j ) = min{d(a j-1 , b j ), d(a j-1 , b j-1 ), d(a j , b j-1 )} + d(a j , b j )

[0025] wherein, dp(a j , b j ) is the DTW distance between node a and node b in the detection time interval j;

[0026] Calculate the sum of the DTW distances of the ambient temperature time series data and the ambient humidity time series data between nodes in the detection time interval j, and calculate the ambient feature correlation coefficient between nodes through the following formula:

[0027]

[0028] wherein, u is the ambient feature correlation coefficient between nodes, Sdtw is the sum of the DTW distances of the ambient temperature time series data and the ambient humidity time series data between nodes in the detection time interval j, Tj is the sum of the means of the ambient temperature time series data between nodes in the detection time interval j, and Rj is the sum of the means of the ambient humidity time series data between nodes in the detection time interval j;

[0029] Conduct ambient feature correlation analysis between each node by calculating the ambient feature correlation coefficient between any two nodes in each grid.

[0030] As a further solution of the present invention: Determine the substation area topology structure according to the electricity consumption feature correlation analysis and the ambient feature correlation analysis, including the following steps:

[0031] According to the results of the electricity consumption feature correlation analysis and the ambient feature correlation analysis, calculate the correlation between nodes through the following formula:

[0032]

[0033] wherein, α is the correlation analysis value between nodes, g is the electricity consumption correlation coefficient between any two nodes in the grid, and e is the Euler number;

[0034] Divide the nodes with the correlation analysis value between nodes in the grid greater than the preset threshold into the same group, randomly select nodes between different grid node groups in the substation area for correlation analysis, and merge the groups of nodes with the correlation analysis value greater than the preset threshold into the same group;

[0035] Calculate the average value of the daily average load within a continuous preset time for different groups, set the nodes in the group with the average value of the daily average load greater than the first preset threshold as high-level nodes, set the nodes in the group with the average value of the daily average load less than the second preset threshold as low-level nodes, and set the nodes in the remaining groups in the substation area as middle-level nodes to determine the substation area topology structure.

[0036] As a further solution of the present invention: The node classification module groups nodes at different levels by analyzing the correlation between nodes at different levels according to the levels of nodes in the substation area topology, and stores the data of nodes into the storage mechanism of high-level nodes through the position data in the node environmental characteristics, including the following steps:

[0037] According to the levels of nodes in the substation area topology, calculate the correlation analysis values between each node in the low-level nodes and the middle-level nodes respectively, and group the low-level nodes and the middle-level nodes by dividing the nodes with correlation analysis values less than the threshold into the same group;

[0038] Obtain the node position data in the node environmental characteristics, and store the data of the low-level nodes and the middle-level nodes into the storage mechanism of the nearest high-level node. When the node data stored in the storage mechanism of the high-level node is greater than the threshold, transfer 10% of the node data stored therein to the storage mechanism of the nearest high-level node for storage.

[0039] As a further solution of the present invention: The dual-mode HPLC communication module packs and stores the data of the grouped nodes at different levels received by the high-level nodes in the substation area, and the high-level nodes perform data transmission between nodes through dual-mode HPLC communication, including the following steps:

[0040] The high-level nodes apply HPLC high-speed power line carrier communication and HRF high-speed wireless communication technologies, and perform data transmission between nodes through the mutual backup of the HPLC and HRF channels;

[0041] Pack and store the data of the grouped nodes at different levels received by the high-level nodes in the substation area, number the nodes in the node groups, use the node number and the node position as the labels of the node data, and add labels to the data in each grouped data packet.

[0042] As a further solution of the present invention: The node fault analysis module establishes a fault diagnosis model for the grouped results of nodes at different levels, and performs sub-diagnosis on the faults of nodes in the substation area topology, including the following steps:

[0043] Obtain the historical data of the electricity consumption characteristic data of the nodes in the groups of nodes at different levels, and add normal and fault labels to the historical data according to whether the historical data is normal or faulty;

[0044] By using the power consumption characteristic data of nodes in node groups at different hierarchical nodes to form a data set, support vector machines for node groups at different hierarchical nodes are trained respectively. After training, fault diagnosis models for each node group are established through the support vector machines for node groups at different hierarchical nodes, and the faults of nodes in the node groups in the substation area topology are diagnosed separately.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0046] The present invention combines the analysis of power consumption characteristics and environmental characteristics, which is convenient for understanding the relationship between nodes from multiple perspectives, rather than relying solely on a single index. This comprehensive method helps to better identify potential problems. The Pearson correlation coefficient can quantify the correlation relationship between power consumption characteristics, provide a clear degree of correlation, and enable the similarity between different nodes to be clearly represented. Combining with the DTW algorithm can effectively analyze the non-linear and time-varying data of environmental characteristics between nodes, so as to capture complex dynamic patterns.

[0047] The present invention groups nodes at different levels, which is convenient for more clearly managing and maintaining the data in the substation area; the high-level nodes serve as data aggregation centers, effectively integrating the information of subordinate nodes and simplifying the data processing process; concentrating the data of multiple low-level nodes into high-level nodes helps to reduce the network burden and improve the data transmission efficiency. High-level nodes can obtain the data of subordinate nodes regularly or on demand, thereby optimizing the bandwidth usage. Through dual-mode HPLC communication, high-level nodes can receive and process node data from different levels in real time, so as to achieve instant monitoring of the substation area status and take timely measures to deal with abnormal situations. The centralized storage of data information from different low levels in high-level nodes can increase the redundancy of the system. If a low-level node fails, its data can still be obtained from other similar types of devices, thereby improving the overall reliability of the system. Description of the Drawings

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0049] Figure 1 It is a schematic diagram of the system framework of the present invention. Detailed Embodiments

[0050] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] Please refer to Figure 1 , an embodiment of the first aspect of the present invention provides a dual-mode HPLC fusion terminal substation area topology intelligent recognition system, including:

[0052] Data acquisition module: Collect the electricity consumption data and environmental data of the nodes through the smart meters and environmental sensors arranged at the substation area nodes, and extract the electricity consumption characteristics and environmental characteristics of the time series of the nodes according to the time series factors.

[0053] Substation area topology structure detection module: According to the electricity consumption characteristics and environmental characteristics of the time series of the nodes, through the Pearson correlation coefficient correlation analysis, perform the electricity consumption characteristic correlation analysis between each node, and through the DTW dynamic time warping algorithm of the environmental characteristics between each node, perform the environmental characteristic correlation analysis between each node. Determine the substation area topology structure according to the electricity consumption characteristic correlation analysis and environmental characteristic correlation analysis.

[0054] Node classification module: According to the levels of the nodes in the substation area topology, group the nodes at different levels by analyzing the correlation between the nodes at different levels, and store the data of the nodes into the storage mechanism of the high-level nodes through the position data in the node environmental characteristics.

[0055] Dual-mode HPLC communication module: The high-level nodes in the substation area pack and store the data of the grouped nodes at different levels received. The high-level nodes perform data transmission between nodes through dual-mode HPLC communication.

[0056] Specifically, in this embodiment, the smart meters and environmental sensors arranged at the substation area nodes collect the electricity consumption data and environmental data of the nodes. The data may include the following fields: Electricity consumption data: power, current, voltage, electricity consumption time (timestamp).

[0057] Environmental data: environmental temperature (temperature), environmental humidity (humidity), and node location (such as city name or coordinates);

[0058] Ensure that the timestamp column of the electricity consumption data and environmental data is in the date and time format and set as the index. Extract the electricity consumption characteristics and environmental characteristics of the time series of the nodes according to the time series factors.

[0059] Through the substation area topology structure detection module, according to the power consumption characteristics and environmental characteristics of the time series of nodes, through the correlation analysis of Pearson correlation coefficients, the Pearson correlation coefficient matrix between each node can be calculated using the.corr() function in Pandas. Here, we need to calculate each feature data in the power consumption characteristics separately to perform the correlation analysis of power consumption characteristics between each node. Through the DTW dynamic time warping algorithm for the environmental characteristics between each node, the fastdtw or other libraries can be used to calculate the DTW distance. Perform the correlation analysis of environmental characteristics between each node, and determine the substation area topology structure according to the correlation analysis of power consumption characteristics and environmental characteristics.

[0060] Combining the analysis of power consumption characteristics and environmental characteristics facilitates understanding the relationships between nodes from multiple perspectives, rather than relying solely on a single indicator. This comprehensive approach helps to better identify potential problems.

[0061] The Pearson correlation coefficient can quantify the correlation relationship between power consumption characteristics, provide a clear degree of correlation, and enable the similarity between different nodes to be clearly represented. Combining the effective analysis of the DTW algorithm for the non-linear and time-varying data of environmental characteristics between nodes can capture complex dynamic patterns.

[0062] Through the node classification module, according to the levels of nodes in the substation area topology, by analyzing the correlation between nodes at different levels, group the nodes at different levels, and store the data of the nodes into the storage mechanism of the high-level nodes through the location data in the node environmental characteristics; the dual-mode HPLC communication module packs and stores the data of the grouped nodes at different levels received by the high-level nodes in the substation area, and the high-level nodes perform data transmission between nodes through dual-mode HPLC communication.

[0063] Grouping the nodes at different levels facilitates more clearly managing and maintaining the data in the substation area. The high-level nodes, as the data aggregation center, effectively integrate the information of the subordinate nodes and simplify the data processing process.

[0064] Concentrating the data of multiple low-level nodes into high-level nodes helps to reduce the network burden and improve the data transmission efficiency. The high-level nodes can obtain the data of the subordinate nodes regularly or on demand, thereby optimizing the bandwidth usage.

[0065] Through dual-mode HPLC communication, high-level nodes can receive and process node data from different levels in real time, thereby achieving instant monitoring of the substation area status and taking timely measures to handle abnormal situations. In high-level nodes, data information from different low levels is centrally stored, which can increase the redundancy of the system. If a low-level node fails, its data can still be obtained from other devices of similar types, thereby improving the reliability of the overall system.

[0066] In one embodiment of the present invention, it further includes:

[0067] Node fault analysis module: For the grouping results of nodes at different levels, establish a fault diagnosis model for different levels of node grouping to diagnose the faults of nodes in the substation area topology.

[0068] In one embodiment of the present invention, the data acquisition module collects the power consumption data and environmental data of nodes through smart meters and environmental sensors arranged at the substation area nodes, and extracts the power consumption characteristics and environmental characteristics of the time series of nodes according to time series factors, including the following steps:

[0069] The smart meters and environmental sensors arranged at the substation area nodes collect the power consumption data and environmental data of nodes. The power consumption data includes: power, current, voltage, power consumption time, and the environmental data includes: environmental temperature, environmental humidity, and node location data;

[0070] Add a data acquisition timestamp to the power consumption data and set it as the index of the corresponding data to obtain a power time series graph and a voltage time series graph. According to the obtained fluctuation graph, obtain the daily average load, peak-valley difference, and load factor of each node. According to the daily average load data of the nodes, statistically analyze the power time series data of the nodes whose daily average load exceeds the threshold range for a continuous preset number of days. Take the daily average load, peak-valley difference, load factor, and power time series data of the nodes as the power consumption characteristics of the nodes;

[0071] According to the daily average load of each node, obtain the environmental data of the nodes whose daily average load exceeds the threshold range for a continuous preset number of days, and according to the corresponding environmental data, obtain the environmental temperature time series data and the environmental humidity time series data;

[0072] Divide the area into grids. According to the node location data, statistically analyze the distances from the nodes in the area to the central nodes of each substation area, and divide the nodes in the area into the substation areas corresponding to the central nodes with the closest distances.

[0073] Specifically, in this embodiment, calculate the average power of each day as the daily average load of the node;

[0074] Calculate the difference between the peak value and the valley value of each day as the peak-valley difference;

[0075] Calculate the ratio of the average daily load to the daily peak value as the load factor.

[0076] Based on the average daily load of each node, obtain the environmental data of the nodes whose average daily load exceeds the threshold range for seven consecutive days, and based on the corresponding environmental data, obtain the environmental temperature time series data and environmental humidity time series data. The upper limit of the threshold range of the average daily load is set to 1.5 times the average daily load of the node in the past year, and the upper limit of the threshold range of the average daily load is set to 0.6 times the average daily load of the node in the past year;

[0077] Divide the area into grids, and according to the node position data, count the distances from the nodes in the area to the central nodes of each substation area, and divide the nodes in the area into the substation areas corresponding to the nearest central nodes.

[0078] In one embodiment of the present invention, the substation area topology detection module performs the correlation analysis of the power consumption characteristics between each node through the Pearson correlation coefficient correlation analysis, including the following steps:

[0079] Form the power consumption characteristic array data from the average daily load, peak-valley difference, load factor and power time series data of the node;

[0080] Through the calculation formula of the Pearson correlation coefficient, the calculation formula of the Pearson correlation coefficient is: , where xi and yi are the values of the i-th corresponding data in the electrical characteristic arrays of two nodes, and x and are respectively the averages of the corresponding data in the electrical characteristic arrays of the two nodes in the sample;

[0081] Calculate the correlation coefficients of all the electrical characteristic array data between any two nodes in each grid, and take the average value of all the correlation coefficients of the electrical characteristic array data as the power consumption correlation coefficient between any two nodes in the grid.

[0082] In one embodiment of the present invention, through the DTW dynamic time warping algorithm of the environmental characteristics between each node, perform the correlation analysis of the environmental characteristics between each node, including the following steps:

[0083] According to the environmental temperature time series data and environmental humidity time series data, divide the time series into several detection time intervals and draw the corresponding time series diagrams;

[0084] Let d(a j , b j ) represent the distance between the time series data of node a in the detection time interval j and the time series data of node b in the detection time interval j. The value of d(a, b) is the difference between the means of the time series data of node a and node b in the detection time interval j. The DTW dynamic time warping equation is set as:

[0085] dp(a j , b j ) = min{d(a j-1 , b j ), d(a j-1 , b j-1 ), d(a j , b j-1 )} + d(a j , b j )

[0086] where dp(a j , b j ) is the DTW distance between node a and node b in the detection time interval j;

[0087] Calculate the sum of the DTW distances of the ambient temperature time series data and the ambient humidity time series data between nodes in the detection time interval j, and calculate the ambient feature correlation coefficient between nodes through the following formula:

[0088]

[0089] where u is the ambient feature correlation coefficient between nodes, Sdtw is the sum of the DTW distances of the ambient temperature time series data and the ambient humidity time series data between nodes in the detection time interval j, Tj is the sum of the means of the ambient temperature time series data between nodes in the detection time interval j, and Rj is the sum of the means of the ambient humidity time series data between nodes in the detection time interval j;

[0090] Perform ambient feature correlation analysis between each node by calculating the ambient feature correlation coefficient between any two nodes in each grid.

[0091] In one embodiment of the present invention, according to the electricity consumption feature correlation analysis and the ambient feature correlation analysis, determine the substation area topology structure, including the following steps:

[0092] According to the results of the electricity consumption feature correlation analysis and the ambient feature correlation analysis, calculate the correlation between nodes through the following formula:

[0093]

[0094] where α is the correlation analysis value between nodes, g is the electricity consumption correlation coefficient between any two nodes in the grid, and e is the Euler number;

[0095] Nodes with a correlation analysis value greater than a preset threshold between nodes in the grid are divided into the same group. Nodes are randomly selected between different grid node groups in the substation area for correlation analysis, and the node groups with a correlation analysis value greater than the preset threshold are merged into the same group;

[0096] Calculate the average value of the daily average load within a continuous preset time for different groups. Nodes in the group with the average value of the daily average load greater than the first preset threshold are set as high-level nodes, nodes in the group with the average value of the daily average load less than the second preset threshold are set as low-level nodes, and nodes in the remaining groups in the substation area are set as middle-level nodes to determine the substation area topology.

[0097] Specifically, in this embodiment, nodes with a correlation analysis value greater than a preset threshold between nodes in the grid are divided into the same group. Nodes are randomly selected between different grid node groups in the substation area for correlation analysis, and the node groups with a correlation analysis value greater than the preset threshold are merged into the same group. The preset threshold of the correlation analysis value is set to 0.6;

[0098] Calculate the average value of the daily average load within a continuous preset time for different groups. Nodes in the group with the average value of the daily average load greater than the first preset threshold are set as high-level nodes, nodes in the group with the average value of the daily average load less than the second preset threshold are set as low-level nodes, and nodes in the remaining groups in the substation area are set as middle-level nodes to determine the substation area topology; the first preset threshold is set to 80 kWh, and the second preset threshold is set to 600 kWh.

[0099] In one embodiment of the present invention, the node classification module groups different levels of nodes by analyzing the correlation between nodes at different levels according to the level of the nodes in the substation area topology, and stores the data of the nodes into the storage mechanism of the high-level nodes through the position data in the node environmental characteristics, including the following steps:

[0100] According to the level of the nodes in the substation area topology, calculate the correlation analysis values between each node in the low-level nodes and the middle-level nodes respectively. By dividing the nodes with a correlation analysis value less than the threshold into the same group, group the low-level nodes and the middle-level nodes; in this embodiment, the preset threshold of the correlation analysis value is set to 0.6.

[0101] Obtain the node position data in the node environmental characteristics, and store the data of the low-level nodes and the middle-level nodes into the storage mechanism of the nearest high-level node. When the node data stored in the storage mechanism of the high-level node is greater than the threshold, transfer the node data of 10% of the nodes stored therein to the storage mechanism of the nearest high-level node for storage.

[0102] In one embodiment of the present invention, the dual-mode HPLC communication module packs and stores the data of different hierarchical node groups received by the high-level nodes in the substation area. The high-level nodes perform data transmission between nodes through dual-mode HPLC communication, including the following steps:

[0103] The high-level nodes apply HPLC high-speed power line carrier communication and HRF high-speed wireless communication technologies, and perform data transmission between nodes through the mutual backup of HPLC and HRF channels;

[0104] The high-level nodes in the substation area pack and store the data of the node groups in different hierarchies received, number the nodes in the node groups, use the node numbers and node positions as labels for the node data, and add labels to the data in each packet data.

[0105] In one embodiment of the present invention, the node fault analysis module establishes a fault diagnosis model for different hierarchical node groups according to the grouping results of different hierarchical nodes, and diagnoses the faults of the nodes in the substation area topology, including the following steps:

[0106] Obtain the historical data of the power consumption characteristic data of the nodes in the groups of different hierarchical nodes, add normal and fault labels to the historical data according to whether the historical data is normal or faulty data;

[0107] In the groups of nodes in different hierarchical nodes, the power consumption characteristic data of the nodes forms a data set, and support vector machines for the groups of nodes in different hierarchical nodes are trained respectively. After training, fault diagnosis models for each node group are established through the support vector machines for the groups of nodes in different hierarchical nodes, and the faults of the nodes in the node groups in the substation area topology are diagnosed separately.

[0108] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A dual-mode HPLC fusion terminal area topology intelligent identification system, characterized in that: include: Data collection module: collects the power consumption data and environmental data of nodes through smart meters and environmental sensors arranged at the nodes in the substation area, and extracts the power consumption characteristics and environmental characteristics of the nodes in the time series according to the time series factors; Substation topology detection module: According to the power consumption characteristics and environmental characteristics of the node time series, the correlation analysis of the power consumption characteristics between each node is carried out through the Pearson correlation coefficient correlation analysis, and the DTW dynamic time warping algorithm of the environmental characteristics between each node is used, including: according to the ambient temperature time series data and the ambient humidity time series data, the time series is divided into several detection time intervals, and the corresponding time series graph is drawn; let d(a j , b j ) represents the distance between the time series data of node a in the detection time interval j and the time series data of node b in the detection time interval j. The value of d(a, b) is the difference between the mean values ​​of the time series data of node a and node b in the detection time interval j. The DTW dynamic time warping equation is set as: dp(a j , b j ) = min{d(a j-1 , b j ), d(a j-1 , b j-1 ), d(a j , b j-1 )} + d(a j , b j ); Among them, dp(a j , b j ) is the DTW distance between node a and node b in the detection time interval j; The sum of the DTW distances of the ambient temperature time series data and the ambient humidity time series data between the nodes in the detection time interval j is calculated, and the environmental feature correlation coefficient between the nodes is calculated by the following formula: ； Among them, u is the environmental feature correlation coefficient between nodes, Sdtw is the sum of the DTW distances of the ambient temperature time series data and the ambient humidity time series data between nodes in the detection time interval j, Tj is the sum of the mean values ​​of the ambient temperature time series data between nodes in the detection time interval j, and Rj is the sum of the mean values ​​of the ambient humidity time series data between nodes in the detection time interval j; By calculating the environmental feature correlation coefficient between any two nodes in each grid, the environmental feature correlation analysis between each node is performed to determine the topological structure of the substation area; Node classification module: According to the level of nodes in the area topology, nodes at different levels are grouped by analyzing the correlation between nodes at different levels, and the node data is stored in the storage mechanism of high-level nodes through the location data in the node environment characteristics; Dual-mode HPLC communication module: The high-level nodes in the station area package and store the data received by the nodes of different levels. The high-level nodes communicate through dual-mode HPLC to transmit data between nodes.

2. A dual-mode HPLC fusion terminal area topology intelligent identification system according to claim 1, characterized in that: Also includes: Node fault analysis module: Based on the grouping results of nodes at different levels, a fault diagnosis model is established to perform sub-diagnosis of node faults in the substation topology.

3. A dual-mode HPLC fusion terminal area topology intelligent identification system according to claim 1, characterized in that: The data acquisition module collects the power consumption data and environmental data of the nodes through the smart meters and environmental sensors arranged at the nodes in the substation area, and extracts the power consumption characteristics and environmental characteristics of the nodes in the time series according to the time series factors, including the following steps: Smart meters and environmental sensors deployed at the nodes in the substation area collect the node's power consumption data and environmental data. The power consumption data includes: power, current, voltage, and power consumption time. The environmental data includes: ambient temperature, ambient humidity, and node location data. Add data collection timestamp to power consumption data and set it as the index of corresponding data to obtain power time series graph and voltage time series graph. According to the obtained fluctuation graph, obtain the daily average load, peak-to-valley difference and load factor of each node. According to the daily average load data of the node, count the power time series data of the node whose daily average load exceeds the threshold range for consecutive preset days, and use the daily average load, peak-to-valley difference, load factor and power time series data of the node as the power consumption characteristics of the node. According to the average daily load of each node, the environmental data of the node whose average daily load exceeds the threshold range for a preset number of consecutive days is obtained, and according to the corresponding environmental data, the environmental temperature time series data and the environmental humidity time series data are obtained; The area is divided into grids, and based on the node location data, the distances from the nodes in the area to the central nodes of each substation are counted, and the nodes in the area are divided into the substations corresponding to the nearest central nodes.

4. A dual-mode HPLC fusion terminal area topology intelligent identification system according to claim 3, characterized in that: The area topology detection module performs a correlation analysis of the power consumption characteristics between each node through the Pearson correlation coefficient correlation analysis, including the following steps: The daily average load, peak-to-valley difference, load factor and power time series data of the node are combined into power consumption characteristic array data; The calculation formula of Pearson's correlation coefficient is: , where xi and yi are the values ​​of the i-th corresponding data in the electrical characteristic arrays of the two nodes, and x and are the average values ​​of the corresponding data in the electrical characteristic arrays of the two nodes in the samples; The correlation coefficient of all electrical characteristic array data between any two nodes in each grid is calculated, and the mean value of the correlation coefficient of all electrical characteristic array data is used as the power consumption correlation coefficient between any two nodes in the grid.

5. A dual-mode HPLC fusion terminal area topology intelligent identification system according to claim 1, characterized in that: According to the correlation analysis of power consumption characteristics and environmental characteristics, the topology of the substation area is determined, including the following steps: According to the results of power consumption feature correlation analysis and environmental feature correlation analysis, the correlation between nodes is calculated using the following formula: ； Among them, α is the correlation analysis value between nodes, g is the power consumption correlation coefficient between any two nodes in the grid, and e is the Euler number; Nodes whose correlation analysis values ​​between nodes in the grid are greater than a preset threshold are divided into the same group, nodes between different grid node groups in the station area are randomly selected for correlation analysis, and node groups whose correlation analysis values ​​are greater than a preset threshold are merged into the same group; Calculate the average value of the daily average load within a continuous preset time for different groups, set the nodes in the group whose average daily average load is greater than the first preset threshold as high-level nodes, set the nodes in the group whose average daily average load is less than the second preset threshold as low-level nodes, set the nodes in the remaining groups in the substation as middle-level nodes, and determine the substation topology structure.

6. A dual-mode HPLC fusion terminal area topology intelligent identification system according to claim 1, characterized in that: The node classification module: according to the level of the node in the station area topology, by analyzing the correlation between nodes at different levels, the nodes at different levels are grouped, and the node data is stored in the storage mechanism of the high-level node through the location data in the node environment characteristics, including the following steps: According to the level of the nodes in the area topology, the correlation analysis values ​​between the low-level nodes and the middle-level nodes are calculated respectively, and the nodes whose correlation analysis values ​​between the nodes are less than the threshold are divided into the same group, so as to group the low-level nodes and the middle-level nodes; Obtain the node location data in the node environment characteristics, store the data of the low-level nodes and the middle-level nodes in the storage mechanism of the nearest high-level node, and when the node data stored in the storage mechanism of the high-level node is greater than the threshold, transfer the node data of 10% of the nodes stored therein to the storage mechanism of the nearest high-level node for storage.

7. A dual-mode HPLC fusion terminal area topology intelligent identification system according to claim 1, characterized in that: The dual-mode HPLC communication module packages and stores the data received by the high-level nodes in the station area from the different-level node groups, and the high-level nodes communicate through the dual-mode HPLC to transmit data between nodes, including the following steps: High-level nodes use HPLC high-speed power line carrier communication and HRF high-speed wireless communication technology to perform data transmission between nodes through mutual backup of HPLC and HRF channels; The high-level nodes in the station area package and store the data received from the node groups in different levels, number the nodes in the node group, use the node number and the node position as the label of the node data, and add a label to the data in each group data packet.

8. A dual-mode HPLC fusion terminal area topology intelligent identification system according to claim 2, characterized in that: The node fault analysis module: according to the grouping results of nodes at different levels, according to the grouping of nodes at different levels, a fault diagnosis model is established to perform sub-diagnosis on the faults of nodes in the substation topology, including the following steps: Obtain historical data of power consumption characteristic data of nodes in groups of nodes at different levels, and add normal and fault labels to the historical data according to whether the historical data is normal or faulty; The power consumption characteristic data of nodes in node groups at different levels are used to form a data set, and support vector machines for node groups at different levels are trained respectively. After training, fault diagnosis models for each node group are established for support vector machines for node groups at different levels, and faults of nodes in node groups in the substation topology are diagnosed separately.

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