CN119051276B - Distribution control platform based on transmission and transformation risk prediction - Google Patents
Distribution control platform based on transmission and transformation risk prediction Download PDFInfo
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Abstract
本申请提供了基于输变电风险预测的配电调控平台，涉及配电调控技术领域，包括：通过拓扑网络获取模块获取输变电拓扑网络，网络分区模块按照调控源进行分区，运行监测模块监测运行数据，故障溯源模块定位故障源，故障网络构建模块构建故障网络，关键调控线路获取模块分析关键线路，配电调控模块进行调控。通过本申请可以解决现有技术中采用单路调控所导致的电网调控效率低下，难以快速定位故障源的技术问题，实现对输变电子网络的配电调控，确保电网运行在最佳状态，同时降低故障风险，提高了电网运行的效率、安全性。
The present application provides a power distribution control platform based on power transmission and transformation risk prediction, which relates to the field of power distribution control technology, including: obtaining the power transmission and transformation topology network through a topology network acquisition module, partitioning according to the control source by a network partitioning module, monitoring the operation data by an operation monitoring module, locating the fault source by a fault tracing module, constructing a fault network by a fault network construction module, analyzing the key lines by a key control line acquisition module, and controlling by a power distribution control module. The present application can solve the technical problems of low power grid control efficiency and difficulty in quickly locating the fault source caused by the use of single-channel control in the prior art, realize power distribution control of the power transmission and transformation electronic network, ensure that the power grid operates in the best state, reduce the risk of faults, and improve the efficiency and safety of power grid operation.
Description
Technical Field
The application relates to the technical field of power distribution regulation and control, in particular to a power distribution regulation and control platform based on power transmission and transformation risk prediction.
Background
Along with the expansion of the scale of a power system and the continuous increase of the power demand, the complexity of a power transmission and transformation system is also increased, the reliability and the stability of the system are important for ensuring the power supply, the running condition of each node and each line directly influences the reliability of the whole system due to the complex topological relation of a power transmission and transformation network, and once certain nodes or lines in the system fail, chain reaction can be caused, so that large-area power interruption is caused, and the condition of large-area power interruption is difficult to effectively treat.
At present, the prior art has low power grid regulation efficiency due to single-path regulation during regulation of the lines, so that a fault source is difficult to quickly locate, and the reliability and safety of power grid operation are further affected.
Disclosure of Invention
The application aims to provide a power distribution regulation and control platform based on power transmission and transformation risk prediction, which is used for solving the technical problems that in the prior art, the regulation and control efficiency of a power grid is low and a fault source is difficult to quickly locate due to adoption of single-path regulation and control.
In view of the above problems, the power distribution regulation platform based on power transmission and transformation risk prediction comprises a topology network acquisition module, a network partitioning module, a key regulation line acquisition module, a fault tracing module and a power distribution line regulation and control module, wherein the topology network acquisition module is used for acquiring power transmission and transformation topology networks, the network partitioning module is used for partitioning the power transmission and transformation topology networks according to regulation and control sources and outputting a plurality of power transmission and transformation sub-networks, each power transmission and transformation sub-network is regulated and controlled by the same regulation and control source, the operation monitoring module is used for monitoring operation data of each power transmission and transformation sub-network in the plurality of power transmission and transformation sub-networks to obtain a multi-block operation data set, the fault tracing module is used for carrying out fault tracing according to the multi-block operation data set and detecting a first fault source corresponding to each power transmission and transformation sub-network, the fault network construction module is used for constructing a corresponding fault network according to the first fault source corresponding to each power transmission and transformation sub-network, the key regulation and control line acquisition module is used for carrying out network center analysis on the fault network through a network center analysis model and obtaining a first key line corresponding to each power transmission and transformation sub-network.
The fault network construction module is further used for acquiring a connection relation in each power transmission and transformation sub-network, carrying out fault propagation analysis on the first fault source according to the connection relation, acquiring nodes subjected to fault propagation in the power transmission and transformation sub-network, calculating fault probability of each node, and generating the fault network according to the fault probability value of each node and the nodes larger than the preset fault probability.
Further, the fault network construction module is further used for defining a conditional probability table of each power transmission and transformation sub-network, wherein the conditional probability table is used for defining the fault probability of each node in each power transmission and transformation sub-network to depend on the state of a father node of the node, each node is set with an initial prior probability, the initial state of the conditional probability table is set according to the first fault source, the posterior probability of the conditional probability table is calculated by using a Bayesian formula based on the initial state of the conditional probability table, and the fault probability of each node is output.
The key regulation and control circuit acquisition module is further used for carrying out network center analysis on the fault network through a network center analysis model, calculating degree centrality, intermediate centrality and proximity centrality of each node, carrying out fusion according to the degree centrality, intermediate centrality and proximity centrality of each node, outputting network center indexes of each node, and carrying out node connection according to the network center index of each node to acquire a first key regulation and control circuit.
The key regulation circuit acquisition module is further used for acquiring the number of first circuit nodes and the number of second circuit nodes of the fault network, wherein the number of the first circuit nodes is the maximum number of nodes continuously connected with the nodes in the fault network, the number of the second circuit nodes is the minimum number of nodes continuously connected with the nodes in the fault network, determining a k value according to the number of the first circuit nodes and the number of the second circuit nodes, and then connecting the k nodes.
Further, the key regulation and control line acquisition module is further configured to acquire a first key regulation and control line that meets a key constraint condition, where the key constraint condition is a network center index of k continuous nodes and a network center index greater than a preset network center index, and the k continuous nodes do not include edge nodes.
The fault tracing module is further used for tracing faults according to the multi-block operation data set, judging whether faults of each power transmission and transformation sub-network are mixed fault sources or not, wherein the mixed fault sources are two or more fault sources, identifying the power transmission and transformation sub-network with the mixed fault sources, outputting the identified power transmission and transformation sub-network, constructing a mixed fault network corresponding to the identified power transmission and transformation sub-network, and inputting the mixed fault network into the network center analysis model for network center analysis.
The fault tracing module is further used for obtaining N fault sources for identifying the power transmission and transformation sub-network, wherein N is a positive integer greater than or equal to 2, obtaining N fault networks corresponding to the N fault sources, and fusing the N fault networks through resetting the redundant node to output a hybrid fault network.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
the current power transmission and transformation topological network is partitioned, then each partition is subjected to fault analysis according to fault tracing, when a certain partition breaks down, the key regulation and control lines of the current partition are identified to regulate and control in time, and the line risks brought by faults are relieved step by step, so that the technical problems that in the prior art, the power grid regulation and control efficiency is low and the fault source is difficult to quickly locate due to the adoption of single-way regulation and control are effectively solved, the power distribution regulation and control of the power transmission and transformation sub-network is realized, the power grid is ensured to run in an optimal state, the fault risk is reduced, and the power grid running efficiency and safety are improved.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent. It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the application or to delineate the scope of the application. Other features of the present application will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the application or the technical solutions of the prior art, the following brief description will be given of the drawings used in the description of the embodiments or the prior art, it being obvious that the drawings in the description below are only exemplary and that other drawings can be obtained from the drawings provided without the inventive effort for a person skilled in the art.
FIG. 1 is a schematic structural diagram of a power distribution regulation platform based on power transmission and transformation risk prediction;
fig. 2 is a schematic flow chart of generating a fault network in the power distribution regulation platform based on power transmission and transformation risk prediction.
Reference numerals illustrate:
The system comprises a topology network acquisition module 11, a network partitioning module 12, an operation monitoring module 13, a fault tracing module 14, a fault network construction module 15, a key regulation and control line acquisition module 16 and a power distribution regulation and control module 17.
Detailed Description
The power distribution regulation and control platform based on power transmission and transformation risk prediction solves the problems that in the prior art, due to single-path regulation and control during regulation and control of a line, the efficiency of power grid regulation and control is low, a fault source is difficult to quickly locate, the reliability and safety of power grid operation are further influenced, the power distribution regulation and control of a power transmission and transformation sub-network is realized, the power grid operation is ensured to be in an optimal state, meanwhile, the fault risk is reduced, and the efficiency and safety of the power grid operation are improved.
In the following, the technical solutions of the present application will be clearly and completely described with reference to the accompanying drawings, and it should be understood that the described embodiments are only some embodiments of the present application, but not all embodiments of the present application, and that the present application is not limited by the exemplary embodiments described herein. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application. It should be further noted that, for convenience of description, only some, but not all of the drawings related to the present application are shown.
The application provides a power distribution regulation and control platform based on power transmission and transformation risk prediction, referring to fig. 1, the power distribution regulation and control platform based on power transmission and transformation risk prediction comprises:
the topology network acquisition module 11 is configured to acquire a power transmission and transformation topology network.
Specifically, the power transmission and transformation topology network refers to a connection relationship network among various power transmission lines, substations, switches, transformers and other devices in a power grid. This network describes the path of energy flow in the power system. Real-time data and grid structure information are extracted from the monitoring and data acquisition system. And obtaining topology information by using a power grid facility layer in the GIS system. Duplicate, erroneous or irrelevant data is removed, ensuring the accuracy of the data. The method comprises the steps of collecting data, converting the collected data into a unified format, defining a transformer substation and a bus as nodes, defining a transmission line and a transformer as branches, and creating an adjacency matrix to represent the connection relation between the nodes and the branches.
The network partitioning module 12 is configured to partition the power transmission and transformation topological network according to a regulation source, and output a plurality of power transmission and transformation sub-networks, where each power transmission and transformation sub-network is regulated by the same regulation source.
In particular, a regulation source in the power grid is determined, which may be a specific substation, a control center or an entity responsible for power supply and regulation in a certain area. Next, each device in the power grid, such as a transformer substation, a transmission line, a switch, etc., is labeled, all the regulated sources are listed, and each regulated source is assigned a unique identifier, and each regulated source is associated with the power grid device it controls. And dividing the whole power grid into a plurality of sub-networks according to the mapping relation. Traversing the power grid equipment list, adding the power grid equipment list into the sub-network of the corresponding regulation source according to the regulation source identifier of each equipment, and adding the power grid equipment list into each sub-network of the corresponding sub-network according to the regulation source identifier of the power transmission line connected with the equipment, wherein each sub-network comprises power grid equipment belonging to the same regulation source. And finally, outputting the topological structure of each sub-network, and ensuring that the equipment in each sub-network is regulated and controlled only by the corresponding regulation and control source.
And the operation monitoring module 13 is configured to monitor operation data of each power transmission and transformation sub-network in the multiple power transmission and transformation sub-networks to obtain a multi-block operation data set.
Specifically, critical operating parameters to be monitored, such as voltage, current, active power, reactive power, frequency, etc., are determined. And selecting proper monitoring points in each power transmission and transformation sub-network, which can be key substations, lines or buses, and collecting the operation data of the monitoring points in real time. And integrating the acquired data, and classifying according to the power transmission and transformation sub-network to form a regional operation data set. All critical operating parameters to be monitored are listed, such as voltage, current, active power, reactive power, frequency, etc. Key monitoring points in each power transmission and transformation sub-network are determined, such as positions of an incoming and outgoing line, a bus connection point and the like of a transformer substation. The operation data of the monitoring points are collected through SCADA systems or RTU and other devices according to a certain time interval, such as every second or every minute. The collected data are classified according to the power transmission and transformation sub-networks, and each network forms a data block. Checking the validity of the data and removing invalid or erroneous data. And carrying out normalization processing on the data. The preprocessed data is stored in a database, for example using an SQL database or a NoSQL database.
The fault tracing module 14 is configured to perform fault tracing according to the multi-block operation data set, and detect a first fault source corresponding to each power transmission and transformation sub-network.
Specifically, a multi-block operational dataset for each power transmission and transformation sub-network is analyzed for signs of anomalies or faults. The source of the anomaly data is determined and located to a particular device or component, possibly the source of the fault. And reading the multi-block operation data set of each power transmission and transformation sub-network. And (3) applying threshold detection, setting upper and lower limits of normal operation parameters, and regarding the abnormal condition when the upper limit exceeds the threshold. A clustering algorithm is used to identify outliers in the data. Time series analysis, such as auto-correlation function ACF and partial auto-correlation function PACF, is applied to detect periodic or trending anomalies in the data. And tracing to a specific monitoring point according to the time and place information of the abnormal data. Devices and lines upstream and downstream of the monitoring point are analyzed to determine possible sources of failure. Decision trees, support vector machines, or other classification algorithms are used in conjunction with the historical fault data to assist in the identification of fault types. And (3) sorting fault tracing results, including the position of a fault source, the fault type, possible reasons and the like.
The fault network construction module 15 is configured to construct a corresponding fault network according to the first fault source corresponding to each power transmission and transformation sub-network.
Specifically, the first source of failure of each power transmission and transformation sub-network, i.e., the device or location where the anomaly or failure first occurred, is identified. Fault propagation paths, i.e. possible paths for a fault to propagate outwards from a fault source, are analyzed, including directly connected devices and lines. Based on the fault propagation path, a fault network model is constructed that includes the fault source, the propagation path and the associated equipment. The areas in the faulty network that the fault may affect, including the affected devices and system parts, are marked. And obtaining the first fault source information of each power transmission and transformation sub-network from the fault tracing analysis. And identifying equipment and lines directly connected with the fault source according to the topological structure diagram of the power grid. The operating principle of the grid is analyzed to determine which paths the fault may propagate along. Using the methodology of graph theory, a new network graph is created in which nodes represent grid devices and edges represent connections between devices. In the network diagram, a fault source is taken as a starting node, and equipment and a line directly connected with the fault source are taken as first-layer nodes and edges. The next layer of equipment and lines affected by the fault are recursively added until all possible fault propagation paths are covered. In a fault network diagram, different colors or markers are used to distinguish between fault sources, propagation paths and affected devices. Based on the type and severity of the fault, the extent to which each device or area is affected by the fault is assessed.
The key regulation circuit obtaining module 16 is configured to perform a network center analysis on the fault network through a network center analysis model, and obtain a first key regulation circuit corresponding to each power transmission and transformation sub-network.
Specifically, network centrality indexes are selected to evaluate the importance of the regulation circuit, such as centrality, median centrality, tight centrality and the like. A selected network centrality index is calculated for each line in the faulty network. And sequencing all the regulation and control circuits according to the calculation result of the network centrality index. And selecting the highest regulation line in the sequence as the first key regulation line of each power transmission and transformation sub-network. The betting centrality is calculated for each line in the faulty network using an analysis tool, such as NetworkX. The calculated median centrality values are ranked. And selecting a circuit with highest medium centrality as a first key regulation circuit according to the sequencing result.
And the power distribution regulation and control module 17 is used for controlling the regulation and control sources corresponding to the power transmission and transformation sub-network to carry out power distribution regulation and control according to the first key regulation and control line.
Specifically, according to the action and influence of the first key regulation and control line in the power grid, a corresponding regulation and control strategy is formulated. According to the regulation strategy, specific regulation instructions are prepared, including regulating the switching state of a circuit, changing the output of a generator, regulating load distribution and the like. And sending the regulation and control instruction to a corresponding regulation and control source, and executing regulation and control operation by the regulation and control source. And analyzing the influence of the first key regulation and control line on the stability of the power grid, and determining the priority and the target of regulation and control. And determining equipment parameters to be adjusted, such as a switching state, a transformer tap position, line power flow and the like, writing a regulation command, defining an operation step and a target state, and sending the regulation command to a regulation source. And monitoring regulated power grid operation data in real time, including voltage, current, power and the like. And evaluating whether the regulation operation achieves the expected effect. And if the regulation effect is not ideal, analyzing the reason and adjusting the regulation strategy. And optimizing a regulation scheme according to the regulation effect and the running condition of the power grid.
Further, as shown in fig. 2, the fault network construction module 15 is further configured to:
The method comprises the steps of obtaining connection relations in each power transmission and transformation sub-network, carrying out fault propagation analysis on a first fault source according to the connection relations, obtaining nodes subjected to fault propagation in the power transmission and transformation sub-network, calculating fault probability of each node, and generating the fault network according to the fault probability value of each node and the nodes larger than the preset fault probability.
Specifically, the connection relation of each power transmission and transformation sub-network is extracted from the topological structure of the power grid. Using the methodology in graph theory, a directed or undirected graph is created as in NetworkX, with the devices in the grid, such as substations, lines, switches, etc., as nodes and the connections between the devices as edges. In the network model, node attributes are used to represent the type of device, and edge attributes are used to represent the type of connection, such as dc, ac, etc., and transmission capabilities. And determining a first fault source of each power transmission and transformation sub-network according to the result of the fault tracing analysis. Propagation of faults in the network from the fault source is simulated using fault propagation models, such as circuit theory based models, state space based methods, and the like. According to the fault propagation model, the influence of faults on each node in the network is evaluated, including voltage drop, current increase, equipment overload and the like. Based on the results of the fault propagation simulation, a probability of each node being affected by the fault is calculated. And screening out nodes larger than the preset fault probability according to the calculated fault probability value. And constructing a fault network model by using the screened nodes and the connection relations between the nodes. In the failure network model, different colors, shapes or labels may be used to represent the nodes affected by the failure and the connections between them.
By way of example, if the power transmission and transformation network nodes are A, B, C, D, E respectively, the connection relationships are as follows, a to B, C, B to A, D, C to A, E, D to B, E, E to C, D, the network may be represented as a graph, the nodes represent substations or devices, and the edges represent power transmission lines. If it is detected that node A fails, A is the first source of failure. A faults directly affect B and C, through which the fault may propagate to D, and through which the fault may propagate to E. The probability of failure of each node can be calculated from these propagation probabilities assuming that the probability of failure propagation is as follows, A-B0.8, A-C0.6, B-D0.5, C-E0.7. The failure probability of node a is 1. The failure probability of the node B is 0.8. The failure probability of node C is 0.6. The failure probability of node D is the propagation probability of B to D, 0.8 x 0.5=0.4. The failure probability of node E is the propagation probability of C to E, 0.6x0.7=0.42. If the preset failure probability threshold is 0.5. And selecting nodes with the fault probability larger than 0.5 to construct a fault network. The nodes A, B, C all have a failure probability greater than 0.5, so they constitute a failure network. Nodes D and E have a probability of failure of less than 0.5 and are therefore not in the failed network. The final failure network is node A, B, C, edge A-B, A-C.
Further, the fault network construction module 15 is further configured to:
defining a conditional probability table of each power transmission and transformation sub-network, wherein the conditional probability table is used for defining the fault probability of each node in each power transmission and transformation sub-network and depends on the state of a father node of the node, each node is set with an initial prior probability, the initial state of the conditional probability table is set according to the first fault source, the posterior probability of the conditional probability table is calculated by using a Bayesian formula based on the initial state of the conditional probability table, and the fault probability of each node is output.
In particular, in a power grid network, each node represents a device or component, the parent node of which is the other node directly connected to the node. An initial prior probability is set for each node based on historical data or system defaults. For each node, its parent node is determined and conditional probabilities in each parent node state are set. These conditional probabilities reflect the influence of the state of the parent node on the child node failure probability. And determining a first fault source according to the result of the fault tracing analysis, and setting an initial state of the first fault source. Is a triggering event such as a line break or equipment damage. Based on the initial state of the first failure source, the state of its parent node is updated, which affects the conditional probability of the child node. The posterior probability of each node, i.e., the probability of the node failing given the known parent node states, is calculated using a bayesian formula. After the initial state of the first fault source is known, iteratively updating the posterior probability of each node according to a Bayesian formula until convergence or a preset iteration number is reached. The posterior probability of each node is output, reflecting the probability of each node failing after considering the states of other nodes in the network.
Further, the critical regulatory line acquisition module 16 is further configured to:
the fault network is subjected to network center analysis through a network center analysis model, the degree centrality, the intermediate centrality and the proximity centrality of each node are calculated, fusion is carried out according to the degree centrality, the intermediate centrality and the proximity centrality of each node, network center indexes of each node are output, node connection is carried out according to the network center index of each node, and a first key regulation and control line is obtained.
In particular, centrality refers to the number of connections of one node to other nodes in the network. In a fault network, a node with high centrality has high fault propagation capability. The median centrality is a measure of the frequency of a node in a network as a shortest path bridge. In a fault network, nodes with high betweenness are key nodes for fault propagation. Proximity centrality refers to the average shortest path length of one node to all other nodes in the network. In a faulty network, nodes that are close to high centrality are typically at core locations in the network. And fusing the centrality, the intermediate centrality and the near centrality, and calculating the comprehensive network center index of each node. By weighted averaging, analytic hierarchy process, or other comprehensive evaluation method. And determining the weight of the degree centrality, the medium centrality and the near centrality in the comprehensive index according to the specific condition and the analysis purpose of the power grid. And sequencing the nodes according to the comprehensive network center index of each node. And selecting the node with the highest comprehensive index as a first key regulation and control line. Is the most important node in a faulty network, as it has the greatest impact on the stability and safety of the grid. And analyzing lines directly connected with the key nodes according to the connection relation of the key nodes, wherein the lines are first key regulation lines.
Exemplary, a fault network is constructed, wherein the fault network nodes are A, B, C, D, E, the connection relations are A-B, A-C, B-D, C-E, the degree centrality, the medium centrality and the proximity centrality of each node are calculated, the degree centrality represents the connection number of one node, A:2 (connection B and C), B:2 (connection A and D), C:2 (connection A and E), D:1 (connection B), E:1 (connection C), the medium centrality represents the importance of one node on the shortest path, A:0.5, B:0.5, C:0.5, D:0, E:0, the proximity centrality represents the average distance between one node and other nodes, A: 1/2=0.5, B: 1/2.5=0.4, C: 1/2.5=0.4, D: 1/3=0.33, E: 1/3=0.33, the centrality index is normalized, and then the center index of each node is obtained through summation. And giving equal weight to each centrality index, connecting the network center indexes of each node according to the network center indexes as follows ：A：(0.5+0.5+0.5)/3=0.5;B：(0.5+0.5+0.4)/3=0.4667;C：(0.5+0.5+0.4)/3=0.4667;D：(0+0.33+0.33)/3=0.22;E：(0+0.33+0.33)/3=0.22,, acquiring the size of the key regulation and control line according to the network center indexes, and determining the most important nodes and the connection relation thereof. A-B (the network center index of A and B is higher) and A-C (the network center index of A and C is higher) are therefore critical regulatory circuits if the two critical regulatory circuits with the highest centrality are selected.
Further, the critical regulatory line acquisition module 16 is further configured to:
The method comprises the steps of obtaining the number of first line nodes and the number of second line nodes of the fault network, wherein the number of the first line nodes is the maximum number of nodes continuously connected with nodes in the fault network, the number of the second line nodes is the minimum number of nodes continuously connected with the nodes in the fault network, determining k values according to the number of the first line nodes and the number of the second line nodes, and then connecting the k nodes.
Specifically, in a failed network, a continuous connection refers to the number of furthest nodes that can be reached from one node through direct or indirect connections. The maximum number of nodes connected in succession in the faulty network is found. By traversing the network, the length of successive connections from each node is recorded and compared. The minimum number of nodes connected in succession in the faulty network is found. By traversing the network as well, the length of successive connections from each node is recorded and compared. The k value is set by a person skilled in the art depending on the specific situation of the grid and the analysis purpose. The k value is an integer representing the number of nodes to be connected. The k value should be chosen taking into account the impact of the fault on the grid stability. The larger the k value, the more nodes that are connected, and the wider the scope of influence of the fault may be. And selecting k continuously connected nodes for connection according to the number of the first line nodes and the number of the second line nodes. According to the selected node, a connection operation is performed, including changing a connection state of the line, adjusting parameters of the device, and the like. And after the connection operation is finished, monitoring the running state of the power grid, and evaluating the effect of the connection operation.
Further, the key regulation line obtaining module 16 is further configured to obtain a first key regulation line that meets a key constraint condition, where the key constraint condition is a network center index of k consecutive nodes and a network center index greater than a preset network center index, and the k consecutive nodes do not include an edge node.
In particular, the key constraints refer to the hub index of k consecutive nodes and the hub index that must be greater than a preset hub index. Traversing the fault network to find k continuous nodes meeting key constraint conditions. By traversing the network, the length of successive connections from each node is recorded and compared. It is ensured that no edge nodes, i.e. those nodes with only one connection, are included in the selected k consecutive nodes. For each k consecutive node combinations meeting the key constraint, the hub index sum thereof is calculated. And selecting a network center index and the largest node combination from all k continuous node combinations meeting key constraint conditions as a first key regulation and control line.
Further, the fault tracing module 14 is further configured to:
And performing fault tracing according to the multi-block operation data set, judging whether faults generated by each power transmission and transformation sub-network are mixed fault sources, wherein the mixed fault sources are two or more than two fault sources, identifying the power transmission and transformation sub-network with the mixed fault sources, outputting the identified power transmission and transformation sub-network, constructing a mixed fault network corresponding to the identified power transmission and transformation sub-network, and inputting the mixed fault network into the network center analysis model for network center analysis.
Specifically, using a multi-block operational dataset, the location and type of fault occurrence is determined by analyzing changes in parameters such as voltage, current, power, etc. And determining a fault source in each power transmission and transformation sub-network according to the fault tracing analysis. A fault source is considered to be a hybrid fault source if it affects multiple power transmission and transformation sub-networks simultaneously. In fault tracing analysis, the power transmission and transformation sub-networks affected by mixed fault sources are marked. And outputting the power transmission and transformation sub-networks affected by the mixed fault sources. And determining the influence range of the mixed fault source, namely the affected power transmission and transformation sub-network, according to the result of the fault tracing analysis. Using graph theory method, a hybrid fault network model is constructed that includes hybrid fault sources and affected power transmission and transformation sub-networks. And inputting the constructed mixed fault network into a network center analysis model. Network center indexes such as node computation degree center, medium center and approaching center in the mixed fault network. From the results of the hub analysis, critical nodes and critical lines in the hybrid fault network are determined, which are critical paths for fault propagation.
The exemplary power transmission and transformation network includes two blocks, and the multi-block operation data set includes a normal operation data a of a first block (node A, B, C), a failure data B, a failure data C, a failure data D of a second block (node D, E, F), a normal operation data E, a failure data F, and a failure data F, where each block is determined whether a hybrid failure source (two or more failure sources) exists, and the first block has a failure data B and C, so that the first block has a hybrid failure source. And only the node F fails in the second block, so that the second block has no mixed failure source. The first identification block is a power transmission and transformation sub-network with a mixed fault source. And constructing a corresponding mixed fault network for the identified power transmission and transformation sub-network (block one). The connection relationship is assumed to be A-B, A-C and B-C, the mixed fault network is input into a network center analysis model for network center analysis, and network center analysis is carried out on the constructed mixed fault network, wherein the network center analysis comprises calculation of degree centrality, medium centrality and near centrality of each node. The center degree is A:2 (connecting B and C), B:2 (connecting A and C), C:2 (connecting A and B), the center degree is A:0, B:1, C:1, the center degree is close to A:1, B:1, C:1, the center degree index is standardized, and the center degree index of each node is obtained through fusion. It is assumed that each centering index is given equal weights of a (2+0+1)/3=1, B (2+1+1)/3=1.33, C (2+1+1)/3=1.33, B and C have higher centering indexes, and a is relatively low, according to the centering index size order.
Further, the fault tracing module 14 is further configured to:
The method comprises the steps of obtaining N fault sources for identifying a power transmission and transformation sub-network, obtaining N fault networks corresponding to the N fault sources, fusing the N fault networks through a reset redundant node, and outputting a mixed fault network.
Specifically, fault tracing analysis is performed according to the multi-block operation data set, and a fault source of each power transmission and transformation sub-network is determined. If a fault source affects multiple power transmission and transformation sub-networks at the same time, the fault source is regarded as a mixed fault source. And according to the analysis result, N fault sources for identifying the power transmission and transformation sub-network are obtained, wherein N is a positive integer greater than or equal to 2. For each fault source, a fault network is constructed, and the fault network comprises an affected power transmission and transformation sub-network and a connection relation thereof. And constructing N corresponding fault networks according to the number of the fault sources. Among the N faulty networks, redundant nodes, i.e., nodes that are present in all of the multiple networks, are identified. For each redundant node, its connection relationship in the plurality of failed networks is reset to a unique connection relationship. And fusing the N fault networks after the reset to form a mixed fault network. Using graph theory method, a mixed fault network model is constructed which contains all fault sources and the affected power transmission and transformation sub-network. And outputting the constructed mixed fault network for subsequent analysis and processing. Through the steps, N fault sources for identifying the power transmission and transformation sub-network can be obtained, and corresponding N fault networks are constructed and fused to output a hybrid fault network, so that complex power grid fault conditions can be better understood and dealt with.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and the equivalent techniques thereof, the present application is also intended to include such modifications and variations.
Claims (8)
1. Power distribution regulation and control platform based on transmission and transformation risk prediction, its characterized in that includes:
The topology network acquisition module is used for acquiring a power transmission and transformation topology network;
the network partitioning module is used for partitioning the power transmission and transformation topological network according to a regulation and control source and outputting a plurality of power transmission and transformation sub-networks, wherein each power transmission and transformation sub-network is regulated and controlled by the same regulation and control source;
The operation monitoring module is used for monitoring the operation data of each power transmission and transformation sub-network in the plurality of power transmission and transformation sub-networks to obtain a multi-block operation data set;
the fault tracing module is used for tracing faults according to the multi-block operation data set and detecting a first fault source corresponding to each power transmission and transformation sub-network;
The fault network construction module is used for constructing a corresponding fault network according to the first fault source corresponding to each power transmission and transformation sub-network;
The key regulation circuit acquisition module is used for carrying out network center analysis on the fault network through a network center analysis model to acquire a first key regulation circuit corresponding to each power transmission and transformation sub-network;
And the power distribution regulation and control module is used for controlling the regulation and control sources corresponding to the power transmission and transformation sub-network to carry out power distribution regulation and control according to the first key regulation and control line.
2. The power distribution regulation platform based on power transmission and transformation risk prediction according to claim 1, wherein the fault network construction module is further configured to:
acquiring a connection relation in each power transmission and transformation sub-network;
performing fault propagation analysis on the first fault source according to the connection relation, acquiring nodes subjected to fault propagation in the power transmission and transformation sub-network, and calculating fault probability of each node;
And generating the fault network according to the fault probability value of each node and the node with the fault probability larger than the preset fault probability.
3. The power distribution regulation platform based on power transmission and transformation risk prediction according to claim 2, wherein the fault network construction module is further configured to:
defining a conditional probability table of each power transmission and transformation sub-network, wherein the conditional probability table is used for determining that the fault probability of each node in each power transmission and transformation sub-network depends on the state of a father node of the node, and each node is provided with an initial prior probability;
Setting an initial state of the conditional probability table according to the first fault source;
Based on the initial state of the conditional probability table, the posterior probability of the conditional probability table is calculated by using a Bayesian formula, and the fault probability of each node is output.
4. The power distribution regulation platform based on power transmission and transformation risk prediction according to claim 1, wherein the key regulation line acquisition module is further configured to:
Carrying out network center analysis on the fault network through a network center analysis model, and calculating the centrality, the betweenness centrality and the proximity centrality of each node;
Fusing according to the degree centrality, the medium centrality and the near centrality of each node, and outputting a network center index of each node;
and performing node connection according to the network center index of each node to obtain a first key regulation and control line.
5. The power distribution regulation platform based on power transmission and transformation risk prediction according to claim 4, wherein the key regulation line acquisition module is further configured to:
Obtaining the number of first line nodes and the number of second line nodes of the fault network, wherein the number of the first line nodes is the maximum number of nodes continuously connected with the nodes in the fault network, and the number of the second line nodes is the minimum number of nodes continuously connected with the nodes in the fault network;
And determining a k value according to the number of the first line nodes and the number of the second line nodes, and then connecting the k nodes.
6. The power distribution regulation platform based on power transmission and transformation risk prediction according to claim 5, wherein the key regulation line acquisition module is further configured to acquire a first key regulation line that meets a key constraint condition, where the key constraint condition is a network center index of k consecutive nodes and a network center index greater than a preset network center index, and the k consecutive nodes do not include an edge node.
7. The power distribution regulation platform based on power transmission and transformation risk prediction according to claim 1, wherein the fault tracing module is further configured to:
Performing fault tracing according to the multi-block operation data set, and judging whether the faults generated by each power transmission and transformation sub-network are mixed fault sources, wherein the mixed fault sources are two or more than two fault sources;
identifying the power transmission and transformation sub-network with the mixed fault source, and outputting an identification power transmission and transformation sub-network;
Constructing a mixed fault network corresponding to the identification power transmission and transformation sub-network;
And inputting the mixed fault network into the network center analysis model for network center analysis.
8. The power distribution regulation platform based on power transmission and transformation risk prediction according to claim 7, wherein the fault tracing module is further configured to:
N fault sources for identifying a power transmission and transformation sub-network are obtained, wherein N is a positive integer greater than or equal to 2;
acquiring N fault networks corresponding to the N fault sources;
and fusing the N fault networks through resetting the redundant nodes, and outputting a mixed fault network.
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| CN104537487A (en) * | 2014-12-25 | 2015-04-22 | 云南电网公司电力科学研究院 | Assessment method of operating dynamic risk of electric transmission and transformation equipment |
| CN112072657A (en) * | 2020-09-15 | 2020-12-11 | 国网山西省电力公司经济技术研究院 | Cascading failure risk assessment method and system for flexible interconnected power distribution system |
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| CN112688431A (en) * | 2020-12-28 | 2021-04-20 | 国家电网有限公司 | Power distribution network load overload visualization method and system based on big data |
| CN116505665B (en) * | 2023-06-30 | 2023-09-22 | 国网江苏省电力有限公司南通供电分公司 | Fault monitoring method and system for power grid distribution line |
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| CN104537487A (en) * | 2014-12-25 | 2015-04-22 | 云南电网公司电力科学研究院 | Assessment method of operating dynamic risk of electric transmission and transformation equipment |
| CN112072657A (en) * | 2020-09-15 | 2020-12-11 | 国网山西省电力公司经济技术研究院 | Cascading failure risk assessment method and system for flexible interconnected power distribution system |
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