Power network substation target optimization method based on multi-dimensional indexes

Abstract

The invention discloses a multi-dimensional index-based power network substation target optimization method. The method comprises the following technical steps of 1) performing condition information investigation; 2) based on a complex network theory, constructing a power network weighted topological graph reflecting a network frame topological relation and electrical distance characteristics; 3) based on a graph theory and a graph maximum flow theory, constructing a power network energy flow graph reflecting the carrying capacity of the power network; 4) on the basis of a graph theory and a complex network theory, six transformer substation key quantitative evaluation indexes are sequentially calculated for each transformer substation of the power system in the target region; 5) calculating the entropy value of each key index based on an entropy weight algorithm, and further calculating a multi-index entropy weight, and 6) on the basis of the transformer substation key quantitative evaluation indexes in the step 4) and the index entropy weight in the step 5), carrying out multi-dimensional key index comprehensive evaluation, and arranging the comprehensive evaluation index values from large to small to form an optimal list of power network transformer substation paralysis.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system automation, and in particular relates to a method for optimizing power network substation targets based on multi-dimensional indicators. Background Art

[0002] Given the importance of power systems to modern society, attacking and protecting them is becoming increasingly crucial. Power networks are comprised of numerous power plants, hub substations, load substations, and the transmission lines between them. When multi-target power paralysis attacks are needed to target a region or multiple loads, even if the target region and the substations supplying the loads are known, attacking each one individually would consume significant resources.

[0003] The following problems exist in existing power system attack and defense technologies:

[0004] 1. It is difficult to effectively identify key nodes in the power network. With limited information, it is impossible to accurately determine which substations are most important to the operation of the entire power system.

[0005] 2. There is a lack of systematic analysis of the overall structure and function of the power network. It is difficult to fully assess the importance of substations based on a single indicator.

[0006] 3. The complexity of the power network makes it difficult to accurately assess the impact of a single substation failure on the entire system.

[0007] In order to achieve accurate paralysis of the power system and efficient restriction of target areas and power supply guarantee of facilities, a target optimization method for power network substations based on multi-dimensional indicators is urgently needed. Summary of the Invention

[0008] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method for optimizing the target of power network substations based on multi-dimensional indicators, which can achieve accurate paralysis of the power system and efficient restriction of target areas and power supply guarantees for facilities.

[0009] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0010] In a first aspect, the present invention provides a method for selecting a target substation in a power network based on multi-dimensional indicators, comprising the following steps:

[0011] S1: Obtain electrical information and geographical distribution information of the power system in the target area;

[0012] S2: Based on complex network theory and the electrical information and geographical distribution information, a weighted topological model of the power network is constructed to reflect the grid topological relationship and electrical distance characteristics;

[0013] S3: Based on graph theory and graph maximum flow theory, and according to the weighted topology model of the power network, a power network energy flow model that reflects the carrying capacity of the power network is constructed;

[0014] S4: Based on graph theory and complex network theory, according to the power network weighted topology model and power network energy flow model, calculate the value of the key quantitative evaluation index of the substation for each substation in the power system of the target area;

[0015] S5: Based on the entropy weight algorithm, the entropy value of each substation's key quantitative evaluation indicators is calculated and the multi-indicator entropy weight is further calculated;

[0016] S6: Based on the calculated values ​​of the substation critical quantitative evaluation indicators and the multi-indicator entropy weights, a comprehensive evaluation of the multi-dimensional key indicators is performed, and the targets of the power network substation paralysis are arranged from large to small according to the comprehensive evaluation indicator values, and a preferred list of targets that may cause paralysis is output.

[0017] Furthermore, the electrical information and geographical distribution information include information on power plants, substations or switch stations, transmission lines, and power loads.

[0018] Furthermore, electrical information and geographical distribution information of the power system in the target area are obtained, including:

[0019] S1-1: Obtain information about a power plant, including the power plant name, primary energy type, single unit capacity, total installed capacity, longitude, and latitude;

[0020] S1-2: Obtain information about a substation or switch station, including the station name, voltage level, longitude, and latitude.

[0021] S1-3: Acquire information about the transmission line, including: line name, voltage level, number of parallel circuits, line length, head end station, and end station;

[0022] S1-4: Obtaining power load information, the power load information includes: load name, load type, maximum power consumption, upstream power supply substation, longitude, and latitude.

[0023] Furthermore, based on complex network theory and the electrical information and geographical distribution information, a weighted topological model of the power network is constructed to reflect the grid topological relationship and electrical distance characteristics, including:

[0024] Step S2-1: Constructing a node set:

[0025] Sort out all substations in the regional power network and build nodes for each i-th substation , traverse all substations to form a node set ,here Indicates the total number of substations.

[0026] Step S2-2: Construct edge set:

[0027] On the basis of building the node set, the edges between nodes are constructed according to the connection relationship between the transmission line and the substation. For the edges of the same voltage level, the line length is used as its weight, and the edge weight is calculated according to the following formula:

[0028] ;

[0029] Where, represents the edge weight of the kth transmission line between substations i and j, represents the physical length of the transmission line, Indicates the voltage level of the transmission line. Indicates the highest voltage level of the regional power network.

[0030] For any transmission line, calculate the weight according to the above formula and add the corresponding edge, traverse all transmission lines to form an edge set .

[0031] Step S2-3: Form a weighted topology model of the power network based on the node set and edge set .

[0032] Furthermore, based on graph theory and graph maximum flow theory, and according to the weighted topology model of the power network, a power network energy flow model reflecting the carrying capacity of the power network is constructed, including:

[0033] Step S3-1: Constructing the flow network:

[0034] According to the power network weighted topology model, power plants and substations are abstracted as nodes of the power grid, transmission lines are abstracted as edges of the power grid, and power flows are transmitted from generator nodes to load nodes via the edges of the power grid.

[0035] Virtual source nodes and virtual sink nodes are introduced. Virtual edges are added between the virtual source nodes and each power plant node to establish a connection relationship. These virtual edges are defined as unidirectional, allowing power flow only from the virtual source node to the power plant node. The capacity of these virtual edges is equivalent to the available generating capacity of the corresponding power plant.

[0036] A virtual edge is added between the virtual sink and each load substation to realize the connection relationship. The virtual edge is defined as a unidirectional edge, which only allows power flow to flow from the load substation to the virtual sink. The capacity of the virtual edge is equivalent to the actual power demand of each load substation at each moment.

[0037] Step S3-2: Based on the Ford-Fulkerson algorithm, solve the maximum energy flow of the power network:

[0038] Step 1: Setup , set each node label function L and queue node set , set Q is a sequential set and must satisfy the first-in-first-out principle;

[0039] Step 2: Take , find the node connected to i along the associated edge , and , then remember , , ,and and , then remember , ; After the elements in J are traversed one by one, mark i as checked;

[0040] Where i: the node currently being processed;

[0041] s: Source node, which is the starting point of the flow network;

[0042] j: neighbor node directly connected to node i (connected by forward or reverse edge);

[0043] f(i,j): actual flow on edge (i,j) (current value);

[0044] c(i,j): capacity (maximum allowed flow) of edge (i,j);

[0045] L j : The label of node j, which is used to record its predecessor node and augmentation direction;

[0046] flow: the maximum flow that can be increased in the current augmenting path;

[0047] L t : The label of the target node (sink t).

[0048] Step 3: Repeat Step 2 until or All have been checked;

[0049] Step 4: If , the algorithm terminates, all saturated edges form the minimum cut set of the network, and the sum of the capacities of the saturated edges is the maximum flow of the network; otherwise, an augmenting chain p is found along the label L, and the adjustment method is as follows: , ,and , . After execution, return to Step 2.

[0050] Step S3-3: Based on the maximum energy flow calculated in step S3-2, the maximum power supply capacity of the target power system under the given grid is obtained;

[0051] If the minimum cut falls on the edge set between the virtual source point and the power plant node, it means that the total available power generation capacity at that moment is less than the load demand power, that is, the power supply is insufficient.

[0052] If the minimum cut falls within the edge set of the power network, it means that the power transmission of the minimum cut surface is limited due to insufficient power transmission capacity of the power network at that moment.

[0053] If the minimum cut falls on the edge set between the load substation and the virtual sink, the maximum flow of the power network is equal to the total load demand power, that is, the power supply can meet the load demand.

[0054] Furthermore, the key quantitative evaluation indicators of the substation include grid structure importance indicators and performance degradation degree indicators;

[0055] The grid structure importance indicators include node centrality indicators, node power degree indicators and network clustering coefficient indicators; the performance degradation indicators include connectivity indicators, shortest path change rate indicators and power supply capacity degradation indicators.

[0056] Furthermore, based on graph theory and complex network theory, and according to the power network weighted topology model and the power network energy flow model, the values ​​of key quantitative evaluation indicators of substations are calculated for each substation in the power system of the target area, including:

[0057] S4-1: Calculate the node betweenness centrality index, which is the ratio of the shortest path passing through the node to the number of all shortest paths. The calculation formula is as follows:

[0058] ;

[0059] Where, It represents the number of shortest paths connecting the power source node s and the load node t that pass through the node i. It represents the total number of shortest paths connecting the power node s and the load node t.

[0060] S4-2: Obtain the node power index, which indicates the number of neighboring nodes directly connected to the node. The calculation formula is as follows:

[0061] ;

[0062] Where, represents the set of downstream neighbor nodes j of node i. represents the active power flowing from node i to its neighbor node j; if the actual active power calculated by the maximum flow model is flowing from node j to node i, then And it is not included in the calculation of the node power degree index. represents the active power injected into the power network by the g-th power plant, Represents the total number of power plants of each type in the power network.

[0063] S4-3: Calculate the node clustering coefficient index, which quantifies the degree to which the central node i and its first-order neighbor nodes form a tightly connected group. Its mathematical description is the ratio of the closed triangle formed by the node i and its first-order neighbor nodes. The node clustering coefficient The calculation formula is as follows:

[0064] ;

[0065] Where, represents the first-order neighbor node of the node i, Indicates that node j and node k are both first-order neighbor nodes of node i, and An edge represents a direct connection between two first-order neighbor nodes j and k.

[0066] S4-4: Calculate the connectivity index, which represents the interoperability of the remaining nodes after the node fails. The connectivity index is described by the source-load imbalance within the electrical island. , and its calculation formula is shown as follows:

[0067] ;

[0068] Where, represents the active load on load substation l, L represents the total number of load substations; D represents the number of electrical islands formed in the power network after the failure of node i. If the power network is still connected after the failure of node i, then D=1. It means that substation k belongs to the jth electrical island after node i fails. and They represent the active load and active power connected to substation k respectively. Indicates an operation to obtain a large number.

[0069] S4-5: Calculate the shortest path change rate index, which is defined as the change rate of the average shortest path distance between the source and load of the power network after any node i fails. The calculation formula is as follows:

[0070] ;

[0071] Where, and Respectively represent the collection of power source nodes and load substation nodes in the power network, and They represent the shortest path distances of the source-load node pair st in the complete grid and in the network after substation i fails, respectively, which are calculated using the Dijkstra algorithm in the weighted topology graph.

[0072] S4-6: Calculate the power supply capacity degradation index, power supply capacity degradation index The calculation formula is as follows:

[0073] ;

[0074] Where, Indicates the power supply capacity under the complete grid condition. represents the power supply capacity of the power network after the failure of the i-th substation;

[0075] The betweenness centrality index, node power degree index, node clustering coefficient index, connectivity index, shortest path change rate index and power supply capacity degradation index are all positive indicators. The larger the index value, the more important the substation is in the topological structure of the power network.

[0076] Furthermore, based on the entropy weight algorithm, the entropy value of each substation's key quantitative evaluation index is calculated and the multi-index entropy weight is further calculated, including:

[0077] Step S5-1: Assume that there are m substation objects and n evaluation indicators. After the indicator calculation, the indicator set is obtained. , where the index set of the i-th object is , and define the sample set of the jth index as .

[0078] like If it is a positive indicator, the standardization preprocessing is performed as follows:

[0079] ;

[0080] like If it is a negative indicator, the standardization preprocessing is performed as follows:

[0081] ;

[0082] In the above formula, and Represents the jth index respectively The maximum and minimum values ​​in the sample set.

[0083] After preprocessing, the new indicator set is .

[0084] Step S5-2: For the jth index after normalization , calculate the i-th substation sample in turn The proportion in this indicator set :

[0085] ;

[0086] ;

[0087] Step S5-3: According to the definition of information entropy, calculate the information entropy of the j-th indicator in sequence:

[0088] ;

[0089] ;

[0090] Among them, when When, define .therefore, Heng is established.

[0091] Step S5-4: Through information entropy , calculate the weight of each indicator according to the following formula :

[0092] ;

[0093] .

[0094] Furthermore, based on the calculated values ​​of the substation critical quantitative evaluation indicators and the multi-indicator entropy weights, a comprehensive evaluation of the multi-dimensional key indicators is performed, and the targets of the power network substation paralysis are outputted in a descending order of the comprehensive evaluation indicator values, including:

[0095] After calculating the weights of each indicator, the indicators are integrated according to the following formula to obtain the comprehensive evaluation index value:

[0096] ;

[0097] ;

[0098] Where, That is the comprehensive evaluation index value of the i-th substation;

[0099] After calculating the comprehensive evaluation index value of each node, the optimal list of power network substation paralysis targets is obtained by arranging them from large to small.

[0100] In a second aspect, the present invention provides a device for optimizing a target of a power network substation based on multi-dimensional indicators, comprising a processor and a storage medium;

[0101] The storage medium is used to store instructions;

[0102] The processor is configured to operate according to the instructions to execute the steps of the method according to the first aspect.

[0103] Compared with the prior art, the present invention has the following beneficial effects:

[0104] 1. This invention discloses a method for optimizing substation targets in a power network based on multi-dimensional indicators. Based on complex network theory and graph theory, this method constructs a weighted topological model of the power network that reflects the electrical connections between power facilities, and a maximum flow model of the power network that reflects the maximum power supply capacity of the power system. These models can analyze the impact and criticality of any substation and transmission line on the overall power transmission capacity of the power system.

[0105] 2. Based on the constructed model, a comprehensive multi-metric evaluation is conducted based on the importance of the grid structure and the degree of performance degradation. Grid structure importance metrics include node centrality, node power degree, and network clustering coefficient, while performance degradation metrics include connectivity, shortest path change rate, and power supply capacity degradation. Finally, using an entropy weighting algorithm, the weights of each key metric are calculated to achieve a quantitative evaluation of substation criticality, ultimately outputting a prioritized list of power network substation failure targets.

[0106] 3. The method of the present invention can be used to prioritize key strike targets in power systems with limited intelligence data, effectively identifying key links in the enemy's power system and providing a basis for decision-making to restrict the enemy's power supply. Furthermore, the method of the present invention can also be used to identify key links in one's own power system. By implementing proactive protective measures such as physical reinforcement and cyber defense at these key links, it can help improve the survivability and continuous power supply capabilities of one's own power system in emergency situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] Figure 1 is a flow network diagram of the power grid;

[0108] Figure 2This is a schematic diagram of the power flow network when the power supply side is insufficient;

[0109] Figure 3 This is a schematic diagram of the power flow network when the grid side capacity is limited;

[0110] Figure 4 This is a schematic diagram of the power flow network when the load power demand is fully met;

[0111] Figure 5 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0112] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0113] Example 1:

[0114] The present invention provides a method for optimizing substation targets in a power network based on multi-dimensional indicators. The main technical steps of the method are as follows:

[0115] 1) Through intelligence research, collect information on power plants, substations or switch stations, transmission lines, land use characteristics, and major power loads in the target area's power system;

[0116] 2) Based on complex network theory, construct a weighted topological diagram of the power network that reflects the grid topology and electrical distance characteristics;

[0117] 3) Based on graph theory and graph maximum flow theory, construct a power network energy flow diagram that reflects the power network's carrying capacity;

[0118] 4) Based on graph theory and complex network theory, six key quantitative evaluation indicators of substations are calculated for each substation in the power system of the target area;

[0119] 5) Based on the entropy weight algorithm, calculate the entropy value of each key indicator and further calculate the entropy weight of multiple indicators;

[0120] 6) Based on the quantitative evaluation indicators of substation keyness in step 4) and the entropy weight of indicators in step 5), a comprehensive evaluation of multi-dimensional key indicators is conducted, and the indicators are arranged from large to small according to the comprehensive evaluation indicator values ​​to form a preferred list of power network substation paralysis.

[0121] Specifically, this embodiment provides a method for selecting a target for a power network substation based on multi-dimensional indicators, such as Figure 5 As shown, the following steps are included:

[0122] S1: Through intelligence research, collect electrical information and geographical distribution information such as power plants, substations or switch stations, transmission lines, and power loads in the target area's power system;

[0123] S2: Based on complex network theory, a weighted topological model of the power network is constructed to reflect the grid topology relationship and electrical distance characteristics;

[0124] S3: Based on graph theory and graph maximum flow theory, construct a power network energy flow model that reflects the power network's carrying capacity;

[0125] S4: Based on graph theory and complex network theory, six key quantitative evaluation indicators of substations are calculated for each substation in the power system of the target area;

[0126] S5: Based on the entropy weight algorithm, calculate the entropy value of each key indicator and further calculate the entropy weight of multiple indicators;

[0127] S6: Based on steps S4 and S5, a comprehensive evaluation of multi-dimensional key indicators is performed, and the targets of power network substation paralysis are arranged from large to small according to the comprehensive evaluation indicator values, and a preferred list of targets is output.

[0128] Step S1 includes: collecting electrical information and geographical distribution information such as power plants, substations or switch stations, transmission lines, and power loads of the power system in the target area through intelligence research, specifically:

[0129] S1-1: Power Plant Information

[0130] The information of the required power plants is shown in Table 1:

[0131] Table 1 Information table of required power plants

[0132]

[0133] S1-2: Substation or switch station information:

[0134] The information of the required substation or switch station is shown in Table 2:

[0135] Table 2 Information of required substation or switch station

[0136]

[0137] S1-3: Transmission line information:

[0138] The information of the required transmission lines is shown in Table 3:

[0139] Table 3 Information of required transmission lines

[0140]

[0141] S1-4: Power load:

[0142] The information of the required power load is shown in Table 4:

[0143] Table 4 Information table of required power load

[0144]

[0145] The step S2 includes: constructing a weighted topology model of the power network that reflects the grid topology relationship and electrical distance characteristics based on complex network theory, specifically:

[0146] Step S2-1: Constructing a node set

[0147] Sort out all substations in the regional power network and build nodes for each i-th substation , traverse all substations to form a node set ,here Indicates the total number of substations.

[0148] Step S2-2: Construct edge set

[0149] On the basis of constructing the node set, the edges between the nodes are constructed according to the connection relationship between the transmission lines and the substations. Conventional weighted topology models generally use the reactance parameters of the transmission lines as the weights of the edges, but in the actual application of power system attack and defense, it is often difficult to obtain the precise parameters of each transmission line. On the other hand, the physical parameters such as resistance and reactance per unit length of transmission lines of the same voltage level are similar. Therefore, for the edges of the same voltage level, the line length can be used as its weight. Furthermore, in the calculation of power grid flow, the resistance, reactance and other parameters of the transmission lines are generally converted to the per-unit value based on the highest voltage level for calculation. Analogously to the per-unit value calculation process, the edge weight can be calculated according to the following formula:

[0150] ;

[0151] Where, represents the edge weight of the kth transmission line between substations i and j (the introduction of k is mainly due to the fact that there may be multiple parallel transmission lines between substations), represents the physical length of the transmission line, Indicates the voltage level of the transmission line (in kV), Indicates the highest voltage level of the regional power network (in kV).

[0152] For any transmission line, calculate the weight according to the above formula and add the corresponding edge, traverse all transmission lines to form an edge set .

[0153] Step S2-3: Forming a weighted graph .

[0154] Finally, based on the node set and edge set, a weighted topological model of the power network is formed. .

[0155] The step S3 includes: constructing a power network energy flow model reflecting the power network's carrying capacity based on graph theory and graph maximum flow theory, specifically:

[0156] Power network power flow calculations, both AC and DC, have established theoretical foundations and algorithms. However, these require high model parameters for each branch of the power network, making them difficult to meet in practical applications of power system attack and defense. However, given that practical applications of power system attack and defense primarily focus on ensuring the supply of target loads in a given region, the power network primarily plays a role in path dependency and transmission capacity constraints. To address these requirements, the maximum flow algorithm from graph theory can be used for approximate modeling, eliminating the need to construct a precise mathematical model of the power network.

[0157] Step S3-1: Constructing the flow network

[0158] The power grid has a graph structure. Power plants and substations can be abstracted as nodes, transmission lines as edges, and power flows are transmitted from generator nodes to load nodes along the edges. However, existing algorithms such as the Ford-Fulkerson algorithm are generally only applicable to maximum flow problems in single-source, single-sink networks. Real power grids typically have numerous power plants and load substations. Obviously, each power plant is a source of injected energy, while each load substation is a sink. This means that the power grid is a multi-source, multi-sink network.

[0159] To this end, virtual source points and virtual sink points are introduced. A new virtual edge is added between the virtual source point and each power plant node to realize the connection relationship. At the same time, the virtual edge is defined as a unidirectional edge, which only allows the power flow to flow from the virtual source point to the power plant node, and the capacity of the virtual edge is equivalent to the available power generation capacity of the corresponding power plant. At the same time, a new virtual edge is added between the virtual sink point and each load substation to realize the connection relationship. Similarly, the virtual edge is defined as a unidirectional edge, which only allows the power flow to flow from the load substation to the virtual sink point, and the capacity of the virtual edge is equivalent to the actual power demand power of each load substation at each moment. Specifically, Figure 1 shown.

[0160] Figure 1 middle, It represents the available power generation capacity of power station G1 and is also the capacity of the one-way edge from the virtual source point to the power station node. Represents the power capacity of the transmission line between hub substations T1 and T4 as the capacity of this bidirectional edge. It represents the active load demand of the load substation L1 at a certain moment, as the capacity of the one-way edge from the load substation to the virtual load point.

[0161] Step S3-2: Based on the Ford-Fulkerson algorithm, solve the maximum energy flow of the power network.

[0162] The basic idea of ​​the Ford-Fulkerson algorithm is to find an augmenting chain under the constraint of flow conservation. When no augmenting chain can be obtained, the algorithm terminates. The specific process is as follows:

[0163] Step 1: Set , set each node label function L and queue node set (Set Q is a sequential set and must satisfy the first-in-first-out principle);

[0164] Step 2: Take , find the node connected to i along the associated edge , and , then remember , , ,and and , then remember , ; After the elements in J are traversed one by one, mark i as checked;

[0165] Where i: the node currently being processed;

[0166] s: Source node, which is the starting point of the flow network;

[0167] j: neighbor node directly connected to node i (connected by forward or reverse edge);

[0168] f(i,j): actual flow on edge (i,j) (current value);

[0169] c(i,j): capacity (maximum allowed flow) of edge (i,j);

[0170] L j : The label of node j, which is used to record its predecessor node and augmentation direction;

[0171] flow: the maximum flow that can be increased in the current augmenting path;

[0172] L t : The label of the target node (sink t).

[0173] Step 3: Repeat Step 2 until or All have been checked;

[0174] Step 4: If , the algorithm terminates, all saturated edges form the minimum cut set of the network, and the sum of the capacities of the saturated edges is the maximum flow of the network; otherwise, an augmenting chain p is found along the label L, and the adjustment method is as follows: , ,and , . After execution, return to Step 2.

[0175] Step S3-3: Based on the maximum energy flow calculated in step S3-2, the maximum power supply capacity of the target power system under the given grid is obtained.

[0176] By introducing virtual sources and sinks, the traditional Ford-Fulkerson algorithm is adapted to the problem of evaluating the power supply capacity of power networks. It is worth noting that for this problem, the algorithm results may appear in three situations, as explained below:

[0177] The minimum cut falls on the edge set between the virtual source point and the power plant node, such as Figure 2 As shown in Figure 2, this means that the total available power generation capacity at this moment is less than the load demand power, that is, the power supply is insufficient.

[0178] The minimum cut falls on the edge set of the power network, such as Figure 3 As shown, this means that at this point in time, the power transmission capacity of the power network is insufficient, and the power transmission of the minimum cut-off surface is limited. This situation often occurs when the power network is deliberately attacked or some hub substations or transmission lines are shut down.

[0179] The minimum cut falls on the edge set between the load substation and the virtual sink, such as Figure 4 As shown in Figure 2, the maximum flow of the power network is equal to the total load demand power, that is, the power supply can meet the load demand.

[0180] Through maximum flow calculation, the flow rate on the edge between the load substation and the virtual sink is the actual power supply at that point in time. The difference between the supplied power and the demanded power is the load loss at the corresponding load substation. The flow rate on the edge between the virtual source and the power plant node is the active power output of the power plant at that point in time. For coal-fired, gas-fired, and oil-fired power plants, this active power output can be used to calculate the primary energy consumption for the corresponding period and update the primary capacity reserve of the thermal power plant.

[0181] The step S4 includes: based on graph theory and complex network theory, calculating six key quantitative evaluation indicators of substations for each substation in the power system of the target area, specifically:

[0182] Based on graph theory and complex network theory, this paper conducts a comprehensive multi-metric evaluation based on two aspects: grid structure importance and performance degradation. Grid structure importance indicators include node centrality, node power degree, and network clustering coefficient, while performance degradation indicators include connectivity, shortest path change rate, and power supply capacity degradation. The following provides a detailed explanation of the definitions and mathematical expressions of each of these indicators.

[0183] S4-1: Node betweenness centrality index

[0184] Betweenness Centrality is the ratio of the shortest path passing through the node to the number of all shortest paths. The shortest path is defined as the path with the smallest total weight among all paths between node s and node t. Considering that power flows from power nodes to load nodes, this project, unlike traditional complex network analysis that identifies the shortest path between any two nodes, only identifies the shortest path of any power-load node pair in the weighted topology graph, reflecting the role of substations as energy transfer hubs for power supply. Node Betweenness Index The calculation formula is as follows:

[0185] ;

[0186] Where, It represents the number of shortest paths connecting the power source node s and the load node t that pass through the node i. It represents the total number of shortest paths connecting the power node s and the load node t.

[0187] A higher betweenness centrality index for a node (substation) indicates that the node is located on a main branch of the source-load electrical connection, meaning that the node has a significant impact on power transmission efficiency. Therefore, betweenness centrality is a positive indicator; a higher value indicates a more important substation in the topology of the power network.

[0188] S4-2: Node power degree indicator

[0189] The node degree (Degree) indicates the number of neighboring nodes directly connected to the node. This project improves the traditional node degree index and proposes the node power degree index, which is defined as the ratio of the active power flowing out of the node to the total active power flow of the power network. The calculation formula is as follows:

[0190] ;

[0191] Where, represents the set of downstream neighbor nodes j of node i. represents the active power flowing from node i to its neighbor node j; if the actual active power calculated by the maximum flow model is flowing from node j to node i, then And it is not included in the calculation of the node power degree index. represents the active power injected into the power network by the g-th power plant, Represents the total number of power plants of each type in the power network.

[0192] A higher node power index for a node (substation) indicates that the hub or load substation carries a higher power transmission capacity. A failure at this node could cause a large-scale power flow shift, increasing the probability of cascading failures and widespread power outages. Therefore, the node power index is a positive indicator; higher values ​​indicate a more important substation in the power grid topology.

[0193] S4-3: Node clustering coefficient indicator

[0194] The node clustering coefficient quantifies the degree to which the central node i and its first-order neighbor nodes form a tightly connected group. Its mathematical description is the ratio of the closed triangle formed by the node i and its first-order neighbor nodes. The calculation formula is as follows:

[0195] ;

[0196] Where, represents the first-order neighbor node of the node i, Indicates that node j and node k are both first-order neighbor nodes of node i, and An edge represents a direct connection between two first-order neighbor nodes j and k.

[0197] The node clustering coefficient quantifies the closeness of connections between node groups. Applicable to power grid issues, if a large number of edges exist between node groups and the connections are close, then when node i fails, the more backup paths its neighboring nodes have, the smaller the impact on power supply reliability. Conversely, if the connections between node groups are weak, or in the extreme case where all neighboring nodes are connected only to node i and no edges exist between neighboring nodes, then when node i fails, its neighboring nodes may lack backup paths and lose power. Therefore, the node clustering coefficient is a negative indicator. A higher value indicates that the failure of a node has a smaller impact on the power grid's power supply capacity, and therefore the substation's importance is relatively low.

[0198] S4-4: Connectivity indicators

[0199] Connectivity refers to the interoperability of the remaining nodes after a node fails. For a power network, if a node (substation) fails and causes the grid to disconnect, the two or more electrical islands formed after the disconnection may have insufficient power generation capacity to support the power load demand, which will result in a loss of load. The connectivity index itself is a binary result of 0-1, but it cannot reflect the source-load balance capability within each electrical island. Therefore, this project uses the source-load imbalance within the electrical island to describe the connectivity index. , and its calculation formula is shown as follows:

[0200] ;

[0201] Where, represents the active load on load substation l, L represents the total number of load substations; D represents the number of electrical islands formed in the power network after the failure of node i. If the power network is still connected after the failure of node i, then D=1. It means that substation k belongs to the jth electrical island after node i fails. and They represent the active load and active power connected to substation k respectively. Indicates an operation to obtain a large number.

[0202] The connectivity index, which considers the source-load imbalance within an electrical island, reflects the impact of node i on the connectivity of the power network and the degree of matching between the source and load distribution after its failure. A higher connectivity index value indicates a greater source-load mismatch, resulting in a larger load loss. Therefore, the connectivity index is a positive indicator; a higher value indicates a more important node.

[0203] S4-5: Shortest path change rate indicator

[0204] The Shortest Path Change Rate (SPCR) is defined as the rate of change of the average shortest path distance between the source and load of the power network after any node i fails. Similar to metric 1, since power flows unidirectionally from the source node to the load node, only the shortest path between the source and load node pairs is considered in the calculation of the shortest path. The calculation formula is as follows:

[0205] ;

[0206] Where, and Respectively represent the collection of power source nodes and load substation nodes in the power network, and They represent the shortest path distances of the source-load node pair st in the complete grid and in the network after substation i fails, respectively, which are calculated using the Dijkstra algorithm in the weighted topology graph.

[0207] A larger shortest path change rate indicates that the electrical distance between the source and the load increases after node i fails, and transmission efficiency decreases. Therefore, the shortest path change rate is a positive indicator; the larger its value, the more important the corresponding substation is in the power network topology.

[0208] S4-6: Power supply capacity degradation indicator

[0209] Using the maximum flow-based power network power supply capacity evaluation algorithm proposed in step S3, the power supply capacity degradation index is defined as the rate of change of the power supply capacity of the power network after any substation fails and the power supply capacity under the complete network condition. The calculation formula is as follows:

[0210] ;

[0211] Where, Indicates the power supply capacity under the complete grid condition. It represents the power supply capacity of the power network after the failure of the i-th substation.

[0212] The larger the power supply capacity degradation index, the lower the power network's power flow transmission capacity will be after the failure of the i-th substation, which is not conducive to load supply. Therefore, the power supply capacity degradation index is a positive indicator. The larger the index value, the more important the corresponding substation.

[0213] The step S5 includes: calculating the entropy value of each key indicator and further calculating the multi-indicator entropy weight based on the entropy weight algorithm, specifically:

[0214] The indicators proposed in step S4 differ in numerical scale. Another key aspect of multi-indicator comprehensive evaluation is determining the weights of each indicator within the evaluation. Compared to subjective weighting methods like the Analytic Hierarchy Process (AHP), the entropy weighting method can measure the degree to which an indicator distinguishes the evaluation object and objectively assign weights to each indicator based on this degree of discrimination. Therefore, the entropy weighting method is used to implement a comprehensive multi-indicator evaluation, thereby determining a ranking of substation importance.

[0215] The calculation process of the multi-index comprehensive evaluation model based on the entropy weight method is explained as follows:

[0216] Step S5-1: Data normalization

[0217] Suppose there are m objects to be evaluated (i.e. substations) and n evaluation indicators. After the indicator calculation, the indicator set is obtained: , where the index set of the i-th object is , and define the sample set of the jth index as .

[0218] like If it is a positive indicator, the standardization preprocessing is performed as follows:

[0219] ;

[0220] like If it is a negative indicator, the standardization preprocessing is performed as follows:

[0221] ;

[0222] In the above formula, and Represents the jth index respectively The maximum and minimum values ​​in the sample set.

[0223] After preprocessing, the new indicator set is .

[0224] Step S5-2: Calculate the weight of each indicator under each solution

[0225] For the jth indicator after standardization , calculate the sample of the i-th object (i.e. substation) in sequence The proportion in this indicator set :

[0226] ;

[0227] ;

[0228] Step S5-3: Calculate the information entropy of each indicator

[0229] According to the definition of information entropy, the information entropy of the j-th indicator is calculated in sequence:

[0230] ;

[0231] ;

[0232] Among them, when When, define .therefore, Heng is established.

[0233] Step S5-4: Calculate the weight of each indicator

[0234] Through information entropy , calculate the weight of each indicator according to the following formula :

[0235] ;

[0236] ;

[0237] The step S6 includes: based on the steps S4 and S5, performing a comprehensive evaluation of the key indicators in multiple dimensions, arranging the comprehensive evaluation indicators from large to small, and outputting a preferred list of power network substation paralysis targets, specifically:

[0238] After calculating the weight of each indicator, the indicators can be integrated according to the following formula to obtain the comprehensive evaluation index value:

[0239] ;

[0240] ;

[0241] Where, That is the comprehensive evaluation index value of the i-th substation.

[0242] After calculating the comprehensive evaluation index value of each node, the substation paralysis list is obtained by arranging them from large to small.

[0243] This invention discloses a method for optimizing substation targets in power networks based on multi-dimensional indicators. Based on complex network theory and graph theory, this method constructs a weighted topological model of the power network that reflects the electrical connections between power facilities, and a maximum flow model that reflects the maximum power supply capacity of the power system. These models can analyze the impact and criticality of any substation or transmission line on the overall power transmission capacity of the power system.

[0244] Example 2:

[0245] This embodiment provides a device for optimizing a target of a power network substation based on multi-dimensional indicators, including a processor and a storage medium;

[0246] The storage medium is used to store instructions;

[0247] The processor is configured to operate according to the instructions to execute the steps of the method according to embodiment 1.

[0248] Example 3:

[0249] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method described in Example 1 are implemented.

[0250] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0251] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0252] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0253] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0254] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for optimizing power network substation targets based on multi-dimensional indicators, characterized in that: The following steps are involved: S1: Obtain electrical information and geographical distribution information of the power system in the target area; S2: Based on complex network theory and the electrical information and geographical distribution information, a weighted topological model of the power network is constructed to reflect the grid topological relationship and electrical distance characteristics; S3: Based on graph theory and graph maximum flow theory, and according to the weighted topology model of the power network, a power network energy flow model that reflects the carrying capacity of the power network is constructed; S4: Based on graph theory and complex network theory, according to the power network weighted topology model and power network energy flow model, calculate the value of the key quantitative evaluation index of the substation for each substation in the power system of the target area; S5: Based on the entropy weight algorithm, the entropy value of each substation's key quantitative evaluation indicators is calculated and the multi-indicator entropy weight is further calculated; S6: Based on the calculated values ​​of the substation critical quantitative evaluation indicators and the multi-indicator entropy weights, a comprehensive evaluation of the multi-dimensional key indicators is performed, and the targets of the power network substation paralysis are arranged from large to small according to the comprehensive evaluation indicator values, and a preferred list of targets that may cause paralysis is output.

2. The method for selecting a target power substation in a power network based on multi-dimensional indicators according to claim 1, characterized in that: The electrical information and geographical distribution information include information on power plants, substations or switch stations, transmission lines, and power loads.

3. The method for selecting a target power substation based on multi-dimensional indicators according to claim 2, characterized in that: Obtain electrical and geographic information about the target area's power system, including: S1-1: Obtain information about a power plant, including the power plant name, primary energy type, single unit capacity, total installed capacity, longitude, and latitude; S1-2: Obtain information about a substation or switch station, including the station name, voltage level, longitude, and latitude. S1-3: Acquire information about the transmission line, including: line name, voltage level, number of parallel circuits, line length, head end station, and end station; S1-4: Obtaining power load information, the power load information includes: load name, load type, maximum power consumption, upstream power supply substation, longitude, and latitude.

4. The method for optimizing power network substation targets based on multi-dimensional indicators according to claim 3, characterized in that: Based on complex network theory and the electrical information and geographic distribution information, a weighted topology model of the power network is constructed to reflect the grid topology relationship and electrical distance characteristics, including: Step S2-1: Sort out all substations in the regional power network and build nodes for each i-th substation , traverse all substations to form a node set , Indicates the total number of substations; Step S2-2: Based on the node set, construct the edges between nodes according to the connection relationship between the transmission line and the substation. For edges of the same voltage level, the line length is used as its weight. The edge weight is calculated according to the following formula: ； Where, represents the edge weight of the kth transmission line between substation i and substation j, represents the physical length of the transmission line, Indicates the voltage level of the transmission line. Indicates the highest voltage level of the regional power network; For any transmission line, calculate the weight according to the above formula and add the corresponding edge, traverse all transmission lines to form an edge set ;e (i,j) represents the edge between substations i and j; Step S2-3: Form a weighted topology model of the power network based on the node set and edge set .

5. The method for optimizing power network substation targets based on multi-dimensional indicators according to claim 4, characterized in that: Based on graph theory and graph maximum flow theory, and according to the weighted topology model of the power network, a power network energy flow model that reflects the carrying capacity of the power network is constructed, including: Step S3-1: Based on the weighted topology model of the power network, power plants and substations are abstracted as nodes of the power grid, and transmission lines are abstracted as edges of the power grid. Power flows are transmitted from generator nodes to load nodes via the edges of the power grid. Introducing virtual source points and virtual sink points; adding virtual edges between the virtual source points and each power plant node to establish a connection relationship. At the same time, the virtual edge is defined as a unidirectional edge, allowing power flow only from the virtual source point to the power plant node, and the capacity of the virtual edge is equivalent to the available power generation capacity of the corresponding power plant; A virtual edge is added between the virtual sink and each load substation to establish a connection relationship. This virtual edge is defined as a unidirectional edge, allowing power flow only from the load substation to the virtual sink. The capacity of this virtual edge is equivalent to the actual power demand of each load substation at each moment. Step S3-2: Based on the Ford-Fulkerson algorithm, solve the maximum energy flow of the power network: Step 1: Setup , set each node label function L and queue node set , set Q is a sequential set and must satisfy the first-in-first-out principle; Step 2: Take , find the node connected to i along the associated edge , and , then remember , , ,and and , then remember , ; After the elements in J are traversed one by one, mark i as checked; Step 3: Repeat Step 2 until or All have been checked; Among them, i is the node currently being processed; is the first node in the set Q, s is the source node, that is, the starting point of the flow network; j is the neighboring node directly connected to node i; f(i,j) is the actual flow on edge (i,j); c(i,j) is the capacity of edge (i,j); L j is the label of node j, which is used to record its predecessor node and augmentation direction; flow is the maximum augmentable flow of the current augmentation path; L t is the label of load node t; L i is the label of node i; Step 4: If , the algorithm terminates, all saturated edges form the minimum cut set of the network, and the sum of the capacities of the saturated edges is the maximum flow of the network; otherwise, an augmenting chain p is found along the label L, and the adjustment method is as follows: , ,and , ; After execution, return to Step 2; Step S3-3: Based on the maximum energy flow calculated in step S3-2, the maximum power supply capacity of the target power system under the given grid is obtained; If the minimum cut falls on the edge set between the virtual source node and the power plant node, it means that the available power generation capacity at this moment is always less than the load demand power, that is, the power supply is insufficient; If the minimum cut falls within the edge set of the power network, it means that the power transmission capacity of the power network is insufficient at that moment, and the power transmission of the minimum cut surface is limited; If the minimum cut falls on the edge set between the load substation and the virtual sink, the maximum flow of the power network is equal to the total load demand power, that is, the power supply can meet the load demand.

6. The method for selecting a target power substation in a power network based on multi-dimensional indicators according to claim 5, characterized in that: The key quantitative evaluation indicators of the substation include grid structure importance indicators and performance degradation indicators; The grid structure importance indicators include node centrality indicators, node power degree indicators and network clustering coefficient indicators; the performance degradation indicators include connectivity indicators, shortest path change rate indicators and power supply capacity degradation indicators.

7. The method for selecting a target power substation based on multi-dimensional indicators according to claim 6, characterized in that: Based on graph theory and complex network theory, and according to the power network weighted topology model and power network energy flow model, the values ​​of key quantitative evaluation indicators of substations are calculated for each substation in the power system of the target area, including: S4-1: Calculate the node betweenness centrality index, which is the ratio of the shortest path passing through the node to the number of all shortest paths. The calculation formula is as follows: ； Where, represents the number of shortest paths connecting the power node s and the load node t and passing through the node i; It represents the total number of shortest paths connecting the power source node s and the load node t. The above parameters are obtained using the weighted topology model of the power network; S4-2: Obtain the node power index, which indicates the number of neighboring nodes directly connected to the node. The calculation formula is as follows: ； Where, represents the set of downstream neighbor nodes j of node i; represents the active power flowing from node i to its neighbor node j; if the actual active power calculated by the maximum flow model is flowing from node j to node i, then And it is not included in the calculation of the node power degree index; represents the active power injected into the power network by the g-th power plant, represents the total number of power plants of various types in the power network. The above parameters are obtained using the power network energy flow model; S4-3: Calculate the node clustering coefficient index, which quantifies the degree to which the central node i and its first-order neighbor nodes form a tightly connected group. Its mathematical description is the ratio of the closed triangle formed by the node i and its first-order neighbor nodes. The node clustering coefficient The calculation formula is as follows: ； Where, represents the first-order neighbor node of the node i, Indicates that node j and node k are both first-order neighbor nodes of node i, and Indicates that there is a direct connection edge between two first-order neighbor nodes j and k. The above parameters are obtained using the weighted topology model of the power network; S4-4: Calculate the connectivity index, which represents the interoperability of the remaining nodes after a node fails; the connectivity index is described by the source-load imbalance within the electrical island. , and its calculation formula is shown as follows: ； Where, represents the active load on load substation l, L represents the total number of load substations; D represents the number of electrical islands formed in the power network after node i fails. If the power network is still connected after node i fails, then D=1; It means that substation k belongs to the jth electrical island after node i fails. and They represent the active load and active power connected to substation k respectively; It represents the operation of taking large numbers. The above parameters are obtained using the power network energy flow model; S4-5: Calculate the shortest path change rate index, which is defined as the change rate of the average shortest path distance between the source and load of the power network after any node i fails; the shortest path change rate index The calculation formula is as follows: ； Where, and Respectively represent the collection of power source nodes and load substation nodes in the power network, and They represent the shortest path distances of the source-load node pair st in the complete grid and in the network after the substation i fails, respectively. The above parameters are calculated using the Dijkstra algorithm in the weighted topology graph; S4-6: Calculate the power supply capacity degradation index, power supply capacity degradation index The calculation formula is as follows: ； Where, Indicates the power supply capacity under the complete grid condition. represents the power supply capacity of the power network after the failure of the i-th substation; The betweenness centrality index, node power degree index, node clustering coefficient index, connectivity index, shortest path change rate index and power supply capacity degradation index are all positive indicators. The larger the index value, the more important the substation is in the topological structure of the power network.

8. The method for optimizing power network substation targets based on multi-dimensional indicators according to claim 7, characterized in that: Based on the entropy weight algorithm, the entropy value of each substation's key quantitative evaluation indicators is calculated and the multi-indicator entropy weight is further calculated, including: Step S5-1: Assume that there are m substation objects and n evaluation indicators. After the indicator calculation, the indicator set is obtained. , where the index set of the i-th object is , and define the sample set of the jth index as ; like If it is a positive indicator, the standardization preprocessing is performed as follows: ； like If it is a negative indicator, the standardization preprocessing is performed as follows: ； In the above formula, and Represents the jth index respectively The maximum and minimum values ​​in the sample set; After preprocessing, the new indicator set is ; Step S5-2: For the jth index after normalization , calculate the i-th substation sample in turn The proportion in this indicator set : ； ； Step S5-3: According to the definition of information entropy, calculate the information entropy of the j-th indicator in sequence: ； ； Among them, when When, define ;therefore, Heng is established; Step S5-4: Through information entropy , calculate the weight of each indicator according to the following formula : ； 。 9. The method for optimizing power network substation targets based on multi-dimensional indicators according to claim 8, characterized in that: Based on the calculated values ​​of substation critical quantitative evaluation indicators and multi-indicator entropy weights, a comprehensive evaluation of multi-dimensional key indicators is performed. The targets are arranged from large to small according to the comprehensive evaluation indicator values, and a preferred list of power network substation paralysis targets is output, including: After calculating the weights of each indicator, the indicators are integrated according to the following formula to obtain the comprehensive evaluation index value: ； ； Where, That is the comprehensive evaluation index value of the i-th substation; After calculating the comprehensive evaluation index value of each node, the optimal list of power network substation paralysis targets is obtained by arranging them from large to small.

10. A device for optimizing target selection of power network substations based on multi-dimensional indicators, characterized in that: including processors and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 9.

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