Complex electromagnetic looped network partition optimization planning method based on Louvain algorithm

Abstract

The invention discloses a complex electromagnetic looped network partition optimization planning method based on a Louvain algorithm, and the method comprises the steps: simplifying a receiving-end power grid model into a weighted undirected topological graph, dividing the nodes of the weighted undirected topological graph into a plurality of communities based on the Louvain algorithm, and allocating each node to the community where the adjacent node is located until the modularity of each community is the maximum value. And when the community formed after all the nodes finish moving and the three-phase short-circuit current of each partition meet judgment conditions, outputting partition schemes, performing index evaluation on all the partition schemes, and selecting an optimal partition scheme. According to the method provided by the invention, the operation efficiency and stability of the power grid are improved, and the complexity in planning and operation of the power grid is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of alternating current power transmission, and in particular to a complex electromagnetic ring network partition optimization planning method based on Louvain algorithm. Background Art

[0002] With the rapid development of the power system, power grids of different voltage levels are growing synchronously, forming an electromagnetic ring network structure of 1000 / 500 / 220kV across multiple voltage levels. In the early stages of power grid development, electromagnetic ring networks across voltage levels played a key role in improving power transmission capacity and ensuring power supply stability. However, with the gradual improvement of UHV AC systems and the increase in power flow carrying capacity, early electromagnetic ring networks have gradually become a potential risk to the safe and stable operation of power grids. When a high-voltage power grid fails, the power flow may be transferred to the low-voltage power grid, bringing huge pressure to the latter, and may even trigger a chain reaction, leading to large-scale power outages in local power grids. Therefore, in order to ensure the stable operation of the power grid, solving the threat of electromagnetic ring networks to the safe and stable operation of the power grid has become an urgent problem to be solved in the field of power systems.

[0003] At present, there are two main methods for partitioning electromagnetic ring networks: experience-based partitioning and search-based partitioning. The experience-based partitioning method relies on the professional knowledge and long-term experience of operators to achieve grid partitioning by disconnecting specific lines or adjusting the operation mode of substation buses. However, with the expansion of the scale of the power grid and the improvement of the degree of grid interconnection, this method lacks in-depth research on the overall structure of the power grid. The search-based partitioning method uses computer-aided analysis to search for specific grid operation problems using the overall topological structure of the power system. However, the existing search partitioning method requires re-division of the partitions after the new project is put into operation, which is not flexible enough. Summary of the invention

[0004] In view of this, the object of the present invention is to provide a complex electromagnetic ring network partition optimization planning method based on Louvain algorithm, which is characterized by comprising:

[0005] Step 1: Set partition parameters based on the receiving-end power grid model and set the short-circuit current interruption value based on the voltage level;

[0006] Step 2: simplifying the receiving-end power grid model into a weighted undirected topological graph, wherein the weighted undirected topological graph has busbars as nodes;

[0007] Step 3: Divide the nodes into multiple communities based on the Louvain algorithm, and calculate the modularity of each community;

[0008] Step 4: Calculate the modularity gain of each community after each node is assigned to the community where its neighboring node is located. If the modularity gain is greater than 0, repeatedly move each node to the community where its neighboring node is located until the modularity of each community reaches a maximum value.

[0009] Step 5: Determine whether the community formed after all the nodes move to meet the partition scale. If so, calculate the three-phase short-circuit current of each node according to the equivalent voltage source method; if not, reconstruct the weighted undirected topological graph and repeat step 4;

[0010] Step 6: Determine whether the three-phase short-circuit current of each partition satisfies the short-circuit current interruption value. If yes, output the partitioning scheme; if no, reconstruct the weighted undirected topological graph and repeat step 4;

[0011] Step 7: When all the nodes are divided into the same community, the partitioning scheme is outputted, and the index evaluation is performed on all the partitioning schemes to select the optimal partitioning scheme.

[0012] Preferably, the weighted undirected topological graph has transformers and lines as edges, and the reciprocals of the reactance values ​​of the transformers and the lines are the weighted values ​​of the edges.

[0013] Preferably, simplifying the receiving-end power grid model into the weighted undirected topological graph comprises:

[0014] Step 201: Equivalently set the external power source to a series circuit model formed by an infinite power source and an internal impedance, and calculate the reactance per unit value of the internal impedance;

[0015] Step 202: Calculate the short-circuit reactance per unit value of the main transformer of the 1000 kV hub substation and the main transformer of the 500 kV hub substation according to the actual situation of the receiving-end power grid;

[0016] Step 203: Calculate the per unit value of the line reactance between the 500 kV substation and the 220 kV substation according to the actual situation of the receiving-end power grid;

[0017] Step 204: Simplify the receiving-end power grid model into the weighted undirected topology graph based on the reactance per unit value, the short-circuit reactance per unit value, and the line reactance per unit value.

[0018] Preferably, dividing the nodes into a plurality of the communities based on the Louvain algorithm, and calculating the modularity of each of the communities comprises:

[0019] Determine whether the node is partitioned and running. If so, assign the nodes in the same partition to the same community based on the Louvain algorithm, and calculate the modularity of each community; if not, treat each node as a community based on the Louvain algorithm, and calculate the modularity of each community;

[0020] The expression of the modularity Q is as follows:

[0021]

[0022] In the formula, A ij is the edge weight between node i and node j; k i and k j are the degrees of the node i and the node j, that is, the sum of the weights of the edges owned by the node i and the node j; c i and c j is the community ID to which the node i and the node j belong; δ(c i ,c j ) is the indicator function, if c i =c j When , the node i and the node j belong to the same community, the indicator function takes the value of 1, otherwise it is 0; m is the sum of the weights of all the edges in the weighted undirected topological graph.

[0023] Preferably, the module gain ΔQ is expressed as:

[0024]

[0025] Among them, k i,in is the sum of the weights of the edges from node i to the nodes in community B where its neighboring nodes are located; k i is the sum of the weights of the edges of node i; ∑ tot is the sum of the weights of the edges of all nodes in community B; m is the sum of the weights of all edges in the weighted undirected topological graph.

[0026] Preferably, during the node movement process, the connection relationship and electrical parameters between the nodes, and the overall improvement degree of the modularity are used as reference factors.

[0027] Preferably, reconstructing the weighted undirected topological graph comprises:

[0028] The community in the weighted undirected topological graph generated in step 2 is taken as a new node, the connection between all the nodes in the community in step 2 and the nodes in other communities is taken as a new edge, and the connection strength between the communities in the weighted undirected topological graph generated in step 2 is taken as the weight of the new edge.

[0029] Preferably, all the partitioning schemes are evaluated for indicators, and the optimal partitioning scheme is selected, including:

[0030] Step 701: Obtain receiving-end power grid data and calculate power grid evaluation indicators of partition scheme i;

[0031] Step 702: Calculate the power grid evaluation vector X of the partition scheme i i Model|X i |;

[0032] Step 703: Alignment i | Sort and select the partition scheme with the largest modulus value as the optimal partition scheme.

[0033] Preferably, the power grid evaluation indicators include: modularity index Q, line overload index R1 under fault, load shedding index R2, short-circuit current index I S , Power and electricity balance margin index P mar .

[0034] The beneficial effects of the present invention are as follows:

[0035] The present invention provides an electromagnetic ring network partition optimization planning method based on the Louvain algorithm, which aims to improve the operation efficiency and stability of the power grid, while reducing the complexity of power grid planning and operation. Compared with the traditional search partitioning method, the method provided by the present invention can realize the optimization of the initial partitioning scheme of the power grid with all nodes completely split; it can also be based on the actual operation partitioning scheme of the current situation to optimize the partitioning scheme.

[0036] The method provided by the present invention is particularly suitable for the discovery problem of large-scale networks. The algorithm converges very quickly and can achieve community division of large-scale networks in a relatively short time. At the same time, the method provides hierarchical partitioning results and can discover hierarchical community structures at different granularity levels, providing more solutions for the optimization of complex electromagnetic ring networks.

[0037] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0038] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 A schematic diagram of a flow chart of an electromagnetic ring network partition optimization planning method based on the Louvain algorithm provided in an embodiment of the present invention;

[0041] Figure 2 A schematic diagram of a single-station power supply model of an electromagnetic ring network partition optimization planning method based on the Louvain algorithm provided in an embodiment of the present invention;

[0042] Figure 3 A schematic diagram of a two-station hand-in-hand power supply model for an electromagnetic ring network partition optimization planning method based on the Louvain algorithm provided in an embodiment of the present invention;

[0043] Figure 4 A schematic diagram of a three-station chain partition power supply model for an electromagnetic ring network partition optimization planning method based on the Louvain algorithm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] To facilitate understanding of this embodiment, Figure 1 The electromagnetic ring network partition optimization planning method based on Louvain algorithm disclosed in the embodiment of the present invention is introduced in detail.

[0046] Embodiment 1:

[0047] Step 1: Set partition parameters based on the receiving-end power grid model and set the short-circuit current interruption value based on the voltage level.

[0048] Among them, the receiving-end power grid model is a typical receiving-end power grid model of 1000kV, 500kV, and 220kV, including: a single-station power supply model, a two-station hand-in-hand power supply model, and a three-station chain partition power supply model.

[0049] Specifically, the partition parameters are set based on the receiving-end power grid model, and the short-circuit current interruption value is set based on the voltage level, including: based on the receiving-end power grid model, setting the upper limit and lower limit of the number of transformers in the partition, and the upper limit of the capacity of the units in the partition according to the historical data and / or actual operation of the power grid; setting the short-circuit current interruption value based on the voltage level;

[0050] Among them, according to the historical maximum load value and the historical minimum load value, the upper limit and lower limit of the number of transformers in the zone are set based on the receiving-end power grid model; the upper limit of the unit capacity in the zone is set according to the maximum power generation capacity of the unit; and the short-circuit current tripping value is set based on the voltage level.

[0051] The structural diagrams of the single-station power supply model, the two-station hand-in-hand power supply model, and the three-station chain partition power supply model are shown in the figure below. Figure 2 , 3 , 4, combined Figure 3 , Figure 4 In the two-station hand-in-hand power supply model and the three-station chain partition power supply model, the upper limit of the number of 500kV transformers is 8, and the lower limit is 3; the short-circuit current interrupting value of the 1000kV power grid is 63kA, the short-circuit current interrupting value of the 500kV power grid is 63kA, and the short-circuit current interrupting value of the 220kV power grid is 50kA.

[0052] In step 1, the safe and stable operation of the power grid is ensured by setting the partition parameters; by setting the short-circuit current interruption value, it is ensured that when a short-circuit fault occurs, the fault current can be cut off in time, thereby protecting the power grid equipment.

[0053] Step 2: Simplify the receiving-end power grid model into a weighted undirected topological graph, where the busbars are nodes.

[0054] Preferably, the weighted undirected topological graph has transformers and lines as edges, and the reciprocals of the reactance values ​​of the transformers and lines are the weight values ​​of the edges.

[0055] The busbars include 1000kV busbars, 500kV busbars, and 220kV busbars. In this embodiment, the nodes of the busbars are used to represent the voltage level and position of the receiving power grid; the edges of the transformers and lines are used to represent the connection relationship and electrical parameters of the receiving power grid; the inverse of the reactance value of the transformers and lines is used as the weighted value of the edge, which can reflect the transmission loss and stability of the receiving power grid.

[0056] Preferably, simplifying the receiving-end power grid model into a weighted undirected topological graph includes:

[0057] Step 201: The external power source is equivalent to a series circuit model formed by an infinite power source and an internal impedance, and the reactance per unit value of the internal impedance is calculated.

[0058] Reactance per unit value Xs ' is expressed as follows:

[0059]

[0060] In the formula, E s ' is the standard value of the wireless high-power power supply voltage; I k Inject current into the external system after the 1000kV busbar three-phase short circuit; I B is the system reference current; S B is the system base capacity, which is 100MVA; U B It is the system reference voltage, and its value is 1050kV.

[0061] The reason for treating the out-of-region power source as a series circuit model formed by an infinite power source and an internal impedance is that, in order to simplify the analysis, the receiving-end power grid model is taken as the internal system, and the out-of-region power source is taken as the external system. The situation where the out-of-region power source injects short-circuit current into the receiving-end power grid is simulated based on the Thevenin theorem.

[0062] Step 202: According to the actual situation of the receiving-end power grid, the short-circuit reactance per unit value of the main transformer of the 1000 kV hub substation and the main transformer of the 500 kV hub substation is calculated.

[0063] Short-circuit reactance per unit value X' T The expression is as follows:

[0064]

[0065] Where U k % is the percentage of transformer short-circuit voltage; S T is the rated capacity of the transformer; S B is the rated capacity of the system, which is 100MVA.

[0066] Step 203: Calculate the per unit value of the line reactance between the 500 kV substation and the 220 kV substation according to the actual situation of the receiving-end power grid.

[0067] Line reactance per unit value X' L The expression is as follows:

[0068]

[0069] In the formula, x L is the line reactance per unit length; l is the line length; Z B is the system reference impedance, S B is the system base capacity, which is 100MVA. B It is the system reference voltage, and its value is 525kV or 230kV according to the actual voltage level of the line.

[0070] Step 204: Simplify the receiving-end power grid model into a weighted undirected topology graph based on the per-unit reactance value, the per-unit short-circuit reactance value, and the per-unit line reactance value.

[0071] In step 2, by simplifying the receiving-end power grid model, the key characteristics of the actual receiving-end power grid can be retained, which facilitates subsequent algorithm processing.

[0072] Step 3: Divide the nodes into multiple communities based on the Louvain algorithm and calculate the modularity of each community.

[0073] Preferably, nodes are divided into multiple communities based on the Louvain algorithm, and the modularity of each community is calculated including:

[0074] Determine whether the node is partitioned. If so, assign the nodes in the same partition to the same community based on the Louvain algorithm, and calculate the modularity of each community. If not, treat each node as a community based on the Louvain algorithm, and calculate the modularity of each community.

[0075] The expression of modularity Q is as follows:

[0076]

[0077] In the formula, A ij is the edge weight between node i and node j; k i and k j are the degrees of node i and node j, that is, the sum of the weights of the edges owned by node i and node j; c i and c j is the community ID to which node i and node j belong; δ(c i ,c j ) is the indicator function, if c i =c j When , node i and node j belong to the same community, the indicator function takes the value of 1, otherwise it is 0; m is the sum of the weights of all edges in the weighted undirected topological graph.

[0078] Among them, when all nodes in the weighted undirected topological graph are divided into communities, the modularity is the difference between the sum of the weights of the edges within each community and the sum of the weights of all edges connected to the community nodes.

[0079] Step 4: Calculate the modular gain of each community after each node is assigned to the community where its adjacent nodes are located. If the modular gain is greater than 0, each node is repeatedly moved to the community where its adjacent nodes are located until the modularity of each community reaches the maximum value.

[0080] Preferably, the expression of the module gain ΔQ is:

[0081]

[0082] Among them, k i,in is the sum of the weights of the edges from node i to the nodes in community B where its neighboring nodes are located; k i is the sum of the weights of the edges of node i; ∑ tot is the sum of the weights of the edges of all nodes in community B; m is the sum of the weights of all edges in the weighted undirected topological graph.

[0083] Preferably, during the node movement process, the connection relationship and electrical parameters between nodes, and the overall improvement degree of modularity are used as reference factors.

[0084] The significance of this step is that after the node is moved, the connection relationship and electrical parameters between the nodes are considered to ensure that the community structure in the receiving network is reasonable; the overall improvement in modularity is considered to ensure that the community structure can improve the modularity of the receiving network. For example, if the connection relationship between two nodes is strong, or the electrical parameters are similar, this indicates that the functions or properties of the two nodes in the network are relatively close. Merging the two nodes into the same community can enhance the connection density within the community, and the community structure after the move is reasonable. This step is one of the core goals of the community detection algorithm, which can identify community structures with tight internal connections and loose external connections.

[0085] Step 5: Determine whether the community formed after all nodes move meets the partition scale. If so, calculate the three-phase short-circuit current of each node according to the equivalent voltage source method; if not, reconstruct the weighted undirected topology graph and repeat step 4.

[0086] The principle of calculating three-phase short-circuit current by equivalent voltage source method is as follows:

[0087] Step 501: When there is a short-circuit fault point in the power system, a virtual voltage source is introduced into the short-circuit fault point, and the virtual voltage source is used as the only voltage source of the network.

[0088] Step 502: The actual electromotive force of other power sources in the power system is regarded as 0, and the electromotive force of other power sources is replaced by the self-reactance;

[0089] Step 503: Calculate the system equivalent reactance of the short-circuit fault point based on the Thevenin equivalent algorithm;

[0090] Step 504: Calculate the three-phase short-circuit current I at the short-circuit fault point according to the system equivalent reactance and the virtual voltage source. k .

[0091] Three-phase short-circuit current I k The expression is:

[0092]

[0093] Where c is the voltage coefficient; U0 is the nominal system voltage at the short-circuit fault point, i.e. the rated line voltage, in kV; Z k It is the system positive sequence equivalent reactance at the short-circuit fault point, in Ω.

[0094] Step 6: Determine whether the three-phase short-circuit current of each partition meets the short-circuit current interruption value. If so, output the partitioning scheme; if not, reconstruct the weighted undirected topological graph and repeat step 4.

[0095] In step 5 and step 6, preferably, reconstructing the weighted undirected topological graph includes: using the community in the weighted undirected topological graph generated in step 2 as a new node, using the connection between all nodes in the community in step 2 and nodes in other communities as a new edge, and using the connection strength between communities in the weighted undirected topological graph generated in step 2 as the weight of the new edge.

[0096] Among them, the new node is the reconstructed weighted undirected topological graph

[0097] Step 7: When all nodes are divided into the same community, the output partitioning scheme is finished, and the indicators of all partitioning schemes are evaluated to select the optimal partitioning scheme.

[0098] Preferably, all partitioning schemes are evaluated for indicators, and the optimal partitioning scheme is selected including:

[0099] Step 701: Obtain receiving-end power grid data and calculate power grid evaluation indicators of partition scheme i.

[0100] Step 702: Calculate the power grid evaluation vector X of partition scheme i i Model|X i |.

[0101] Step 703: Alignment i |Sort and select the partition scheme with the largest modulus value as the optimal partition scheme.

[0102] Furthermore, the grid evaluation indicators include: modularity index Q, line overload index R1 under fault, load shedding index R2, short-circuit current index I S , Power and electricity balance margin index P mar .

[0103] Specifically, the line overload index R1 under fault is a quantitative index of the operation risk caused by line overload due to the occurrence of N-1 faults. The line overload index under fault is established by comprehensively considering the possibility and severity of the accident.

[0104] The expression of line overload index R1 under fault is:

[0105]

[0106] Where n F is the total number of failures; n l is the total number of failures; h i is the ratio of the annual fault duration to the annual utilization hours, that is, the probability of fault i occurring; I ij is the current value of line j when fault i occurs, I ij,lim is the thermal stability limit of line j; when fault i occurs, if there is a line overload, γ ij =1, otherwise γ ij =0.

[0107] The load shedding index R2 is a quantitative index of load shedding required for a partition in order to avoid severe overload. The reason for reducing the equipment load rate is that when a key line of a partition fails or a failure occurs during maintenance, the power supply capacity may not be able to meet the load demand in the area due to the thermal stability of the equipment. Severe overload can be avoided by reducing the equipment load rate.

[0108] The expression of load shedding index R2 is:

[0109]

[0110] Where n F is the total number of failures; h i is the probability of fault i occurring; P int,i is the load shedding amount when fault i occurs, P load For partition load.

[0111] Short circuit current index I S According to the actual situation of the receiving-end power grid, the short-circuit current is divided into several different levels from high to low, and different correction coefficients are set for different levels. S It can reflect different short-circuit current levels under different partitioning schemes.

[0112] Short circuit current index I S The expression is:

[0113]

[0114] Where N is the number of short-circuit current intervals; δ i is the correction coefficient for the short-circuit current in the ith interval, and the correction coefficients are 0.8, 0.6, 0.4, and 0.2 respectively; g i is the number of buses with short-circuit current in the ith interval; I j ,I NThey are busbar short-circuit current and rated breaking current respectively.

[0115] In this embodiment, N is set to 4, and the short-circuit current intervals are (48,50], (45,48], (40,45], and (0,40] respectively.

[0116] Power balance margin index P mar The expression is:

[0117]

[0118] Where P iMAX P is the power limit of the partition, GMAX is the installed capacity of power plants in the region; P load is the partition load; η is the network loss rate.

[0119] Due to the community module degree Q, power balance margin P mar The larger the index value is, the better the partitioning scheme is. The smaller the index values ​​of the line overload index R1 and the load shedding index R2 are, the better the partitioning scheme is. Therefore, the above evaluation indicators are processed in the same direction to obtain the evaluation vector X of the i-th partitioning scheme. i , the expression of the partition scheme evaluation vector is as follows:

[0120] X i =[Q i ,1-R 1,i ,1-R 2,i ,P mar,i ] T

[0121] In the formula, Q i is the modularity index of partition scheme i; R 1,i is the line overload index under fault in partition scheme i; R 2,i is the load shedding index of partition scheme i; P mar,i is the power balance margin index of partition scheme i.

[0122] In combination with this embodiment, the advantage of the present application is that the partitioning method provided by the present application can take any partitioning state as the initial state, and can optimize the existing partitioning after the new project is put into production to form a new partitioning scheme; the partitioning method provided by the present application can take the completely split state as the initial state to construct a power grid partitioning scheme under ideal conditions.

[0123] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A complex electromagnetic ring network partition optimization planning method based on Louvain algorithm, characterized in that: include: Step 1: Set partition parameters based on the receiving-end power grid model and set the short-circuit current interruption value based on the voltage level; Step 2: simplifying the receiving-end power grid model into a weighted undirected topological graph, wherein the weighted undirected topological graph has busbars as nodes; Step 3: Divide the nodes into multiple communities based on the Louvain algorithm, and calculate the modularity of each community; Step 4: Calculate the modularity gain of each community after each node is assigned to the community where its neighboring node is located. If the modularity gain is greater than 0, repeatedly move each node to the community where its neighboring node is located until the modularity of each community reaches a maximum value. Step 5: Determine whether the community formed after all the nodes move to meet the partition scale. If so, calculate the three-phase short-circuit current of each node according to the equivalent voltage source method; if not, reconstruct the weighted undirected topological graph and repeat step 4; Step 6: Determine whether the three-phase short-circuit current of each partition meets the short-circuit current interruption value, and if so, output the partition scheme; If not, reconstruct the weighted undirected topological graph and repeat step 4; Step 7: When all the nodes are divided into the same community, the partitioning scheme is outputted, and the index evaluation is performed on all the partitioning schemes to select the optimal partitioning scheme.

2. The complex electromagnetic ring network partition optimization planning method based on Louvain algorithm according to claim 1 is characterized in that: The weighted undirected topological graph has transformers and lines as edges, and the reciprocals of the reactance values ​​of the transformers and the lines are weighted values ​​of the edges.

3. The complex electromagnetic ring network partition optimization planning method based on Louvain algorithm according to claim 1 is characterized in that: Simplifying the receiving-end power grid model into the weighted undirected topological graph includes: Step 201: Equivalently set the external power source to a series circuit model formed by an infinite power source and an internal impedance, and calculate the reactance per unit value of the internal impedance; Step 202: Calculate the short-circuit reactance per unit value of the main transformer of the 1000 kV hub substation and the main transformer of the 500 kV hub substation according to the actual situation of the receiving-end power grid; Step 203: Calculate the per unit value of the line reactance between the 500 kV substation and the 220 kV substation according to the actual situation of the receiving-end power grid; Step 204: Simplify the receiving-end power grid model into the weighted undirected topology graph based on the reactance per unit value, the short-circuit reactance per unit value, and the line reactance per unit value.

4. The complex electromagnetic ring network partition optimization planning method based on Louvain algorithm according to claim 1 is characterized in that: Dividing the nodes into a plurality of communities based on the Louvain algorithm, and calculating the modularity of each community comprises: Determine whether the node is partitioned and running. If so, assign the nodes in the same partition to the same community based on the Louvain algorithm, and calculate the modularity of each community; if not, treat each node as a community based on the Louvain algorithm, and calculate the modularity of each community; The expression of the modularity Q is as follows: In the formula, A ij is the edge weight between node i and node j; k i and k j are the degrees of the node i and the node j, that is, the sum of the weights of the edges owned by the node i and the node j; c i and c j is the community ID to which the node i and the node j belong; δ(c i ,c j ) is the indicator function, if c i =c j When , the node i and the node j belong to the same community, the indicator function takes the value of 1, otherwise it is 0; m is the sum of the weights of all the edges in the weighted undirected topological graph.

5. The complex electromagnetic ring network partition optimization planning method based on Louvain algorithm according to claim 1 is characterized in that: The expression of the module gain ΔQ is: Among them, k i,in is the sum of the weights of the edges from node i to the nodes in community B where its neighboring nodes are located; k i is the sum of the weights of the edges of node i; ∑ tot is the sum of the weights of the edges of all nodes in community B; m is the sum of the weights of all edges in the weighted undirected topological graph.

6. The complex electromagnetic ring network partition optimization planning method based on Louvain algorithm according to claim 1 is characterized in that: During the node movement process, the connection relationship and electrical parameters between the nodes, and the overall improvement degree of the modularity are used as reference factors.

7. The complex electromagnetic ring network partition optimization planning method based on Louvain algorithm according to claim 1 is characterized in that: Reconstructing the weighted undirected topological graph includes: The community in the weighted undirected topological graph generated in step 2 is taken as a new node, the connection between all the nodes in the community in step 2 and the nodes in other communities is taken as a new edge, and the connection strength between the communities in the weighted undirected topological graph generated in step 2 is taken as the weight of the new edge.

8. The complex electromagnetic ring network partition optimization planning method based on Louvain algorithm according to claim 1 is characterized in that: All the partitioning schemes are evaluated and the optimal partitioning scheme is selected, including: Step 701: Obtain receiving-end power grid data and calculate power grid evaluation indicators of partition scheme i; Step 702: Calculate the power grid evaluation vector X of the partition scheme i i Model|X i |; Step 703: Alignment i | Sort and select the partition scheme with the largest modulus value as the optimal partition scheme.

9. The complex electromagnetic ring network partition optimization planning method based on Louvain algorithm according to claim 8 is characterized in that: The power grid evaluation indicators include: modularity index Q, line overload index R1 under fault, load shedding index R2, short-circuit current index I S , Power balance margin index P mar .

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