CN106920015B - Shortest loop dynamic division method suitable for power distribution network reconstruction problem codes - Google Patents
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Abstract
The invention relates to a shortest loop dynamic division method suitable for a power distribution network reconstruction problem code, and belongs to the field of power distribution network optimization operation control. The method can dynamically monitor the topological structure change of the power distribution network, automatically search the shortest loop set of the power distribution network, encode the switch in each shortest loop, quickly eliminate infeasible solutions by using the radiation judgment condition, and finally apply the method to an artificial intelligence algorithm for solving the power distribution network reconstruction problem, so that the algorithm can better and more efficiently search the optimal solution. The technical scheme overcomes the defect that the traditional power distribution network reconstruction method cannot dynamically adapt to the network frame change, can find out the shortest loop according to the network frame change, compress the total solving space and find out the optimal power distribution network operation mode, and lays a theoretical foundation for further realizing long-time dynamic reconstruction and uncertainty model reconstruction.
Description
Technical Field
The invention relates to a dynamic partitioning method, in particular to a shortest loop dynamic partitioning method suitable for a power distribution network reconstruction problem code, and belongs to the technical field of power distribution network optimization operation control.
Background
Power Distribution Network Reconfiguration (PDNR) is a very important means in the field of Power Distribution Network optimization operation, and can realize load transfer between feeders or substations through simple and economic switching operation, thereby improving the operation index of a Power Distribution system and realizing economic operation. In the solution of the power distribution network reconstruction problem, an artificial intelligence algorithm, such as a genetic algorithm, a particle swarm optimization algorithm, a simulated annealing algorithm, a tabu search algorithm and the like, is mostly used at present. The algorithms all face the problem of how to encode the power distribution network so that the algorithm has higher solving efficiency and smaller solving space.
The loop-based coding mode originates from a 'circle-breaking method' used by a graph spanning tree in a graph theory, and the method only codes the loops in the network, does not consider branches irrelevant to the loops, and greatly compresses a decoding space. However, the current research has not taken into consideration the influence of the length of the loop on the optimization efficiency, how to dynamically search the shortest loop set in a dynamically changing network environment, how to improve and eliminate the few un-solved solutions generated based on loop coding, and the like. Therefore, a reasonable loop division mode is extremely important, and a shortest loop dynamic division method suitable for power distribution network reconstruction problem codes is urgently needed in the field, so that the power distribution network reconstruction problem can be better and more efficiently solved by an artificial intelligence algorithm.
Disclosure of Invention
The invention provides a method for dynamically dividing the shortest loop suitable for the reconstruction problem code of the power distribution network aiming at the technical problems in the prior art, the technical scheme is used for solving the reconstruction problem of the power distribution network, the method can dynamically monitor the topological structure change of the power distribution network, automatically searches the shortest loop set of the power distribution network, codes a switch in each shortest loop, and quickly eliminates the infeasible solution by using the radiation judgment condition, so that the artificial intelligence algorithm can better and more efficiently solve the reconstruction problem of the power distribution network, and a theoretical basis is laid for further realizing the long-period dynamic reconstruction and the uncertainty model reconstruction.
In order to achieve the above object, the present invention provides a method for dynamically partitioning a shortest loop applicable to a power distribution network reconstruction problem code, which is characterized in that the method includes the following steps:
step S1, reading network structure information including node state information and network adjacent matrix to form topology structure chart;
step S2, simplifying the network, and deleting the nodes and branches which are not related to the loop except the branch where the power node is;
step S3, deleting all nodes with degree of 2 for the simplified network, merging the branches into branch chains, and assigning weight to the branch chains, wherein the weight is the number of the branches contained in the branch chains and is recorded as a weight graph G;
step S4, for the generated weighted graph G, the power supply node is taken as a root node, the Kruskal algorithm in the graph theory is applied to obtain the minimum spanning tree T of the graph, and the set of the remaining L edges in the graph G is marked as G-T;
step S5, removing one edge e in G-T from the weighted graph G, solving the shortest path between two vertexes of the edge e in the graph G-e by applying a Floyd-Warshall algorithm in the graph theory, forming a loop by the solved path and the edge e, recording the loop as a loop 1, traversing all edges in the G-T until each edge is removed from the weighted graph G once, and forming L loops;
step S6, checking whether there is repeat loop in the loop set, if yes, correcting by finding the shortest loop until generating L non-repeat loops;
because there may be m edges e belonging to G-T in the same loop1、e2……emWhen the edge e needs to be listed1、e2……emAnd reconstructing the next shortest path and the edge of the corresponding vertex into new m loops, selecting the loop with the m-1 weight sums, and keeping the original 1 repeated loop to form m loops. Repeating the steps until L different loops are formed;
step S7, real number coding is carried out based on the shortest loop, each individual has L dimension, and the real number of each dimension represents the opened switch number on the loop; modifying or culling the few topologically infeasible individuals that appear.
In this scheme, there may be individuals with infeasible topology, so modifications or culling are performed: the judgment method is as follows: if the same switch is opened in different loops, a switch is reselected in each loop; if the switches are not consistent when being opened but the network is not communicated any more, namely, an isolated point or an island is formed, the switches are directly removed; the coding mode is suitable for coding of intelligent algorithms (such as genetic algorithm, particle swarm algorithm, simulated annealing algorithm and the like) for solving the problem of power distribution network reconstruction.
As a modification of the present invention, in the loop-independent branch in step S2, all non-power nodes with degree 1 are removed from the graph. The downstream nodes can only obtain power supply of a single-side power supply through the branch, and certainly do not participate in the reconstruction process of the power distribution network, so that the downstream nodes can be deleted in the simplification process, and the branch where the power supply node is located is generally reserved although the branch may not be related to a loop.
As an improvement of the present invention, in step S3, the number of branches adjacent to a node is referred to as the degree of the node, only the power node and the node with the degree >2 need to be reserved in the simplified weighted graph, after the node with the degree of 2 is deleted, the branches where the node with the original degree of 2 is located are merged into a branch chain, a weight is assigned to the branch chain, the weight is the number of the original branches in the branch chain, and the obtained graph is referred to as a simplified weighted graph G.
As an improvement of the invention, the step S4 applies a typical minimum spanning tree algorithm Kruskal algorithm in graph theory, the algorithm has the specific steps of firstly selecting the branch e with the minimum weight for a weighted graph with n nodes, wherein n is more than or equal to 21(ii) a ② if the branch e has been selected1、e2……ekThen e is selected from the remaining branchesk+1，ek+1Is that e is satisfied1、e2……ek、ek+1The branch with the minimum weight of the condition that the ring does not exist in the constructed graph; and thirdly, repeating the step two until n-1 branches are selected, wherein the graph formed by the n-1 branches is the minimum spanning tree T of the weighted graph G, and the set of the rest branches is G-T.
As an improvement of the present invention, a typical shortest path algorithm Floyd-Warshall algorithm in graph theory is applied in step S5, and the specific steps of the algorithm are as follows (i.e. let d beijIs the vertex viTo the vertex vjMinimum distance of, omegaijRepresents an edge vivjThe right above, if vi，vjIf they are not adjacent, let omegaijN + ∞, for all i, j, there is dij＝ωij(ii) a ② update dijStarting from k equal to 1, k is successively added to k equal to k +1, and for all i, j, if dij＞dik+dkjThen let dij＝dik+dkj(ii) a And c, repeating the step c until k equals n (n is the total number of nodes).
As an improvement of the present invention, in step S6, it is checked whether L loops in the generated shortest loop set have the same loop, if yes, m branch chains in G-T corresponding to the same shortest loop need to be found, the shortest paths of two vertices of these branch chains are obtained, the branch chains form a loop, the weight sum of the newly generated loop is compared, m-1 loops with the smallest weight sum are found, and the m shortest loops are reconstructed together with the same loop; repeating the steps until L different loops are formed; algorithm tool for sub-shortest pathThe method comprises the following steps of setting a vertex viTo the vertex vjContains p directed edges; deleting the edges in the shortest path in the original weight matrix W each time, thereby obtaining p new weight matrices with only one element difference from W; thirdly, respectively solving a vertex v of the p weight matrixesiTo the vertex vjThe shortest of the p shortest paths is selected as the path with the shortest weight, namely the vertex viTo the vertex vjThis shortest secondary short.
As an improvement of the present invention, in step S7, L shortest loops are applied to a code of an artificial intelligence algorithm for a power distribution network reconstruction problem, the L shortest loops are coded by real numbers, each individual has L dimensions, the real number of each dimension represents a switch number to be opened on the loop, and when a switch is opened in each loop, a spanning tree of the power distribution network can be generated, and the spanning tree is radial and conforms to a topology constraint during operation of the power distribution network; modifying or culling the few topologically infeasible individuals that appear.
As an improvement of the present invention, in step S7, the individuals with few topology impracticable situations are modified or eliminated, and the determination is made in such a way that if the same switch is opened in different loops, a switch is reselected in each loop; if the switches are not consistent when being opened, the network is not communicated any more, namely, an isolated point or an island is formed, and then the network is directly removed.
Compared with the prior art, the invention has the advantages that 1) the technical scheme is suitable for the shortest loop dynamic division method of the power distribution network reconstruction problem codes, on the basis of earlier stage research based on loop coding, the problem of dynamically dividing loops is solved, different loops can be dynamically searched through an algorithm, 2) the technical scheme compares the lengths of the obtained loops, ensures that the length of the loop searched dynamically each time is the shortest, greatly compresses a learning space, and finally solves the problem of judging a small amount of infeasible solutions based on the loop coding, the verification method can quickly and accurately correct or provide an infeasible solution, so that the artificial intelligence algorithm can better and more efficiently solve the reconstruction problem of the power distribution network by applying a coding mode based on the shortest loop, and a theoretical basis is laid for further realizing long-time dynamic reconstruction and uncertainty model reconstruction.
Drawings
FIG. 1 is a flowchart of a method for dynamically partitioning a shortest loop suitable for a reconstruction problem code of a power distribution network according to the present invention;
FIG. 2 is a network diagram of an IEEE33 node distribution network;
FIG. 3 is a simplified weighted graph G retaining only power supply nodes and degree >3 nodes;
FIG. 4 is a minimum spanning tree T of the simplified weighted graph G;
FIG. 5 is the remaining portion G-T of FIG. G with the minimum spanning tree T removed;
FIG. 6 is the shortest path 12-9-15 between nodes 12 and 15, which forms a shortest loop 12-9-15-12 with branch 12-15;
FIG. 7 is the next shortest path 12-21-8-9-15 for nodes 12 and 15, and the next shortest path 9-8-21-12 for nodes 9 and 12;
fig. 8 shows 5 shortest loops of an IEEE33 node distribution network.
The specific implementation mode is as follows:
for the purpose of enhancing an understanding of the present invention, the present embodiment will be described in detail below with reference to the accompanying drawings.
Example 1: referring to fig. 1-8, fig. 2 is a network diagram of an IEEE33 node distribution network, in the 33 node distribution network, there are 33 nodes, 32 sectionalizers, 5 tie switches, and 37 switches in total, thereby determining that the loop number L is 5.
A shortest loop dynamic division method suitable for a power distribution network reconstruction problem code comprises the following steps:
step S1, reading network structure information including node state information and network adjacent matrix to form topology structure chart; the topology of the network is shown in fig. 2, where node 1 is a power node and the direct adjacency of the nodes is shown in fig. 2, where the solid line represents the sectionalizer and the dashed line represents the tie switch;
step S2, simplifying the network, and deleting nodes and branches that are not related to the loop except the branch where the power node is located, where such nodes do not exist in fig. 2 in this case;
step S3, for the simplified network diagram 2, the rectangular nodes therein are power nodes or special nodes with a degree greater than 3, and all of them are reserved; the circular nodes are all ordinary nodes with degree equal to 2, the branches where the circular nodes are located are merged into a branch chain, and the branch chain is given a weight value according to the number of the branches contained in the branch chain, as shown in fig. 3. For example, branches 3-23-24-25-29, special nodes 3 and 29 are reserved, and the generated branch chain 3-29 has a weight of 3. ) The simplified weighted network icon is designated as weighted graph G.
Step S4, for the weighted graph G, the power node is used as a root node, and a Kruskal algorithm in graph theory is applied to obtain a minimum spanning tree T of the graph, as shown in fig. 4, a set of the remaining L edges in the graph G is denoted as G-T, as shown in fig. 5, and L is 5 edges in the G-T;
step S5, taking out the branch 12-15 from the weighted graph G, applying the graph theory Floyd-Warshall algorithm to obtain that the shortest path of the vertexes 12 and 15 of the branch 12-15 is 12-9-15, then the vertex 12-9-15-12 is a loop, the sum of weights is 7, and as shown in FIG. 6, executing 5 edges in G-T according to the step until the traversal is finished;
in step S6, it is checked whether or not there is a duplicate loop in the generated loop set. When the shortest path is taken for the branches 9-12, a situation of duplicate loops may occur. If the edge 9-12 takes the shortest path 9-15-12, the loop 9-15-15-9 formed is the same as the loop 12-9-15-12 obtained at Step 5. The next shortest path for branch 9-12 and branch 12-15, 9-8-21-12-9 (weight sum 7) and 12-21-8-9-15-12 (weight sum 8), respectively, needs to be found at this point, see fig. 7. The weights are selected and the smaller new loop 9-8-21-12-9 is kept and the repeated loop 12-9-15-12 is kept, forming 2 different loops as the corresponding loops for branch 9-12 and branch 12-15.
And step S7, performing real number coding according to the loop serial number based on the shortest loop, wherein each loop has dimension L-5, and the real number in each dimension represents the switch number to be opened on the loop. The shortest loop set of an IEEE33 node power distribution network is shown in FIG. 8, the topology feasible solution (namely, spanning tree) of the network diagram is 50751, the traditional encoding is according to the switch state, and the solution space size is as follows
The feasible solution accounts for 11.64%, while the size of the solution space adopting the shortest loop coding is 86240, the feasible solution accounts for 58.85% of the total solution space and is more than 5 times of the solution space coded according to the switch state, and the optimization efficiency of the intelligent algorithm is obviously improved. But the optimization process still needs to check the individuals with infeasible topology: if the code is [ 2 ]4,21,13,33,4]Then the same switch is opened in different loops4With a re-selection of a switch in loop 118Opened and the code is corrected to [ 2 ]18,21,13,33,4](ii) a Or is encoded as [ 2 ]3,21,13,33,5]Then the network appears disconnected, i.e. nodes [4,5 ]]An island is formed and can be directly removed.
It should be noted that the above-mentioned embodiments are not intended to limit the scope of the present invention, and all equivalent modifications and substitutions based on the above-mentioned technical solutions are within the scope of the present invention as defined in the claims.
Claims (4)
1. A method for dynamically dividing a shortest loop suitable for a reconstruction problem code of a power distribution network is characterized by comprising the following steps:
step S1, reading network structure information including node state information and network adjacent matrix to form topology structure chart;
step S2, simplifying the network, and deleting the nodes and branches which are not related to the loop except the branch where the power node is;
step S3, deleting all nodes with degree of 2 for the simplified network, merging the branches into branch chains, and assigning weight to the branch chains, wherein the weight is the number of the branches contained in the branch chains and is recorded as a weight graph G;
step S4, for the generated weighted graph G, the power supply node is taken as a root node, the Kruskal algorithm in the graph theory is applied to obtain the minimum spanning tree T of the graph, and the set of the remaining L edges in the graph G is marked as G-T;
step S5, removing one edge e in G-T from the weighted graph G, solving the shortest path between two vertexes of the edge e in the graph G-e by applying a Floyd-Warshall algorithm in the graph theory, forming a loop by the solved path and the edge e, recording the loop as a loop 1, traversing all edges in the G-T until each edge is removed from the weighted graph G once, and forming L loops;
step S6, checking whether there is repeat loop in the loop set, if yes, correcting by finding the shortest loop until generating L non-repeat loops;
step S7, real number coding is carried out based on the shortest loop, each individual has L dimension, and the real number of each dimension represents the opened switch number on the loop; modifying or eliminating a small number of topologically infeasible individuals;
in the loop-independent branch in step S2, all non-power nodes with the degree of 1 are removed from the graph;
in step S3, the number of branches adjacent to a node is referred to as the degree of the node, only the power node and the node with the degree >2 need to be reserved in the simplified weighted graph, after the node with the degree of 2 is deleted, the branches where the node with the original degree of 2 is located are merged into a branch chain, a weight is assigned to the branch chain, the weight is the number of the original branches in the branch chain, and the obtained graph is referred to as a simplified weighted graph G;
the step S4 adopts a typical minimum spanning tree algorithm Kruskal algorithm in graph theory, and the algorithm specifically comprises the steps of firstly, selecting a branch e with the minimum weight for a weighted graph with n nodes, wherein n is more than or equal to 21(ii) a ② if the branch e has been selected1、e2……ekThen e is selected from the remaining branchesk+1，ek+1Is that e is satisfied1、e2……ek、ek+1The branch with the minimum weight of the condition that the ring does not exist in the constructed graph; repeating the step two until n-1 branches are selected, wherein a graph formed by the n-1 branches is the minimum spanning tree T of the weighted graph G, the set of the remaining branches is G-T, a typical shortest path algorithm Floyd-Warshall algorithm in the graph theory is applied in the step S5, and the specific steps of the algorithm are as follows, wherein d is calledijIs the vertex viTo the vertex vjMinimum distance of, omegaijRepresents an edge vivjThe right above, if vi，vjIf they are not adjacent, let omegaijN + ∞, for all i, j, there is dij＝ωij(ii) a ② update dijStarting from k equal to 1, k is successively added to k equal to k +1, and for all i, j, if dij＞dik+dkjThen let dij＝dik+dkj(ii) a Repeating the third step until k is equal to n, wherein n is the total number of nodes, checking whether L loops in the generated shortest loop set have the same loop or not in step S6, if so, finding m branch chains in G-T corresponding to the same shortest loop, finding the shortest paths of two vertexes of the branch chains, forming a loop with the branch chains, comparing the sum of weights of the newly generated loops, finding m-1 loops with the smallest sum of weights, and forming m shortest loops together with the same loop; and the like until L different loops are formed.
2. The method for dynamically dividing the shortest loop suitable for the reconstruction problem code of the power distribution network according to claim 1, wherein the algorithm of the second shortest path in the step S6 is specifically as follows (i) set from the vertex viTo the vertex vjContains p directed edges; deleting the edges in the shortest path in the original weight matrix W each time, thereby obtaining p new weight matrices with only one element difference from W; thirdly, respectively solving a vertex v of the p weight matrixesiTo the vertex vjThe shortest of the p shortest paths is selected as the path with the shortest weight, namely the vertex viTo the vertex vjThis shortest secondary short.
3. The method for dynamically dividing the shortest loops suitable for the codes of the reconstruction problems of the power distribution network according to claim 2, wherein L shortest loops are applied to the codes of the artificial intelligence algorithm of the reconstruction problems of the power distribution network in step S7, the shortest loops are coded by real numbers, each individual has L dimensions, the real number of each dimension represents the number of the opened switch on the loop, and when one switch is opened in each loop, a spanning tree of the power distribution network can be generated, the spanning tree is radial and meets the topology constraint during the operation of the power distribution network; modifying or culling the few topologically infeasible individuals that appear.
4. The method for dynamically dividing the shortest loop suitable for the reconstruction problem code of the power distribution network according to claim 3, wherein in step S7, a small number of individuals with infeasible topology are modified or eliminated, and the determination is made in such a way that if the same switch is opened in different loops, one switch is reselected in each loop; if the switches are not consistent when being opened, the network is not communicated any more, namely, an isolated point or an island is formed, and then the network is directly removed.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102945296A (en) * | 2012-10-15 | 2013-02-27 | 河海大学 | Method for reconstructing and modeling uncertainty of distribution network in demand response viewing angle |
| CN105701717A (en) * | 2015-11-11 | 2016-06-22 | 东南大学 | Power distribution network interactive solution programming method based on improved genetic algorithm |
Family Cites Families (5)
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| AU2004269785A1 (en) * | 2004-10-29 | 2006-05-18 | The Australian Gas Light Company | Fault detection and location in power distribution networks |
| CN102684229B (en) * | 2012-05-15 | 2014-06-11 | 天津大学 | Random neighborhood searching method for distribution network reconfiguration |
| CN103440521B (en) * | 2013-08-21 | 2016-08-10 | 南昌大学 | A kind of coding be applicable to power distribution network and genetic algorithm and the application Distribution system |
| CN103795057B (en) * | 2014-01-23 | 2015-08-26 | 华北电力大学（保定） | Based on the power distribution network topology numbering generation method of search in distribution power system load flow calculation |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102945296A (en) * | 2012-10-15 | 2013-02-27 | 河海大学 | Method for reconstructing and modeling uncertainty of distribution network in demand response viewing angle |
| CN105701717A (en) * | 2015-11-11 | 2016-06-22 | 东南大学 | Power distribution network interactive solution programming method based on improved genetic algorithm |
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