Heuristic power distribution network reconstructing method based on minimum spanning tree

Abstract

The invention discloses a heuristic power distribution network reconstructing method based on a minimum spanning tree and is aimed to realize the minimum active power loss and solve the power distribution network reconstructing problem through combining the minimum spanning tree algorithm with a heuristic method. The solving process includes that using the relationship between voltages of two sides of a branch as a weight, and performing preliminary optimization through the minimum spanning tree algorithm to obtain an initial solution to enable the subsequent optimization to be independent of the initial structure of a network; the secondary optimization includes that using a new candidate switch evaluation strategy to evaluate candidate switches, obtaining an optimized reconstructing structure through a little of candidate switches to be evaluated, and correcting to obtain the optimal solution. The heuristic power distribution network reconstructing method based on the minimum spanning tree does not depend on the initial structure of the network, has high reconstructing efficiency and is suitable for different sizes of power distribution networks.

Description

technical field [0001] The invention relates to a distribution network reconfiguration method, in particular to a minimum spanning tree-based heuristic distribution network reconfiguration method. Background technique [0002] Distribution network reconfiguration is an important tool for distribution system network planning and online management. It mainly changes the topology of the network by switching the on / off status of branch switches and tie switches to achieve optimal operation of the system. Usually, the main purpose of power distribution network reconfiguration is to reduce the active power loss of the system and realize the rapid recovery after the system failure. [0003] The distribution network reconfiguration with the goal of minimizing the active network loss of the system can be regarded as a combinatorial optimization problem. Since the number of switches used as optimization variables is huge and the operating constraints need to be satisfied, the computat...

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Problems solved by technology

The disadvantage of this type of method is that it is more dependent on the initial structure of the network, the optimization speed is slow, and it is generally not easy to obtain the global optimal solution.

(3) Intelligent optimization algorithms, mainly particle swarm algorithm, genetic algorithm, tabu search method and acoustic search method, etc. These methods are very effective in solving global optimal solutions, but such algorithms are affected by factors such as network scale As a result, some parameters are difficult to determine, the amoun

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