Power distribution network reconstruction method and device based on R2 index multi-target particle swarm algorithm

Abstract

The invention relates to a power distribution network reconstruction method and device based on an R2 index multi-target particle swarm algorithm, and the method comprises the following steps: S1, obtaining the original data of a power distribution network, and carrying out the particle swarm coding according to a network structure; s2, carrying out iterative solution by adopting a particle swarmalgorithm to obtain an optimal reconstructed network topology; in the iteration process; updating the particle swarm based on the R2 index; specifically, integrating an elitist particle swarm and an updated particle swarm as a candidate solution set RR, adopting uniformly distributed weight vectors and ideal points z * to trim the candidate solution set RR, ranking R2 contribution values of the candidate solutions to the weight vectors from large to small, and using a preset number of candidate solutions to form a particle swarm of next iteration. Compared with the prior art, the method has the advantages of high convergence, good diversity and the like.

Description

technical field [0001] The invention relates to the technical field of distribution network reconfiguration optimization, in particular to a distribution network reconfiguration method and device based on an R2 index multi-objective particle swarm algorithm. Background technique [0002] The power distribution system is an important part responsible for providing energy to final consumers. At this stage, a large amount of power loss and additional costs are generated. Distribution network reconfiguration refers to the basic requirements of system voltage, current, and line capacity. , by changing the opening and closing state of the switch on the distribution network to optimize the operation structure of the distribution network, so as to achieve one or more optimal goals such as balancing load, improving node voltage offset, eliminating overload, and reducing network active power loss. Constrained, multi-objective, nonlinear combinatorial optimization relations. [0003] ...

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Problems solved by technology

[0003] Distribution network reconfiguration is a complex large-scale non-specified polynomial combinatorial optimization problem. At present, the research methods for distribution network reconfiguration can be roughly divided into two categories: one is the traditional mathematical optimization algorithm, that is, the distribution network reconfiguration The problem is described by a mathematical model, and the optimization result that does not depend on the initial network structure is obtained, but the distribution network reconfiguration problem belongs to the combinatorial optimization of a large-scale network, which occupies a large computer memory and is difficult to operate; the other is artificial intelligence algorithm, in which the particle swarm optimization algorithm is a swarm intelligence algorithm following the genetic algorithm and ant colony algorithm, which simulates the migration and clustering behavior of birds in the process of foraging. When encountering a local optimum, the particle speed will quickly drop to zero, and the particle will stagnate and appear premature convergence, and it is difficult to jump out of the local extreme point

Many algorithms have been proposed to solve high-dimensional multi-objective optimization problems. A class of algorithms based on evaluation indicators uses evaluation indicators as selection criteria to measure the quality of the solution. The selection mechanism selects a better solution by comparing the quality of the solution. This type of algorithm Representatives include IBEA and HypE, etc. Although the above algorithms can handle some high-dimensional multi-objective optimization problems well, these algorithms are very sensitive to the shape of the Pareto front, resulting in a poor balance between convergence and diversity.

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