Positioning method for distribution circuit fault section based on BPSO and GA

Abstract

The invention relates to a positioning method for a distribution circuit fault section based on BPSO and a GA. The mixture of a binary system particle swarm and the genetic algorithm is achieved by a bi-population evolution and information interchange strategy utilized by the method, and a binary system mixed algorithm is formed; two subpopulations are each provided with an individual scale, an evolutionary process of each generation is not affected, after evolution of each generation is completed, information interchange transmission is conducted, an optimal individual is selected, optimizing searches are carried out on the next generation of the two populations respectively until an optimal solution is found out. The probability of immature convergence occurred in a fault positioning process can be reduced by the algorithm, a certain fault tolerance is obtained, and compared with fault section positioning conducted by single binary system particle swarm or the single genetic algorithm, the convergence speed is obviously improved.

Description

technical field [0001] The invention relates to a method for locating fault sections of distribution lines based on BPSO and GA. Background technique [0002] With the aggravation of the world's energy crisis and environmental pollution, people began to pay attention to a cleaner, renewable and efficient power source - Distributed Generator (DG). As one of the common DGs, wind turbines are widely used in grid connection. Distribution lines are directly connected to user loads, so it is of great significance to ensure their reliability, safety and power supply quality during operation. However, the access of DG makes the power distribution system change from a single power radial network to a complex multi-terminal power network with bidirectional power flow. In addition, the structure and power flow of the distribution system also change accordingly, and many parameters of the distribution network have dynamic characteristics, which has a certain impact on the location of ...

AI Technical Summary

Problems solved by technology

[0006] Because GA has fast global search capability, chromosomes share information with each other, and the entire population moves to the most evenly. However, the feedback information in the system is not fully utilized, often resulting in redundant iterations and slow convergence.

By updating the position and speed, PSO makes full use of the individual optimal value and the group optimal value to converge to the optimal value, which has the advantage of fast convergence speed, but at the same time it is easy to fall into the phenomenon of "immature convergence"

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