Study on Distribution Network Failure Positioning Algorithm based on Gaussian Variation Swarm Intelligence Optimization Algorithm

Abstract

The premise of effective use of clean energy is to correctly determine the malfunction zone of distribution network with distributed generation. In the case of distortion information, the misjudgment can be appeared only by the power distribution network fault location method of fault overcurrent information. In this paper, a fault location algorithm based on the simulated annealing gaussian variation swarm intelligence optimization algorithm (SAGVSIOA) was proposed for the study. Combined with the idea of regional division, group intelligence optimization algorithm was chosen. In addition, simulated annealing (SA), gaussian variation and chaotic perturbation operator were also be considered, which can balance the efficiency of the search and the diversity of the population, avoiding the algorithm quickly into the local optimal. In addition, the rapid fault location achieved by determining the fault information lied in the substation transformer low side switch. The simulation results for the IEEE33 nodes distribution system were shown that the SAGVSIOA could present correct fault section by an average of less than 5 iterations. It is also found that the algorithm realized the fault location of the distribution network more effectively compared with particle swarm optimization and genetic algorithm.