Optimization of distribution network fault restoration strategy based on fuzzy logic algorithm

Abstract In the context of the crucial importance of stable operation of distribution network, its frequent faults have a serious impact. This paper focuses on the fault recovery strategy of distribution network. Aiming at the limitations of traditional algorithms, the fuzzy logic algorithm is innovatively introduced to study. Through the IEEE 14-node and IEEE 33-node distribution network standard data sets, various fault types, locations and times are set, and the performance of the fuzzy logic algorithm model and the genetic algorithm, particle swarm optimization algorithm, ant colony algorithm model and traditional empirical rule baseline in terms of fault recovery success rate, voltage stability after recovery and load recovery ratio is compared. The experimental results show that the fuzzy logic algorithm model has significant advantages and the system developed using the python programming tool for evaluating the system efficiency. The average fault recovery success rate on the IEEE 14-node data set is 94.6%, and on the IEEE 33-node data set is 93.9%; the average voltage deviation after recovery is 0.033 pu on the IEEE 14-node and 0.035 pu on the IEEE 33-node; the average load recovery ratio is 92.3% on the IEEE 14-node and 91.6% on the IEEE 33-node. This provides a better solution for distribution network fault recovery, which has important theoretical and practical significance.