Application of BPSO with GA in model-based fault diagnosis of traction substation
Song Gao, Chenxi Dai, Zhigang Liu, Xiao Geng · 2014
In this paper, a hybrid evolutionary algorithm based on Binary Particle Swarm Optimization (BPSO) and Genetic Algorithm (GA) is proposed to compute the minimal hitting sets in model-based diagnosis. And a minimal assurance strategy is proposed to ensure that the final output of algorithm is the minimal hitting sets. In addition, the logistic mapping of chaos theory is adopted to avoid the local optimum. The high efficiency of new algorithm is proved through comparing with other algorithms for different problem scales. Additionally, the new algorithm with logistic mapping could improve the realization rate to almost 100% from 96%. At last, the new algorithm is used in the model-based fault diagnosis of traction substation. The results show that the new algorithm makes full use of the advantages of GA and BPSO and finds all the minimal hitting sets in 0.2369s, which largely meet the real-time requirement of fault diagnosis in the traction substation.