An Improved Particle Swarm Optimization Algorithm for Fault Diagnosis of Nuclear Power Equipment

LIU Rui, LI Tieping, ZHOU Guoqiang, TIAN Xinlu

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Journal of Chinese Society of Power Engineering ›› 2017, Vol. 37 ›› Issue (10) : 837-841.

An Improved Particle Swarm Optimization Algorithm for Fault Diagnosis of Nuclear Power Equipment

  • LIU Rui1, LI Tieping1, ZHOU Guoqiang2, TIAN Xinlu1
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Abstract

An improved particle swarm optimization (IPSO) algorithm was proposed for fault diagnosis of nuclear power systems. By using the known symptom sets of nuclear power faults, and with the probabilistic causal model, the IPSO algorithm was introduced to solve the fault sets with maximum a posteriori probability; based on traditional PSO algorithm, the principle of good point set was used to initialize the range of PSO; by adaptive adjustment of inertia weight, the premature convergence of PSO was avoided, and the convergence speed was accelerated. Finally, the validity of the method was demonstrated by examples. Results show that the probabilistic causal model based on IPSO algorithm is not limited by fault samples, which therefore has good versatility, with high precision in fault diagnosis and high speed in optimization.

Key words

nuclear power equipment / fault diagnosis / particle swarm optimization algorithm / good point set

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LIU Rui, LI Tieping, ZHOU Guoqiang, TIAN Xinlu. An Improved Particle Swarm Optimization Algorithm for Fault Diagnosis of Nuclear Power Equipment. Journal of Chinese Society of Power Engineering. 2017, 37(10): 837-841

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