针对基于粒子群优化算法的粒子滤波(PSO-PF)算法精度不高,容易陷入局部最优,难以满足电厂温控系统故障诊断的需求,提出一种适用于故障诊断的新型粒子群优化粒子滤波(NPSO-PF)算法.该算法引入社会个体对群体的认知规律优化了粒子更新的方式,并且完善了粒子速度的更新策略,对优势速度赋有较小概率的变异,提高了粒子的寻优能力,同时随机初始化劣势速度,保证了样本的多样性.实验结果表明,与PSO-PF相比,NPSO-PF提高了故障检测的精度和鲁棒性,可以有效地应用于温控系统故障的诊断.%Particle Filter based on Particle Swarm Optimization (PSO-PF) algorithm is not precise and easily trapped in local optimum, which can hardly satisfy the requirement of fault diagnosis of temperature control system in power plant. To solve these problems, a new particle swarm optimization particle filter named NPSO-PF suitable for fault diagnosis was proposed. This algorithm introduced the cognition rule of individuals to groups to optimize the method for updating particles and improved the speed update strategy. As a result, the superior particle velocity can mutate with a small probability and improve the search ability. Meanwhile, due to the random, initialization of on inferior particle, the diversity of samples is ensured. The simulation results show that NPSO-PF improves the precision and robustness compared with PSO-PF, and it is suitable for fault diagnosis of temperature control system.
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