摘要
本文针对电网故障诊断中存在的保护和断路器拒动误动以及故障信息丢失的情况,给出基于加权模糊Petri网(WFPN)的故障诊断方法。考虑到故障诊断模型中权值设定完全依赖于专家经验的问题,设计具有学习能力的加权模糊Petri网,建立电网元件的WFPN故障诊断模型,并利用粒子群算法对权值进行学习实现权值自适应,最后利用故障诊断模型进行故障案例的仿真分析,并与基于加权模糊Petri网的诊断方法相比证明所提出方法具有较好的诊断可信度。
Weighted Fuzzy Petri Net is presented to deal with the problem in power system fault diagnosis,such as the refuse and misoperation of relay protection and the loss of fault information. Besides,owing to the dependence on expert experience of the weight,an adaptive weighted fuzzy Petri net is proposed in this paper. First,the WFPN model of each electric component is established. Then the particle swarm algorithm is taken into the model for weight learning. Finally,this model is applied to analyze fault cases,the simulation results prove that the proposed method has better diagnosis reliability compared with the weighted fuzzy Petri net.
出处
《电气开关》
2016年第3期68-72,76,共6页
Electric Switchgear
