基于BP网络算法优化粗糙-Petri网的电网故障诊断

Power System Fault Diagnosis Based on Rough Set and Petri Network Optimized by BP Algorithm

为了使电网故障诊断的过程更简洁、快速和直观,提出了一种基于BP网络算法优化粗糙-Petri网的电网故障诊断方法。首先用粗糙集理论对电网的故障征兆数据进行处理,从冗余的故障信息中约简出最小决策表;然后基于得到的最小决策表提取诊断规则并建立最优的Petri网模型,利用Petri网处理并行推理的能力来实现高效的电网故障诊断。其中引入神经网络中的BP算法对电网故障诊断Petri网模型的权值参数进行网络优化训练。电网实例分析结果表明该模型能准确找到故障区域,具有较好的快速性、自适应性和一定的容错性。

In order to improve fault diagnosis process, a power grid fault diagnosis method is proposed based on rough sets and Petri network combined with Back-Propagation algorithm. Firstly, the rough set theory is applied to grid fault symptoms for deduction of minimum decision table from the redundant fault information. Then, diagnosis rules are extracted and optimal Petri net model is created. Petri net＇ s parallel processing ability is used to implement efficient fault diagnosis. BP algorithm is adopted to train the weight values of Petri network parameters. The results of grid instance show that the proposed model can find fault zone accurately, and has good adaptability, speediness and fault tolerance.