基于改进动态自适应模糊Petri网与BP算法的电网故障诊断

Power System Fault Diagnosis Based on Improved Dynamic Adaptive Fuzzy Petri Nets and Back Propagation Algorithm

为减小电网故障诊断过程中人为主观因素造成的误差,提高模糊Petri网模型的容错性和准确性,提出基于改进动态自适应模糊Petri网与BP算法的电网故障诊断方法。首先,在动态自适应模糊Petri网中引入补充弧元组,并利用动态自适应模糊Petri网能动态适应专家系统中模糊知识更新的特性,构建电网故障诊断的通用Petri网模型;其次,利用神经网络中的BP算法对模型中的参数进行训练;最后,分析该故障诊断方法的适应性与容错性。对某8节点测试系统和吉林四平实际电网的仿真结果表明:该算法充分利用了Petri网的并行处理能力,推理简单且思路清晰,在信息不完备的情况下能给出较准确的诊断结果,具有较好的容错性。

In order to reduce error caused by artificial subjective factors in power system fault diagnosis process and improve fault tolerance and accuracy of fuzzy Petri net models, a power system fault diagnosis method based on improved dynamic adaptive fuzzy Petri nets and back propagation algorithm was proposed. First, the general Petri net models were built by introducing complementary arcs tuple in dynamic adaptive fuzzy Petri nets, which is able to dynamically adapt to updated fuzzy knowledge in expert systems. Second, the back propagation algorithm of neural network was used to train model parameters. Finally, the adaptability and the fault tolerance of the algorithm were analyzed. Simulation results on 8-bus testing system and Siping actual power system in Jilin province indicate that the proposed method can make full use of the parallel processing capabilities of Petri nets. Simple and clear in derivation, satisfying diagnosis results with incomplete information can be obtained by the proposed algorithm in this paper, also with good fault tolerance.