Fault Diagnosis of Power Systems Based on Adaptive Fuzzy Spiking Neural P Systems

摘要：

　　Fault diagnosis method based on adaptive fuzzy spiking neural p systems (in short, AFSN P systems) is presented to improve the efficiency and accuracy of diagnosis in this paper. AFSN P systems are a novel kind of computing models with parallel computing and learning ability. This paper focuses on AFSN P system inference algorithms and learning algorithms and builds the fault diagnosis model based on AFSN P systems for diagnosing effectively. The process of diagnosis based on AFSN P systems is expressed by matrix successfully so that the rate of diagnosis is improved eminently. Furthermore, particle swarm optimization algorithm is introduced into the learning algorithm of AFSN P systems, thus the convergence speed of diagnosis has a big progress. An example of 4-node system is given out to verify the effectiveness of this method. Compared with the existing methods, this method own faster diagnosis speed, higher accuracy and better capacity of the grid topology.

关键词： AFSN P systems fault diagnosis reasoning algorithm learning algorithm particle swarm optimization algorithm

作者: Jun Wang Min Tu Hong Peng Juan Luo

作者单位: School of Electrical and Information Engineering,Xihua University,Chengdu,Sichuan,610039,China School of Mathematics and Computer Engineering,Xihua University. Chengdu,Sichuan,610039,China

会议类型: 国际会议

会议名称: 2013年第二届亚洲膜计算国际会议(2013ACMC)

会议地点: 成都

会议语种:英文

页码: 249-264

在线出版日期: 2013-11-04（万方平台首次上网日期，不代表论文的发表时间）