A Fault Diagnosis Method of Power Systems Based on an Improved Adaptive Fuzzy Spiking Neural P Systems and PSO Algorithms  

作　　者：WANG Jun PENG Hong TU Min Perez-Jimenez J. Mario SHI Peng 

机构地区：[1]School of Electrical and Information Engineering, Xihua University, Chengdu 610039, China [2]Sichuan Province Key Laboratory of Power Electronics Energy-saving Technologies and Equipment, Chengdu 610039, China [3]Center for Radio Administration and Technology Development, Xihua University, Chengdu 610039, China [4]Neijiang Power Supply Company, Neijiang 641003, China [5]Department of Computer Science and Artificial Intelligence, University of Seville, Sevilla 41012, Spain [6]School of Electrical and Electronic Engineering, the University of Adelaide, SA 5005, Australia

出　　处：《Chinese Journal of Electronics》2016年第2期320-327,共8页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61170030,No.61472328);Fund of Sichuan Provincial Department of Science and Technology(No.2013GZ0130)

摘　　要：A new fault diagnosis method based on improved Adaptive fuzzy spiking neural P systems(in short,AFSN P systems) and Particle swarm optimization(PSO)algorithm is presented to improve the efficiency and accuracy of diagnosis for power systems in this paper. AFSN P systems are a novel kind of computing models with parallel computing and learning ability. Based on our previous works, this paper focuses on AFSN P systems inference algorithms and learning algorithms and builds the fault diagnosis model using improved AFSN P systems for diagnosing effectively. The process of diagnosis based on AFSN P systems is expressed by matrix successfully to improve the rate of diagnosis 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 to verify the effectiveness of this method. Compared with the existing methods, this method has faster diagnosis speed, higher accuracy and strong ability to adapt to the grid topology changes.A new fault diagnosis method based on improved Adaptive fuzzy spiking neural P systems（in short,AFSN P systems） and Particle swarm optimization（PSO）algorithm is presented to improve the efficiency and accuracy of diagnosis for power systems in this paper. AFSN P systems are a novel kind of computing models with parallel computing and learning ability. Based on our previous works, this paper focuses on AFSN P systems inference algorithms and learning algorithms and builds the fault diagnosis model using improved AFSN P systems for diagnosing effectively. The process of diagnosis based on AFSN P systems is expressed by matrix successfully to improve the rate of diagnosis 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 to verify the effectiveness of this method. Compared with the existing methods, this method has faster diagnosis speed, higher accuracy and strong ability to adapt to the grid topology changes.

关 键 词：Fault diagnosis Power systems Member Computing AFSN P systems Particle swarm optimization algorithm 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TM73[自动化与计算机技术—控制科学与工程]