A Fault Diagnosis Method of Power Systems Based on an Improved Adaptive Fuzzy Spiking Neural P Systems and PSO Algorithms. Issue 2 (1st March 2016)

• Record Type: Journal Article
• Authors: Wang, Jun; Peng, Hong; Tu, Min; Pérez‐Jiménez, J. Mario; Shi, Peng
• Is Part Of: Chinese journal of electronics. Volume 25:Issue 2(2016)
• Page Start: 320
• Page End: 327
• Publication Date: 2016-03-01
• Subjects: Fault diagnosis -- Power systems -- Member Computing -- AFSN P systems -- Particle swarm optimization algorithm; biocomputing -- fuzzy reasoning -- learning (artificial intelligence) -- particle swarm optimisation -- power engineering computing -- power system faults -- power system reliability; fault diagnosis method -- power systems -- improved adaptive fuzzy spiking neural P systems -- AFSN P system inference algorithm -- particle swarm optimization -- PSO algorithm -- parallel computing -- AFSN P system algorithm -- 4‐node system; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2016.03.019
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : 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.