WANG Jun, PENG Hong, TU Min, Pérez-Jiménez J. Mario, SHI Peng. A Fault Diagnosis Method of Power Systems Based on an Improved Adaptive Fuzzy Spiking Neural P Systems and PSO Algorithms[J]. Chinese Journal of Electronics, 2016, 25(2): 320-327. doi: 10.1049/cje.2016.03.019
Citation: WANG Jun, PENG Hong, TU Min, Pérez-Jiménez J. Mario, SHI Peng. A Fault Diagnosis Method of Power Systems Based on an Improved Adaptive Fuzzy Spiking Neural P Systems and PSO Algorithms[J]. Chinese Journal of Electronics, 2016, 25(2): 320-327. doi: 10.1049/cje.2016.03.019

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

doi: 10.1049/cje.2016.03.019
Funds:  This work is supported by the National Natural Science Foundation of China (No.61170030, No.61472328), and Fund of Sichuan Provincial Department of Science and Technology (No.2013GZ0130).
  • Received Date: 2014-03-03
  • Rev Recd Date: 2014-06-12
  • Publish Date: 2016-03-10
  • 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.
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