Fault Section Estimation of Power Systems with Optimization Spiking Neural P Systems

An optimization spiking neural P system (OSNPS) provides a novel way to directly use a P system to solve optimization problems. This paper discusses the practical application of OSNPS for the first time and uses it to solve the power system fault section estimation problem formulated by an optimization problem. When the status information of protective relays and circuit breakers read from a supervisory control and data acquisition system is input, the OSNPS can automatically search and output fault sections. Case studies show that an OSNPS is effective in fault sections estimation of power systems in different types of fault cases: including a single fault, multiple faults and multiple faults with incomplete and uncertain information. Key-words: Membrane computing, optimization spiking neural P system, fault section estimation, power systems, fault diagnosis. Fault Section Estimation of Power Systems with OSNPS 241

[1]  Gheorghe Paun,et al.  The Oxford Handbook of Membrane Computing , 2010 .

[2]  Doug Fisher,et al.  SCADA: Supervisory Control and Data Acquisition , 2015 .

[3]  Xiangxiang Zeng,et al.  Spiking Neural P Systems with Thresholds , 2014, Neural Computation.

[4]  Tao Wang,et al.  Fault Diagnosis Models for Electric Locomotive Systems Based on Fuzzy Reasoning Spiking Neural P Systems , 2014, Int. Conf. on Membrane Computing.

[5]  Shyh-Jier Huang,et al.  Application of artificial bee colony-based optimization for fault section estimation in power systems , 2013 .

[6]  Gheorghe Paun,et al.  Spike Trains in Spiking Neural P Systems , 2006, Int. J. Found. Comput. Sci..

[7]  Mario J. Pérez-Jiménez,et al.  Fuzzy Membrane Computing: Theory and Applications , 2015, Int. J. Comput. Commun. Control.

[8]  Gheorghe Paun.  Spiking Neural P Systems: A Tutorial , 2007, Bull. EATCS.

[9]  Marian Gheorghe,et al.  Evolutionary membrane computing: A comprehensive survey and new results , 2014, Inf. Sci..

[10]  Zhu Yongli,et al.  Bayesian networks-based approach for power systems fault diagnosis , 2006, IEEE Transactions on Power Delivery.

[11]  Hong Peng,et al.  Fuzzy reasoning spiking neural P system for fault diagnosis , 2013, Inf. Sci..

[12]  Qi Meng,et al.  A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems , 2013, Appl. Soft Comput..

[13]  Zhengyou He,et al.  Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems , 2015, IEEE Transactions on Power Systems.

[14]  Mario J. Pérez-Jiménez,et al.  Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis , 2014, Int. J. Comput. Commun. Control.

[15]  Fushuan Wen,et al.  Fault section estimation in power systems using a genetic algorithm , 1995 .

[16]  Gheorghe Paun,et al.  Computing with Membranes , 2000, J. Comput. Syst. Sci..

[17]  Linqiang Pan,et al.  Asynchronous spiking neural P systems with local synchronization , 2013, Inf. Sci..

[18]  Xiangxiang Zeng,et al.  On Some Classes of Sequential Spiking Neural P Systems , 2014, Neural Computation.

[19]  Rudolf Freund,et al.  Extended Spiking Neural P Systems with Decaying Spikes and/or Total Spiking , 2008, Int. J. Found. Comput. Sci..

[20]  H.H. Zurn,et al.  Identifying the Primary Fault Section After Contingencies in Bulk Power Systems , 2008, IEEE Transactions on Power Delivery.

[21]  Oscar H. Ibarra,et al.  Asynchronous spiking neural P systems , 2009, Theor. Comput. Sci..

[22]  Ferrante Neri,et al.  An Optimization Spiking Neural P System for Approximately Solving Combinatorial Optimization Problems , 2014, Int. J. Neural Syst..

[23]  Fushuan Wen,et al.  Fuzzy logic approach in power system fault section identification , 1997 .

[24]  Tao Wang,et al.  Weighted Fuzzy Spiking Neural P Systems , 2013, IEEE Transactions on Fuzzy Systems.

[25]  Yong-Hua Song,et al.  Fault diagnosis of electric power systems based on fuzzy Petri nets , 2004 .

[26]  Gexiang Zhang,et al.  Application of Weighted Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis in Traction Power Supply Systems of High-speed Railways , 2014 .

[27]  Xiangning Lin,et al.  A Fault Diagnosis Method of Power Systems Based on Improved Objective Function and Genetic Algorithm-Tabu Search , 2010, IEEE Transactions on Power Delivery.

[28]  Wang Jun,et al.  Application of Adaptive Fuzzy Spiking Neural P Systems in Fault Diagnosis of Power Systems , 2014 .

[29]  Young-Moon Park,et al.  A fault diagnosis expert system for distribution substations , 2000 .

[30]  Henry N. Adorna,et al.  Improving GPU Simulations of Spiking Neural P Systems , 2012 .