Title :
Smart fault classification in HVDC system based on optimal probabilistic neural networks
Author :
Khodaparastan, M. ; Mobarake, A.S. ; Gharehpetian, G.B. ; Fathi, S.H.
Author_Institution :
Electr. Eng. Dept., Amirkabir Univ. of Technol., Tehran, Iran
Abstract :
Optimal probabilistic neural network-based method has been porposed in this paper to identify different types of fault in high voltage direct current (HVDC) system. Probabilistic neural network is a type of artificial neural networks capable of approximating the optimal classifier. The particle swarm optimization is porposed to achive an optimal value of smoothing factor for PNN which is an important parameter. The main purpose of this paper is fast and accurate fault classification, for this purpose simple HVDC system has been evaluated under various fault type condition to examine the efficacy of the proposed method. The performance of the proposed method is investigated using MATLAB/Simulink environment.
Keywords :
HVDC power transmission; approximation theory; fault diagnosis; neural nets; particle swarm optimisation; pattern classification; power engineering computing; probability; smoothing methods; HVDC system; PNN; artificial neural network; high voltage direct current; optimal classifier approximation; particle swarm optimization; probabilistic neural network; smart fault classification; smoothing factor; HVDC transmission; Harmonic analysis; Mathematical model; Neural networks; Particle swarm optimization; Probabilistic logic; Rectifiers; Fault Classification; HVDC System; PNN; PSO;
Conference_Titel :
Smart Grids (ICSG), 2012 2nd Iranian Conference on
Conference_Location :
Tehran
Print_ISBN :
978-1-4673-1399-5