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Smart fault classification in HVDC system based on optimal probabilistic neural networks

Khodaparastan, M ; Sharif University of Technology | 2012

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  1. Type of Document: Article
  2. Publisher: IEEE , 2012
  3. Abstract:
  4. 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
  5. Keywords:
  6. Fault classification ; Fault types ; High voltage direct current ; HVDC systems ; MATLAB/Simulink environment ; Network-based ; Optimal classifiers ; Optimal values ; PNN ; Probabilistic neural networks ; PSO ; Smoothing factors ; HVDC power transmission ; Particle swarm optimization (PSO) ; Smart power grids ; Neural networks
  7. Source: 2012 2nd Iranian Conference on Smart Grids, ICSG 2012, 23 May 2012 through 24 May 2012 ; May , 2012 , Page(s): 1 - 4 ; 9781467313995 (ISBN)
  8. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6243541