Title :
Application of a radial basis function (RBF) neural network for fault diagnosis in a HVDC system
Author :
Narendra, K.G. ; Sood, V.K. ; Khorasani, K. ; Patel, R.
Author_Institution :
Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, Que., Canada
Abstract :
The application of a radial basis function (RBF) neural network (NN) for fault diagnosis in a HVDC system is presented in this paper. To provide a reliable pre-processed input to the RBF NN, a new pre-classifier is proposed, This pre-classifier consists of an adaptive filter (to track the proportional values of the fundamental and average components of the sensed system variables), and a signal conditioner which uses an expert knowledge base (KB) to aid the pre-classification of the signal. The proposed method of fault diagnosis is evaluated using simulations performed with the EMTP package
Keywords :
HVDC power transmission; adaptive filters; fault diagnosis; fault location; feedforward neural nets; knowledge based systems; power system analysis computing; signal processing; EMTP package; HVDC system; adaptive filter; expert knowledge base; fault diagnosis; pre-classifier; pre-processed input; radial basis function neural network; sensed system variables; signal conditioner; Control systems; Fault diagnosis; HVDC transmission; Intelligent networks; Least squares approximation; Neural networks; Neurons; Power system faults; Signal processing; Transfer functions;
Conference_Titel :
Power Electronics, Drives and Energy Systems for Industrial Growth, 1996., Proceedings of the 1996 International Conference on
Conference_Location :
New Delhi
Print_ISBN :
0-7803-2795-0
DOI :
10.1109/PEDES.1996.537295
