Title :
Neural network based fault diagnosis in an HVDC system
Author :
Etemadi, H. ; Sood, V.K. ; Khorasani, K. ; Patel, R.V.
Author_Institution :
Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, Que., Canada
Abstract :
This paper presents a neural network (NN) based method for fault classification in a power system. A new method of generating the training data is proposed which has the advantage that the total number of fault simulations needed to generate the training patterns is less than that required by the conventional training method. This is obtained at the cost of a time delay in the NN output response. The performance of the proposed method is investigated using the Matlab simulation model of a simple HVDC system
Keywords :
HVDC power transmission; fault simulation; learning (artificial intelligence); neural nets; power system simulation; power transmission faults; HVDC system; Matlab simulation model; fault classification; fault simulations; neural network based fault diagnosis; neural network output response; power system; time delay; training data generation; training patterns generation; Circuit faults; Delay effects; Fault diagnosis; HVDC transmission; Input variables; Intelligent networks; Neural networks; Power system modeling; Power system simulation; Training data;
Conference_Titel :
Electric Utility Deregulation and Restructuring and Power Technologies, 2000. Proceedings. DRPT 2000. International Conference on
Conference_Location :
London
Print_ISBN :
0-7803-5902-X
DOI :
10.1109/DRPT.2000.855665
