Title :
Discrimination of partial discharge from noise in XLPE cable lines using a neural network
Author :
Katsuta, Ginzo ; Suzuki, Hiroshi ; Eshima, Hirotaka ; Endoh, Takeshi
Author_Institution :
Div. of Eng., Tokyo Electric Power Co. Inc., Japan
Abstract :
This paper describes an experimental study of the discrimination of partial discharge (PD) signals from external noise in a cross-linked polyethylene (XLPE) power cable by using a neural network (NN) system. Measurement of PD signal and external noise was carried out with a PD pulse recorder for a 66 kV XLPE cable with an artificial defect and a drill. The NN was a three-layer artificial neural system with feedforward connections, and its learning method was a backpropagation algorithm. Its input information was a combination of the discharge magnitude, the number of pulse counts, and the phase angle of applied voltage.
Keywords :
automatic testing; backpropagation; cable insulation; electric breakdown of solids; feedforward neural nets; insulation testing; partial discharges; polymers; power cables; 66 kV; XLPE; automatic testing; backpropagation algorithm; cable insulation; discharge magnitude; electric breakdown; feedforward; insulation testing; learning; neural network; noise; partial discharge; phase angle; polymers; power cable; pulse counts; pulse recorder; three-layer; Artificial neural networks; Backpropagation algorithms; Learning systems; Neural networks; Noise measurement; Partial discharge measurement; Partial discharges; Polyethylene; Power cables; Pulse measurements;
Conference_Titel :
Neural Networks to Power Systems, 1993. ANNPS '93., Proceedings of the Second International Forum on Applications of
Conference_Location :
Yokohama, Japan
Print_ISBN :
0-7803-1217-1
DOI :
10.1109/ANN.1993.264291
