Title :
Application of artificial neural network in noise mixed partial discharge recognition
Author :
Zheng, Zhong ; Tan, Kexiong
Author_Institution :
Dept. of Electr. Eng., Tsinghua Univ., Beijing, China
Abstract :
To test partial discharge (PD) recognition ability under different noise conditions, systemic research is carried out. In a noise-screened high voltage lab and using a high speed, wide-band digital measuring system, different kinds of PD current waveforms are recorded. Noises of different types are investigated. Then the PD signals are immersed into different noises with certain signal-noise ratios (SNR). By applying the segmented time domain data compression method, the features vectors of mixed waveforms are extracted. Employing a backpropagation algorithm, a feedforward triple-layered artificial neural network (ANN) program is generated and optimized. The mixed waveforms are tested and influence of each noise types in different SNR conditions are studied
Keywords :
automatic test software; backpropagation; data compression; feedforward neural nets; insulation testing; multilayer perceptrons; noise; partial discharge measurement; PD current waveforms; PD recognition ability; artificial neural network; backpropagation algorithm; features vectors; feedforward triple-layered artificial neural net; noise mixed partial discharge recognition; segmented time domain data compression method; signal-noise ratios; wide-band digital measuring system; Artificial neural networks; Current measurement; Noise measurement; Partial discharge measurement; Partial discharges; Signal to noise ratio; System testing; Velocity measurement; Voltage; Wideband;
Conference_Titel :
Electrical and Computer Engineering, 2001. Canadian Conference on
Conference_Location :
Toronto, Ont.
Print_ISBN :
0-7803-6715-4
DOI :
10.1109/CCECE.2001.933765
