TY - GEN
T1 - FPGA implementation of neural network classifier for partial discharge time resolved data from magnetic probe
AU - Nguyen, Thi Ngoc Tho
AU - Kumar Chakrabarty, Chandan
AU - Ahmad, Basri Abd Ghani
AU - Yap, Keem Siah
PY - 2011/12/1
Y1 - 2011/12/1
N2 - Partial discharge (PD) is a common reason that causes electrical breakdown in high voltage underground XLPE cables. This paper proposes a concept of how to build an on-line, on-site system that is able to diagnose the severity of PD activities in XLPE cable as well as differentiate different types of PD signals. The system consists of magnetic probes, low noise amplifier, 3GSPS analog to digital converter (ADC) and a field programmable gate array (FPGA) board. The energy of PD signals is used to assess the severity of PD activities and artificial neural network (ANN) is used to classify different types of PD waveforms. In addition, wavelet transform is used to clean the time-resolved input signals and statistical method is used to extract important features of PD signals to fetch into neural network. The training of ANN is done on personal computer. The prototype and results of the research is elaborated in this paper.
AB - Partial discharge (PD) is a common reason that causes electrical breakdown in high voltage underground XLPE cables. This paper proposes a concept of how to build an on-line, on-site system that is able to diagnose the severity of PD activities in XLPE cable as well as differentiate different types of PD signals. The system consists of magnetic probes, low noise amplifier, 3GSPS analog to digital converter (ADC) and a field programmable gate array (FPGA) board. The energy of PD signals is used to assess the severity of PD activities and artificial neural network (ANN) is used to classify different types of PD waveforms. In addition, wavelet transform is used to clean the time-resolved input signals and statistical method is used to extract important features of PD signals to fetch into neural network. The training of ANN is done on personal computer. The prototype and results of the research is elaborated in this paper.
UR - http://www.scopus.com/inward/record.url?scp=84860689238&partnerID=8YFLogxK
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U2 - 10.1109/APAP.2011.6180444
DO - 10.1109/APAP.2011.6180444
M3 - Conference contribution
AN - SCOPUS:84860689238
SN - 9781424496198
T3 - APAP 2011 - Proceedings: 2011 International Conference on Advanced Power System Automation and Protection
SP - 451
EP - 455
BT - APAP 2011 - Proceedings
T2 - 2011 International Conference on Advanced Power System Automation and Protection, APAP 2011
Y2 - 16 October 2011 through 20 October 2011
ER -