Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | APAP 2011 - Proceedings |
| Subtitle of host publication | 2011 International Conference on Advanced Power System Automation and Protection |
| Pages | 451-455 |
| Number of pages | 5 |
| Volume | 1 |
| DOIs | |
| Publication status | Published - 2011 |
| Event | 2011 International Conference on Advanced Power System Automation and Protection, APAP 2011 - Beijing, China Duration: 16 Oct 2011 → 20 Oct 2011 |
Other
| Other | 2011 International Conference on Advanced Power System Automation and Protection, APAP 2011 |
|---|---|
| Country | China |
| City | Beijing |
| Period | 16/10/11 → 20/10/11 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Safety, Risk, Reliability and Quality
Cite this
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FPGA implementation of neural network classifier for partial discharge time resolved data from magnetic probe. / Nguyen, Thi Ngoc Tho; Kumar Chakrabarty, Chandan; Ahmad, Basri Abd Ghani; Yap, Keem Siah.
APAP 2011 - Proceedings: 2011 International Conference on Advanced Power System Automation and Protection. Vol. 1 2011. p. 451-455 6180444.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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
Y1 - 2011
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
UR - http://www.scopus.com/inward/citedby.url?scp=84860689238&partnerID=8YFLogxK
U2 - 10.1109/APAP.2011.6180444
DO - 10.1109/APAP.2011.6180444
M3 - Conference contribution
SN - 9781424496198
VL - 1
SP - 451
EP - 455
BT - APAP 2011 - Proceedings
ER -