Abstract
| Language | English |
|---|---|
| Title of host publication | Power and Energy Society General Meeting, 2012 IEEE |
| Publisher | IEEE |
| DOIs | |
| Status | Published - 2012 |
| Event | 2012 IEEE Power and Energy Society General Meeting - San Diego, UK United Kingdom Duration: 21 Jul 2012 → 25 Jul 2012 |
Conference
| Conference | 2012 IEEE Power and Energy Society General Meeting |
|---|---|
| Country | UK United Kingdom |
| City | San Diego |
| Period | 21/07/12 → 25/07/12 |
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A new approach to EHV transmission line fault classification and fault detection based on the wavelet transform and artificial intelligence. / Chen, J.; Aggarwal, R.K.
Power and Energy Society General Meeting, 2012 IEEE. IEEE, 2012.Research output: Chapter in Book/Report/Conference proceeding › Other chapter contribution
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TY - CHAP
T1 - A new approach to EHV transmission line fault classification and fault detection based on the wavelet transform and artificial intelligence
AU - Chen, J.
AU - Aggarwal, R.K.
PY - 2012
Y1 - 2012
N2 - This paper describes a novel fault classification and fault detection scheme using current signal data from only one end of a transmission system. Firstly, the measured current signals are decomposed using the wavelet transform to obtain the necessary frequency details and then the spectral energy for a chosen number of wavelet coefficients are calculated using a moving short time window; this forms the feature extraction stage, which in turn, defines the inputs for the neural network which is used for classifying the types of fault. After the fault type is identified, the proposed scheme selects the specific neural network of the fault type to distinguish between internal and external faults by utilizing the same patterns features extracted from the previous stage. The input features comprise both the high and low frequency components to enhance performance of the scheme. An extensive series of studies for a whole variety of different system and fault conditions clearly show that the performance of the scheme both for fault classification and detection is accurate and robust.
AB - This paper describes a novel fault classification and fault detection scheme using current signal data from only one end of a transmission system. Firstly, the measured current signals are decomposed using the wavelet transform to obtain the necessary frequency details and then the spectral energy for a chosen number of wavelet coefficients are calculated using a moving short time window; this forms the feature extraction stage, which in turn, defines the inputs for the neural network which is used for classifying the types of fault. After the fault type is identified, the proposed scheme selects the specific neural network of the fault type to distinguish between internal and external faults by utilizing the same patterns features extracted from the previous stage. The input features comprise both the high and low frequency components to enhance performance of the scheme. An extensive series of studies for a whole variety of different system and fault conditions clearly show that the performance of the scheme both for fault classification and detection is accurate and robust.
UR - http://www.scopus.com/inward/record.url?scp=84870611326&partnerID=8YFLogxK
UR - http://dx.doi.org/10.1109/PESGM.2012.6344762
U2 - 10.1109/PESGM.2012.6344762
DO - 10.1109/PESGM.2012.6344762
M3 - Other chapter contribution
BT - Power and Energy Society General Meeting, 2012 IEEE
PB - IEEE
ER -