Wavelet signal energy with RBFNN and GRNN for fault classification in transmission line with series compensator

Shaaban, S. A.; Abdel-Moamen, M.A.

doi:10.1109/ipact.2017.8244876

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…By combining the wavelet transform method with artificial intelligence (AI) methods, the accuracy of fault classification in electrical systems has been improved [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. The discrete wavelet transform (DWT) has been applied previously to decompose fault signals [11,13,14,17]. The DWT results were input into artificial neural networks (ANNs) to detect and classify faults in transmission lines.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Artificial Intelligence Methods for Fault Classification of the 115-kV Hybrid Transmission System

Klomjit

Ngaopitakkul

2020

Applied Sciences

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This research proposes a comparison study on different artificial intelligence (AI) methods for classifying faults in hybrid transmission line systems. The 115-kV hybrid transmission line in the Provincial Electricity Authority (PEA-Thailand) system, which is a single circuit single conductor transmission line, is studied. Fault signals in the transmission line were generated by the EMTP/ATPDraw software. Various factors such as fault location, type, and angle were considered. Then, fault signals were analyzed by coefficient details on the first scale of the discrete wavelet transform. Daubechies mother wavelet from MATLAB software was used to decompose the fault signal. The coefficient value of the mother wavelet behaved depending on the position, inception of fault angle, and fault type. AI methods including probabilistic neural networks (PNNs), back-propagation neural networks (BPNNs), and support vector machine (SVM) were used to identify faults. AI input used the maximum first peak coefficients of phase ABC and zero sequence. The results obtained from the study were found to be satisfactory with all AI methodologies having an average accuracy of more than 98% in the case study. However, the SVM technique can provide more accurate results than the PNN and BPNN techniques with less computation burden. Thus, it is suitable for being applied to actual protection systems.

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