Abstract
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.
Highlights
Many big cities have undergone economic and infrastructure growth, with a rising population density due to migration from rural to urban areas
A technique for classification of internal and external faults of protection zones in transmission lines has been proposed, in which fault signals were decomposed by the wavelet transform, and high frequency components and spectral energy were input into a support vector machine (SVM) to detect faults and classify them, respectively
The coefficients and behavior of discrete wavelet transform (DWT) were analyzed for fault classification using various artificial intelligence (AI) methods
Summary
Many big cities have undergone economic and infrastructure growth, with a rising population density due to migration from rural to urban areas. A wavelet transform was applied to detect and classify faults in transmission lines [3,4,5,6,7,8,9]. Detection and classification of faults in transmission lines were performed by introducing a novel method based on power spectral density (PSD) in time and frequency. The DWT results were input into artificial neural networks (ANNs) to detect and classify faults in transmission lines. A technique for classification of internal and external faults of protection zones in transmission lines has been proposed, in which fault signals were decomposed by the wavelet transform, and high frequency components and spectral energy were input into a support vector machine (SVM) to detect faults and classify them, respectively.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
