Power distribution fault diagnostic method based on machine learning technique

• Published in: 2019 IEEE PES/IAS PowerAfrica pp. 238 - 242
• Main Authors: Moloi, K., Akumu, A. O.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-08-2019
• Subjects: Circuit faults; Classification; Fault detection; Fault location; Feature extraction; Kernel; Power System; Relevance Vector Machine (RVM); Support vector machines; Wavelet transforms
• DOI: 10.1109/PowerAfrica.2019.8928633

Fault detection, classification and estimation in power systems is one of the most critical aspects of the engineering society. This goes beyond engineering factors to economic implications. Thus, proper applications of protection schemes are required to minimize the equipment damage resulting from a fault. This paper presents a method which tries to proactively detect, classify and estimate the position of the fault. A simplified two bus 132 kV system is modelled to study the effect of the proposed fault diagnostic method. The proposed method has a fault feature extraction technique done by stationary wavelet transform (SWT) on the fault signal. Relevance Vector Machine (RVM) and Support vector machine (SVM) schemes are applied for fault classification and detection. Fault location along the distribution line is achieved by using Support Vector Regression (SVR). The proposed method comprises of SWT-RVM and SVR schemes and tested using MATLAB.