DETECTION AND CLASSIFICATION OF HIGH IMPEDANCE FAULT (HIF) IN THE 330kV HIGH VOLTAGE POWER SYSTEM NETWORK IN NIGERIAN WITH ANFIS ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS)

Abstract

The complexity of the power system network can be said to have resulted in the occurrence of faults of a complex nature. Misinterpretation and misrepresentation of faults have become common as power system blackouts are caused because of this issue. The paper presented the utilization of an adaptive neuro-fuzzy inference system for detecting and classifying high impedance fault (HIF) of the Nigerian 330kV transmission line connecting the Awka-New-heaven transmission system. The modeled network was simulated and the current signals at each fault class were simulated and sent to the Matlab environment for data analytics performance. The current signals and classification code were used as input and output variables to the adaptive neuro-fuzzy inference system (ANFIS) models respectively and the performance of the adaptive neuro-fuzzy inference system (ANFIS) model was determined. From the results presented, the maximum fault class at AB-g error value of 0.023 was very tolerable. Hence, the adaptive neuro-fuzzy inference system (ANFIS) model was sufficient for detecting and classifying high impedance faults (HIF) on high-voltage transmission lines.