Fault identification and classification algorithm for high voltage transmission lines based on a fuzzy-neuro-fuzzy approach

Traditional techniques are used for fault location detection in high-voltage transmission lines that mostly depend on traveling waves and impedance-based techniques suffer from large errors owing to the intricacy of fault modeling for various types of faults. Although single-line to ground faults are dominant in high-voltage transmission lines, fault resistance as well as fault inception angle might distort the current fault detection techniques. In addition, other types of faults exist and that raises the need to develop an accurate fault detection technique to minimize the recovery time. The current paper introduces a fuzzy and neuro-fuzzy algorithm to detect, analyze, and locate different faults taking place in high-voltage transmission lines. A MATLAB Simulink Model is used for analyzing different fault cases; fault detection and classification are done by the Fuzzy Interface System (FIS), while fault location detection is done using the Adaptive Neuro-Fuzzy Interface System (ANFIS). The introduced algorithm is evaluated via the Mean Square Error (MSE) technique. The results showed full success in detecting and identifying different fault types, with a 0.0042 validity performance factor for fault location detection using ANFIS.