Rapid Fault Diagnosis and Location Technique of Overhead Line based on Quantum Algorithm Optimization

An innovative approach for diagnosing the kind and phase of faults on overhead transmission lines (OHTLs) is presented in this research. Quantum-centric galactic search optimized support vector regression (QGSO-SVR) is proposed in this work for an effective fault detection process. Data instances, or signals, are obtained at the transmitting and receiving portions of the line to evaluate the effectiveness of the suggested method. Principal component analysis (PCA) is used to identify the important features from the raw data after wavelet transform (WT) has been used to reduce the signal's volume of data without compromising the detection accuracy. The suggested model is trained using frequencies across various fault regions. Further improvements are made to the SVR's fault detection technique according to quantum characteristics in the galactic optimization procedure. Estimating the location of the defect comes after identifying the faulty phase or phases. The suggested approach is used to analyze effectiveness regarding speed and precision using the Matlab tool. Extensive simulation was conducted for different levels of fault resistances (FR), inception angles, and positions. It was researched how noise affects the current and voltage measurements. Based on the analysis of the experiments, the proposed approach has provided 267.85 km in speed which seems to be the best technique for identifying fault diagnosis and location of overhead line.