Quantum-Inspired Genetic Programming for Single-End Line-to-Ground Fault Location in Transmission Lines with Resilience to High Impedance Faults

Accurately locating faults in transmission lines remains a major challenge, especially when relying on single-end measurements and the presence of High-Impedance Faults (HIFs), which are often difficult to detect using traditional techniques. This paper presents a fault location determination method for transmission lines based on Quantum-Inspired Genetic Programming (QIGP) that does not require a communication link, using measurements from only one end of the line. Current prediction techniques, such as neural networks and multi-layer regression, often rely on parameter tuning or intricate transformations of predictor or outcome variables, but still fail to deliver highly accurate results. The proposed QIGP model enhances the accuracy of fault location estimation, as seen in internal validation analysis. Genetic programming is used to derive a linear equation that improves the precision of fault location estimation. Quantum computing is leveraged to optimize the choice of top-performing solutions while managing parsimony pressure to minimize solution complexity. The results include various fault distances associated with different inception angles, load angles, and fault resistances.