Research on Fault Detection and Location Method of Medium and Low Voltage Distribution Network Based on Optical Fiber Sensing

Fault detection and localization in medium and low-voltage distribution networks are critical for ensuring the reliable operation of power systems. Traditional electrical measurement methods face challenges in accurately detecting fault locations and types, especially in complex and noisy environments, which often results in delays in fault identification and resolution. This paper proposes a fault detection and localization approach based on fiber optic sensing technology, which offers high sensitivity, strong anti-interference capabilities, and the ability to monitor physical parameters such as vibration, temperature, and strain in real time. A monitoring system utilizing fiber optic sensors is designed, and signal processing techniques such as wavelet and Fourier transforms are employed to extract key fault features from the collected data. Furthermore, machine learning models, including Gradient Boosting Machine (GBM) and Support Vector Machine (SVM), are applied for fault classification and precise localization. Experimental results demonstrate the effectiveness of the proposed method in detecting and localizing faults under various operational conditions, providing a more efficient and reliable alternative to traditional methods. This approach offers a promising solution for the intelligent operation and maintenance of distribution networks and contributes to the development of advanced fault diagnosis and decision-making systems.