A Machine Learning-based Approach to Detection of Fault Locations in Transmission Networks

Fast and accurate fault location detection methods are crucial for power system maintenance crews to isolate permanent faults, which in turn helps to minimize downtime and improve system reliability. Therefore, it is vital from the perspective of power system reliability planners to develop an approach that can quickly detect and locate faults. This paper proposes an approach to locate faults in transmission networks based on machine learning using high-precision data gathered through Real-time Digital Simulators (RTDS). The data-driven machine learning algorithms are provided transient data collected during the fault to reliably predict fault locations, via temporal voltage phasors captured through Phasor Measurement Units (PMUs). A Python automated real-time simulation is conducted to simulate hundreds of three-phase fault scenarios considering different loading levels, fault locations, durations, and resistances. To facilitate the data gathering process, a lab-scale data acquisition and storage framework is developed with the integration of RTDS, virtual Phasor Data Concentrator (OpenPDC), and MySQL. The proposed approach is demonstrated through a comprehensive study on the WECC 9-Bus System. A comparative study among different machine learning algorithms is conducted to determine the superior algorithm for fault location detection.