Research on Grid Fault Diagnosis Algorithm Based on Real-Time Feedback

With the rapid advancement of global energy storage technologies, surging electricity demand, and the escalating penetration of renewable energy, microgrids have become pivotal in energy supply and resource management, while Electrical Power Grid Systems face growing complexity, diversified fault patterns, and accelerated fault propagation. Conventional methods of data analysis and periodic examination cannot satisfy the immediate requirements of providing quick fault localization, isolation and prevention of further spread which limits the capability of power utilities to recover grid safety and efficiency. The most common algorithms that are being researched include data-driven and intelligent algorithms including Neuromorphic Analytics, Variational Mode Decomposition (VMD) and Convolutional Neural Networks (CNN). These analyses are limited by a lack of flexibility to realistic grid-conditions, cross scenario generalizability, engineering feasibility, and the problem of large scale, accurately labelled data that limits the applications of real-time diagnosis efficacy. To deal with these issues, this paper will present an extensive review and outlook of intelligent real-time feedback based grid fault diagnostics, with perspective of moving away the individual algorithm approaches to an engineering based closed-circuit autonomous technical system. It presents the systematic examination of the data processing in real time (such as the technique of feature extracting and adaptive learning diagnostic model) and evaluates five important system structures, as well as examines their operations on the basis of practical application cases. The research should maximize the efficiency, flexibility, and robustness of grid diagnostic algorithms, provide technical advice on future research, and establish the basis of next generation high reliability and self healed power grid.