Design of an intelligent decision model for power grid fault location and isolation based on topology analysis

Abstract

Detecting and isolating power grid faults quickly and efficiently ensures a constant power flow. The lack of speed and accuracy in conventional approaches to fault management highlights the importance of creating a smart decision model. The suggested approach uses topology analysis to better localize and isolate faults, reducing downtime and boosting grid performance. Real-time fault detection, precise fault location identification, and choosing the best isolation schemes are all difficulties in power grid fault management. Furthermore, significant issues need innovative solutions, such as managing the grid's complexity, responding to changing load conditions and guaranteeing cyber security. The present research unveils a new approach combining the Intelligent Decision Model integrated with the Supervisory Control and Data Acquisition system (SC-DAS). Here, Real-time decision-making, control, and monitoring are made possible by the SC-DAS system, which mediates between the intelligent decision model and the physical grid. In SC-DAS, Phasor Measurement Units (PMUs) are used for the real-time monitoring of the grid's electrical properties, which are collected using SCADA (Supervisory Control and Data Acquisition) systems. Further, SC-DAS uses decision-making models for instantaneous modifications and remedial measures of the grid's functioning. The grid's reliability uses Topological DAS for fault detection, localization, and isolation. Topological DAS represents a revolutionary approach that could significantly improve power grid reliability, reduce interruption frequency, and boost overall system performance in microgrids and renewable energy sources. The grid layouts, load circumstances, and fault scenarios used in the simulations help to improve the decision-making strategies in a controlled environment.