MUMBAI, India, June 26 -- Intellectual Property India has published a patent application (202641070992 A) filed by Vidyavardhaka College Of Engineering on June 08, 2026, for Ai Based Fault Detection And Isolation In A Smart Microgrid.
Inventors include Akash K S; Adarsh Gowda; Ruchi K A; Harsha P; and Mohammed Jalaluddin.
The application for the patent was published on June 19, 2026, under issue no. 25/2026.
Abstract: Smart microgrids have emerged as critical components of modern electrical power systems, integrating renewable energy sources, energy storage systems, and advanced control mechanisms. The increasing penetration of distributed energy resources and bidirectional power flows in microgrids presents significant challenges for traditional threshold-based protection schemes, often leading to false tripping, delayed fault detection, and system-wide outages. This paper provides a comprehensive review of artificial intelligence (AI) approaches applied to fault detection and isolation in smart microgrids, with particular emphasis on embedded implementation and real-time deployment. We systematically analyze machine learning, deep learning, and lightweight AI techniques suitable for resource-constrained hardware platforms such as microcontrollers. The review examines various fault types including short circuits, overloads, and open circuits, while addressing the critical gap between research-grade simulations and practical embedded deployment. Key findings indicate that AI implementation using lightweight classifiers such as Random Forest with physics-derived feature sets enables real-time fault detection with detection accuracies exceeding 99% on embedded platforms. However, challenges remain in model optimization for resource constraints, real-time inference latency, and integration with existing microgrid control systems. This review identifies emerging trends including physics-informed lightweight models, edge AI deployment strategies, and self-healing microgrid capabilities, while highlighting future research directions toward cost-effective, scalable embedded protection systems. KEYWORDS: Artificial intelligence, edge computing, embedded systems, ESP32, INA219, fault detection, fault isolation, machine learning, microgrid, real-time dashboard, smart grid, Random Forest
Disclaimer: Curated by HT Syndication.
