MUMBAI, India, Jan. 23 -- Intellectual Property India has published a patent application (202541117169 A) filed by Mr. Karthiban R; Ms. Akshaya VL; Ms. Avantika R; Mr. Dheena Dhayalan S; and Mr. Gopi Krishnan B, Coimbatore, Tamil Nadu, on Nov. 26, 2025, for 'phantom grid: an intelligent digital twin for energy systems.'
Inventor(s) include Mr. Karthiban R; Ms. Akshaya VL; Ms. Avantika R; Mr. Dheena Dhayalan S; and Mr. Gopi Krishnan B.
The application for the patent was published on Jan. 23, under issue no. 04/2026.
According to the abstract released by the Intellectual Property India: "Traditional power grid systems lack real-time monitoring capabilities, reactive maintenance approaches, and energy optimization features, leading to inefficient energy management and increased operational costs. Phantom Grid is an: innovative loT-based smart grid monitoring system that integrates digital twin technology with Al-powered predictive maintenance to address these challenges. The system comprises a comprehensive architecture incorporating real-time sensor data acquisition, digital twin synchronization, 3D visualization, and machine learning-based anomaly detection. The Phantom Grid platform obtained eighty-seven percent accuracy in anomaly detection employing the Isolation Forest algorithm for analysis of eight engineered features derived from real-time sensor readings taken at thirty-second intervals. Major contributions include scalable loT architecture, implementation of unsupervised machine learning, device health scoring algorithm, digital twin framework with three-dimensional visualization, and comprehensive cost-benefit analysis showing monthly savings of more than five hundred rupees per device through phantom load detection. Demonstrated results include phantom load detection, a thirty percent decrease in maintenance expenses, a thirty percent reduction in unplanned downtime, a four percent increase in equipment availability, and a seventy-five percent decrease in maintenance response time from forty-eight to twelve hours. Future directions involve examination of deep learning architectures, deployment of edge computing, integration of blockchain for secure data exchange, extension to commercial infrastructures, and reinforcement learning for optimizing automated controls. The system effectively integrates FastAPI backend, lnfluxDB time-series database, SQLite metadata storage, Node-RED for loT simulation, and Streamlit dashboard, i ~ providing a complete solution for mo dern smart grid management with predictive 1 ~ analytics capabilities."
Disclaimer: Curated by HT Syndication.
