MUMBAI, India, May 29 -- Intellectual Property India has published a patent application (202641061870 A) filed by Usha Rani Vinjamuri; and Dr J Praveen, Guntur, Andhra Pradesh, on May 15, for 'artificial intelligence driven smart electrical distribution system for real-time energy optimization and fault prediction.'

Inventor(s) include Usha Rani Vinjamuri; and Dr J Praveen.

The application for the patent was published on May 29, under issue no. 22/2026.

According to the abstract released by the Intellectual Property India: "The present invention discloses an Artificial Intelligence Driven Smart Electrical Distribution System (AI-SED) for real-time energy optimization and fault prediction in electrical power networks. The system integrates a dense network of IoT sensors, edge computing devices, advanced machine learning and deep learning models (including LSTM, CNN, GNN, Transformers), reinforcement learning agents, and digital twin technology to create an autonomous, self-optimizing, and self-healing grid. Key innovations include multi-modal data fusion for precise state estimation, predictive analytics for fault detection up to days in advance, dynamic energy routing to minimize losses and integrate renewables, and automated reconfiguration to isolate faults with minimal disruption. The architecture supports hybrid edge-cloud processing for ultra-low latency responses while enabling large-scale simulations in the digital twin for scenario planning and optimization. The system significantly reduces transmission losses (25-40%), outage durations (up to 60%), and maintenance costs through proactive interventions. It incorporates cybersecurity measures, explainable AI for operator trust, and federated learning for collaborative model improvement. Applicable to urban, rural, and industrial distribution networks, this invention paves the way for resilient, sustainable, and efficient next-generation smart grids, addressing critical challenges in modern energy infrastructure amid rising renewable adoption and demand variability. Extensive simulations on standard test feeders and case studies demonstrate superior performance over conventional and existing AI-assisted systems."

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