MUMBAI, India, Aug. 31 -- Intellectual Property India has published a patent application (202621084230 A) filed by Dr. Sunny Kumar; Ojasvi Sanjay Sonone; Udaykumar Subhash Suryawanshi; Amey Sushil Chavekar; Dr. Sudhir K Bhil; and Dr. Sushama Rajaram Wagh on July 09, 2026, for Ai Based Grid Stability Enhancement Platform.
Inventors include Dr. Sunny Kumar; Ojasvi Sanjay Sonone; Udaykumar Subhash Suryawanshi; Amey Sushil Chavekar; Dr. Sudhir K Bhil; and Dr. Sushama Rajaram Wagh.
The application for the patent was published on August 28, 2026, under issue no. 35/2026.
Abstract: ABSTRACT AI based Grid Stability Enhancement Platform The present invention relates to an artificial intelligence (AI)-based grid stability enhancement platform designed to improve the operational reliability, resilience, efficiency, and sustainability of modern electrical power systems. The invention integrates advanced machine learning algorithms, deep neural networks, reinforcement learning techniques, predictive analytics, edge computing, Internet of Things (IoT) devices, synchronized phasor measurement units (PMUs), supervisory control and data acquisition (SCADA) systems, distributed energy resource (DER) controllers, and cloud-based analytics to continuously monitor, predict, and mitigate grid disturbances in real time. The platform acquires multidimensional operational data from generation plants, substations, transmission lines, renewable energy installations, battery energy storage systems, electric vehicle charging infrastructure, industrial loads, and consumer smart meters. AI models analyze historical and real- time data to forecast frequency deviations, voltage instability, power oscillations, transmission congestion, renewable generation uncertainty, demand fluctuations, and equipment failures before they occur. Based on predictive intelligence, the system autonomously recommends or executes corrective actions including adaptive load balancing, voltage regulation, distributed generation dispatch, battery scheduling, reactive power compensation, demand response activation, network reconfiguration, and fault isolation. A digital twin of the electrical grid continuously simulates future operating scenarios to evaluate alternative control strategies while minimizing operational risks. The platform employs self-learning mechanisms that improve prediction accuracy through continuous model retraining and feedback from operational events. Cybersecurity modules ensure secure communication and anomaly detection across interconnected grid assets. The proposed invention significantly enhances grid stability, minimizes outage duration, reduces operational costs, increases renewable energy integration capability, improves asset utilization, and supports the transition toward intelligent, autonomous, and carbon-neutral smart grids suitable for utility-scale deployment.
Disclaimer: Curated by HT Syndication.
