MUMBAI, India, Aug. 10 -- Intellectual Property India has published a patent application (202631086386 A) filed by Swagat Khatai; Dr Siddharth Swarup Rautaray; Dr Sarita Nanda; and Dr Swetaleena Sahoo on July 15, 2026, for An Intelligent Smart Grid Monitoring System Using Big Data Analytics And Machine Learning For Predictive Event And Anomaly Detection.
Inventors include Swagat Khatai; Dr Siddharth Swarup Rautaray; Dr Sarita Nanda; and Dr Swetaleena Sahoo.
The application for the patent was published on July 31, 2026, under issue no. 31/2026.
Abstract: An intelligent smart grid monitoring system using big data analytics and machine learning for predictive event and anomaly detection is disclosed. The system is designed to process large-scale phasor measurement unit (PMU) data for real-time monitoring and analysis of power grid conditions. The invention incorporates a pre- processing module that identifies missing and correct data records, followed by a data recovery mechanism to reconstruct incomplete datasets and ensure data reliability. A big data analytics module is employed to efficiently handle high-volume data streams and segregate outlier data from non-outlier data, thereby optimizing further analysis. The system further integrates a spiking neuron model that is trained using historical PMU event logs to capture temporal dynamics and patterns within the grid. The trained model performs predictive event detection and classifies anomalies into specific categories such as short circuit, trip events, line outages, islanding, heavy load, low load, and reactive power shortages. The classification module distinguishes between normal and abnormal conditions, enabling accurate identification of critical events. The proposed invention provides enhanced accuracy, reduced latency, and improved scalability in smart grid monitoring. By combining data recovery, big data processing, and advanced machine learning techniques, the system enables proactive detection of anomalies and supports efficient decision-making for grid stability and reliability. FIG.1
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