An India-Centric Generative AI-Based Smart Grid Data Modeling Lab Using a HILLTOP+ Inspired Decision Intelligence Framework
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An India-Centric Generative AI-Based Smart Grid Data Modeling Lab Using a HILLTOP+ Inspired Decision Intelligence Framework
Rajasivasairaj1, Palem Bhadra Reddy2, Pandula Shiva Kumar3, Mr. S. Mohan4, Dr. T. Kumanan5, Dr.
M. Nisha6
1,2,3 UG Students, 4,6 Assistant Professor ,Dept. of Cybersecurity4 & CSE6 , 5 Professor –,Dept. of CSE
Dr. MGR Research and Educational Institute of Technology, Maduravoyal, Chennai – 95, Tamil Nadu.
Abstract—This paper presents a novel cost-effective generative AI based smart grid decision intelligence system for India centric feeder analysis. The system addresses key issues affecting smart grid performance in the Indian grid, including frequency variation, feeder overloading, renewable energy intermittency, and evening peak demands. The proposed system follows the HILLTOP+ digital testbed concept with a module-based architecture for input handling, synthetic data generation, simulation, analytics, decision support, and output handling. The system generates synthetic multivariate time series for voltage, demand, solar power, wind power, and frequency signals. It simulates stress cases, quantifies risk before and after control actions, and performs forecasting, fault detection, dispatch recommendation, N-1 contingency analysis, multi-feeder simulation, and recovery analysis. The novelty lies in a cost-effective decision intelligence workflow for smart grid performance analysis in the Indian grid context.
Index Terms—Smart Grid, Synthetic Data, Decision Support, Scenario Simulation, Risk Analytics, India, Feeder Operations, Dispatch Control.
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