AI-Enabled IoT-Based Smart Grid Fault Detection and Load Optimization for Renewable Energy Integration
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Abstract
Rising penetration of renewable energy sources presents significant challenges to smart grid operations, especially in fault detection, power quality, and cost-effective energy management. In this research, suggest a smart grid framework with AI-supported IoT that combines real-time fault detection solution and intelligent load monitoring for the smart grid for grid reliability and the renewable energy supply optimization. Electrical and environmental information such as voltage, current, frequency, total harmonic distortion, power quantities, power factor, and temperature are gathered by IoT sensors and processed through an efficient, toolbox-free AI-powered, computationally efficient artificial intelligence model. A custom multi-class K-Nearest Neighbors classifier is applied in fault detection with a high sensitivity classification accuracy score of 97.67%, perfect detection accuracy for harmonic and overcurrent failures and strong classification results for voltage sag, swell and frequency deviation events. Concurrently, a mixed-integer linear programming–based optimization approach is proposed to plan flexible load utilization and reduce the energy requirement associated with grid use in an environment with time-varying tariffs and renewable resources. The optimization results indicate that about 700 kWh of renewable energy is used effectively as opposed to 490 kWh of grid-imported energy, yet renewable energy curtailment is below 1%, indicating that demand–supply is good coordination. At the same time, a fault-aware optimization framework drastically reduces the total operational cost and renewable energy waste and grid dependency, while guaranteeing high reliability in fault detection. The solutions demonstrate that the method proposed is suitable for real-time rollout in intelligent, reliable, and sustainable smart grid systems.
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