AI-Driven Predictive Maintenance Framework for Fault Detection and Performance Optimization in Smart Grid and Renewable Energy Systems

Abstract

The growing involvement of renewable energy sources into smart grid systems has escalated the complexity of the systems, and reliance on top-quality predictive maintenance programs are necessary in order to meet reliability demand, operational performance, and cost-competitiveness. The current paper provides an AI-based element system of predictive maintenance focused on fault diagnosis and performance upgrade of smart grid and renewable energy systems. The offered method is based on a hybrid representation of deep learning that is based on the integration of the Convolutional Neural Network (CNN) model of spatial feature extraction and Long Short-Term Memory (LSTM) networks as models of the temporal dependence. Data describing these 200+ data inputs can be varied or put in various forms, whether sensor data, the Supervisory Control and Data Acquisition (SCADA) logs and weather predictions are multi-modal in nature, and preprocessed using an optimized feature selection pipeline including Mutual Information and Principal Component Analysis (PCA) to keep computation time at a minimum. Experimental results, on a simulated data set modeled after the IEEE 39-bus system with renewable energy integration and NREL environmental benchmarks, reported an accuracy of 97.8% in fault detection, a decrease in false alarms of 21 percent and an increase in the predictive maintenance lead time of 14 hours over baseline models. The outcomes confirm the framework ability to offer the scalable, real-time decision support to proactive asset management, which contributes to the resilience and performance improvement in contemporary energy infrastructures.