Machine Learning and IoT for Predictive Maintenance in Power Systems: Improving Fault Detection Accuracy and Grid Reliability

Abstract

Modern power systems face escalating operational complexity arising from aging infrastructure, rising electricity demand, and the large-scale integration of variable renewable generation, all of which increase the risk of equipment failure, unplanned outages, and degraded grid reliability. This study examined how the integration of machine learning (ML) and the Internet of Things (IoT) improves predictive maintenance, fault detection, fault classification, asset condition monitoring, and overall grid reliability in contemporary power systems. A quantitative, framework-based evaluation was conducted using an integrated architecture that combined IoT sensing through smart sensors and phasor measurement units, edge and cloud analytics, and a portfolio of supervised and deep-learning models including Random Forest, XGBoost, Long Short-Term Memory (LSTM) networks, and convolutional–recurrent hybrids. The analysis compared baseline reactive and time-based maintenance performance against the performance achieved under the AI- and IoT-enabled predictive framework across detection, classification, reliability, and economic dimensions. The findings demonstrated that fault detection accuracy improved from 68% under conventional baselines to 96% under the deep-learning hybrid configuration, while fault classification accuracy improved from 61% to 92%. Reliability and operational outcomes improved substantially, with outage duration reduced by 42%, equipment downtime reduced by 46%, and total maintenance expenditure reduced by 31%, alongside a marked decline in emergency repairs and a substantial extension of mean time between failures. Correlation analysis confirmed a strong relationship between detection accuracy and classification accuracy across model families, and the largest gains were associated with deep-learning architectures capable of learning temporal degradation patterns from high-frequency sensor streams. The study concluded that the convergence of ML and IoT transforms power-system maintenance from a reactive and schedule-driven activity into a proactive, condition-based capability that simultaneously improves reliability, reduces cost, and strengthens operational resilience. The findings contribute quantitative evidence supporting the broader adoption of intelligent predictive-maintenance frameworks across generation, transmission, and distribution infrastructure.