Data-Driven Predictive Maintenance of Renewable-Rich Power Systems for Improved Reliability and Carbon Emission Reduction
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Abstract
The accelerating integration of wind and solar generation into modern power systems introduces significant variability, uncertainty, and additional stress on grid assets, complicating conventional operation-and-maintenance (O&M) practices. Reactive and time-based preventive maintenance strategies, still dominant in many utilities, result in unplanned outages, excessive spare-part costs, and avoidable greenhouse-gas emissions associated with inefficient plant dispatch and diesel back-up generation. This paper proposes a data-driven predictive maintenance (PdM) framework for renewable-rich power systems that combines multi-source IoT sensor data, weather and historical O&M records with a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) architecture augmented by an attention mechanism for fault classification and Remaining Useful Life (RUL) estimation. The framework further embeds reliability-index computation (SAIDI, SAIFI, MTBF, MTTR) and a carbon-emission impact model to quantify the operational and environmental benefits of predictive intervention. The proposed model is benchmarked against Support Vector Machine (SVM), Random Forest, XGBoost and standalone LSTM baselines on a representative wind–solar–battery test system. Results indicate that the proposed CNN-LSTM-Attention model achieves the highest fault-classification accuracy (96.8%) and area under the ROC curve (0.978), while reducing SAIDI by approximately 39% and SAIFI by approximately 45% relative to a reactive-maintenance baseline. The associated carbon-emission analysis shows a cumulative avoided-emission trend of roughly 11 tonnes CO2 per month of operation as unplanned outages and inefficient back-up dispatch are reduced
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