Early prediction of wind turbine anomalies using 1D-CNN and temporal feature engineering on multi-source SCADA data.

Early and accurate detection of anomalies in wind turbines is critical for ensuring system reliability, minimizing unplanned downtime, and reducing maintenance costs in large-scale renewable energy infrastructures. In this study, we propose a robust deep learning framework for wind turbine anomaly detection, leveraging a newly constructed dataset that integrates Supervisory Control and Data Acquisition (SCADA) data from three distinct wind farms. Extensive preprocessing and domain-specific temporal feature engineering were employed to capture complex patterns and enhance model generalizability across heterogeneous data sources. A comparative evaluation of several state-of-the-art deep learning models—including 1D Convolutional Neural Networks (1D-CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Units (GRU); was conducted using standard classification metrics. Among these, the 1D-CNN consistently outperformed the recurrent models, achieving an accuracy and F1-score of 85%. This performance is attributed to the model’s capacity to effectively learn localized temporal dynamics in multivariate time series data. The findings demonstrate that a carefully designed 1D-CNN architecture, combined with strategic temporal feature engineering and multi-source data fusion, offers a scalable and accurate solution for early fault detection in wind turbine systems. This work lays the foundation for intelligent condition monitoring systems in the renewable energy sector. We then propose and evaluate a hybrid CNN-LSTM architecture augmented with an attention mechanism. This model leverages both CNN’s strength of extracting local features. And LSTM capacity to capture temporal dependencies, while the attention layer dynamically focuses on the most important segments of the sequence. Our findings show that the suggested hybrid model performs noticeably better than the independent base models, attaining higher generalization and accuracy. This work advances wind turbine fault detection through creating a diverse, multi-source wind farm dataset for superior generalizability; pioneering a reproducible benchmarking framework across deep learning models on heterogeneous data; and a hybrid CNN-LSTM with attention, surpassing baselines by 2% while enabling practical decision-making.