Wind Turbine Blade Failure Diagnosis Based on SCADA Data-Driven Approach

Abstract

This research introduces an innovative data-driven approach for diagnosing wind turbine blade faults, specifically icing, using Supervisory Control and Data Acquisition (SCADA) system data. Wind turbine blades are particularly vulnerable to icing during cold weather conditions, which can severely impact their efficiency, performance, and overall reliability. Early detection of icing faults is crucial to avoid unscheduled downtimes, costly repairs, and extended periods of reduced energy production.

In this study, we propose a deep learning-based fault detection system designed to accurately identify and predict icing conditions on wind turbine blades. The system integrates Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (BiGRU) to analyze large-scale SCADA data. The CNN is employed to extract spatial features, while the BiGRU captures temporal dependencies in the dataset, making the system robust against varying operational conditions over time.

The methodology includes preprocessing the SCADA data to extract relevant features, followed by training and testing the deep learning models to classify and predict blade icing conditions. We compared the performance of this data-driven approach with traditional machine learning techniques such as support vector machines and decision trees, demonstrating significantly improved accuracy, robustness, and scalability in real-world applications.

The proposed system offers real-time monitoring capabilities and the ability to issue early warnings, allowing operators to take proactive measures to prevent severe blade damage. Furthermore, the integration of SCADA data ensures that the system can be seamlessly implemented in existing wind farms, eliminating the need for additional hardware. The results indicate that this approach leads to significant reductions in maintenance costs and enhances the overall operational efficiency of wind turbines.

This research contributes to the field of predictive maintenance in renewable energy systems by offering a practical and scalable solution for intelligent fault diagnosis. It represents an important step toward the future of autonomous wind turbine monitoring systems, combining the power of deep learning and SCADA data to optimize wind energy production, improve turbine reliability, and ensure long-term sustainability in wind energy generation.

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