TransWind: A Vision Transformer Framework for Wind Turbine Fault Diagnosis on Supervisory Control and Data Acquisition System

Abstract

Accurate fault detection in wind turbines is essential for maximizing operational efficiency and reducing maintenance expenditures. This paper presents TransWind, a novel Vision Transformer (ViT)-based framework designed specifically for analyzing SCADA data to pinpoint and diagnose issues in wind turbines. Unlike traditional machine learning models, TransWind leverages the attention mechanisms of ViTs to capture complex temporal and spatial relationships within SCADA time including parameters for example rotational speed, generator -series data, temperature, and electricity output. This unique capability allows for precise identification of anomalies and their underlying causes. The innovation of TransWind lies in its capacity to integrate explainable AI (XAI) techniques, including Self-Attention Attribution, to provide transparency in fault predictions, enabling maintenance teams to focus on critical system elements. The proposed framework will assessed on publicly available SCADA datasets, demonstrating a fault detection accuracy improvement of 8 TransWind exhibits robustness in handling noisy and incomplete -12% compared to state-of-the-art models. Furthermore, SCADA data, a common challenge in real-world deployments. This research highlights the transformative potential of transformer-based architectures in renewable energy fault diagnostics. By enhancing detection accuracy and interpretability, TransWind offers a scalable solution for predictive maintenance, reducing turbine downtime and operational costs while advancing AI-driven sustainability in wind energy systems.