Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI

Chatterjee, Joyjit; Dethlefs, Nina

Abstract

© 2020 Published under licence by IOP Publishing Ltd. Machine learning techniques have been widely used for condition-based monitoring of wind turbines using Supervisory Control & Acquisition (SCADA) data. However, many machine learning models, including neural networks, operate as black boxes: despite performing suitably well as predictive models, they are not able to identify causal associations within the data. For data-driven system to approach human-level intelligence in generating effective maintenance strategies, it is integral to discover hidden knowledge in the operational data. In this paper, we apply deep learning to discover causal relationships between multiple features (confounders) in SCADA data for faults in various sub-components from an operational turbine using convolutional neural networks (CNNs) with attention. Our technique overcomes the black box nature of conventional deep learners and identifies hidden confounders in the data through the use of temporal causal graphs. We demonstrate the effects of SCADA features on a wind turbine's operational status, and show that our technique contributes to explainable AI for wind energy applications by providing transparent and interpretable decision support.

Citation

Chatterjee, J., & Dethlefs, N. (2020). Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI. Journal of Physics: Conference Series, 1618(2), Article 022022. https://doi.org/10.1088/1742-6596/1618/2/022022

• Journal Article Type: Conference Paper
• Acceptance Date: Aug 1, 2020
• Publication Date: Sep 22, 2020
• Deposit Date: Jun 8, 2022
• Publicly Available Date: Jul 18, 2022
• Journal: Journal of Physics: Conference Series
• Print ISSN: 1742-6588
• Electronic ISSN: 1742-6596
• Publisher: IOP Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 1618
• Issue: 2
• Article Number: 022022
• DOI: https://doi.org/10.1088/1742-6596/1618/2/022022
• Public URL: https://hull-repository.worktribe.com/output/3646870