Explainable AI for Intelligent Decision Support in Operations & Maintenance of Wind Turbines

Chatterjee, Joyjit

Abstract

As global efforts in transitioning to sustainable energy sources rise, wind energy has become a leading renewable energy resource. However, turbines are complex engineering systems and rely on effective operations & maintenance (O&M) to prevent catastrophic failures in sub-components (gearbox, generator, etc.). Wind turbines have multiple sensors embedded within their sub-components which regularly measure key internal and external parameters (generator bearing temperature, rotor speed, wind speed etc.) in the form of Supervisory Control & Data Acquisition (SCADA) data. While existing studies have focused on applying ML techniques towards anomaly prediction in turbines based on SCADA data, they have not been supported with transparent decisions, owing to the inherent black box nature of ML models. In this project, we aim to explore transparent and intelligent decision support in O&M of turbines, by predicting faults and providing human-intelligible maintenance strategies to avert and fix the underlying causes. We envisage that in contributing to explainable AI for the wind industry, our method would help make turbines more reliable, encouraging more organisations to switch to renewable energy sources for combating climate change.

Citation

Chatterjee, J. (2020). Explainable AI for Intelligent Decision Support in Operations & Maintenance of Wind Turbines. In J. M. Alonso, & U. Cortés (Eds.), Proceedings of the 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI2020-proceedings) (53-54)

• Conference Name: 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020)
• Conference Location: Online
• Start Date: Aug 29, 2020
• End Date: Aug 30, 2020
• Acceptance Date: Aug 1, 2020
• Publication Date: 2020
• Deposit Date: Jul 11, 2022
• Publicly Available Date: Aug 31, 2022
• Publisher: Universidade de Santiago de Compostela
• Pages: 53-54
• Book Title: Proceedings of the 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI2020-proceedings)
• Public URL: https://hull-repository.worktribe.com/output/4028490
• Publisher URL: https://minerva.usc.es/xmlui/handle/10347/23263
• Related Public URLs: http://ecai2020.eu/