Natural Language Generation for Operations and Maintenance in Wind Turbines

Chatterjee, Joyjit; Dethlefs, Nina

Abstract

Wind energy is one of the fastest-growing sustainable energy sources in the world but relies crucially on efficient and effective operations and maintenance to generate sufficient amounts of energy and reduce downtime of wind turbines and associated costs. Machine learning has been applied to fault prediction in wind turbines, but these predictions have not been supported with suggestions on how to avert and fix faults. We present a data-to-text generation system using transformers to produce event descriptions from SCADA data capturing the operational status of turbines and proposing maintenance strategies. Experiments show that our model learns feature representations that correspond to expert judgements. In making a contribution to the reliability of wind energy, we hope to encourage organisations to switch to sustainable energy sources and help combat climate change.

Citation

Chatterjee, J., & Dethlefs, N. (2019, December). Natural Language Generation for Operations and Maintenance in Wind Turbines. Paper presented at NeurIPS 2019 Workshop: Tackling Climate Change with Machine Learning, Vancouver Convention Center, British Columbia, Canada

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: NeurIPS 2019 Workshop: Tackling Climate Change with Machine Learning
• Start Date: Dec 14, 2019
• End Date: Dec 14, 2019
• Deposit Date: Jul 11, 2022
• Public URL: https://hull-repository.worktribe.com/output/4028459
• Publisher URL: https://www.climatechange.ai/papers/neurips2019/9