Attention is All You Need to Optimize Wind Farm Operations and Maintenance

TL;DR

This paper introduces a novel multi-head attention framework for wind farm operations and maintenance that drastically reduces solution times, guarantees feasibility, and improves solution quality over traditional methods.

Contribution

The paper presents a new AI-based decision-making framework using multi-head attention models that embed complex optimization constraints for wind farm O&M.

Findings

Reduces solution time from hours to seconds

Guarantees feasibility considering complex constraints

Improves solution quality over traditional MIP models

Abstract

Operations and maintenance (O&M) is a fundamental problem in wind energy systems with far reaching implications for reliability and profitability. Optimizing O&M is a multi-faceted decision optimization problem that requires a careful balancing act across turbine level failure risks, operational revenues, and maintenance crew logistics. The resulting O&M problems are typically solved using large-scale mixed integer programming (MIP) models, which yield computationally challenging problems that require either long-solution times, or heuristics to reach a solution. To address this problem, we introduce a novel decision-making framework for wind farm O&M that builds on a multi-head attention (MHA) models, an emerging artificial intelligence methods that are specifically designed to learn in rich and complex problem settings. The development of proposed MHA framework incorporates a number of modeling innovations that allows explicit embedding of MIP models within an MHA structure. The proposed MHA model (i) significantly reduces the solution time from hours to seconds, (ii) guarantees feasibility of the proposed solutions considering complex constraints that are omnipresent in wind farm O&M, (iii) results in significant solution quality compared to the conventional MIP formulations, and (iv) exhibits significant transfer learning capability across different problem settings.