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

Iman Kazemian, Murat Yildirim, Paritosh Ramanan

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.

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

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.

Paper Structure

This paper contains 34 sections, 40 equations, 7 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Problem Description
  3. Wind Farm Maintenance Scheduling Problem as a Mathematical Formulation
  4. Decision Variables
  5. Parameters
  6. Objective Function
  7. Constraints
  8. Production Management Constraints
  9. Maintenance Capacity Constraints
  10. Crew Logistics Constraints
  11. Non-negativity and Integrity Constraints
  12. Wind Farm Maintenance Scheduling Problem as a Learning Problem
  13. Multi-Step Embeddings for Wind Farm Maintenance
  14. Maintenance Cost Embeddings
  15. Idle Period Embeddings
  16. ...and 19 more sections

Figures (7)

  • Figure 1: Transitioning from data and math programming to AttenCOpt
  • Figure 2: Multi Step Embedding in AttenCOpt.
  • Figure 3: Scheduling Multiple Maintenance Teams
  • Figure 5: Performance of different cases when trained on respective cases. Each subfigure shows the performance of various cases when trained on the corresponding case indicated.
  • Figure : (a)
  • ...and 2 more figures