An Integrated Optimization Framework for Multi-Component Predictive Analytics in Wind Farm Operations & Maintenance

TL;DR

This paper presents an integrated optimization framework combining real-time degradation models and mixed integer optimization to improve maintenance and operational decisions for wind farms with multiple interacting components.

Contribution

It introduces a novel system reliability-based optimization framework that accounts for component interactions and competing risks in wind turbine maintenance and operations.

Findings

Framework outperforms conventional methods in experiments

Accurately predicts component failure times and turbine failure risks

Optimizes maintenance schedules to enhance wind farm revenue

Abstract

Recent years have seen an unprecedented growth in the use of sensor data to guide wind farm operations and maintenance. Emerging sensor-driven approaches typically focus on optimal maintenance procedures for single turbine systems, or model multiple turbines in wind farms as single component entities. In reality, turbines are composed of multiple components that dynamically interact throughout their lifetime. These interactions are central for realistic assessment and control of turbine failure risks. In this paper, an integrated framework that combines i) real-time degradation models used for predicting remaining life distribution of each component, with ii) mixed integer optimization models and solution algorithms used for identifying optimal wind farm maintenance and operations is proposed. Maintenance decisions identify optimal times to repair every component, which in turn, determine the failure risk of the turbines. More specifically, optimization models that characterize a turbine's failure time as the first time that one of its constituent components fail - a systems reliability concept called competing risk is developed. The resulting turbine failures impact the optimization of wind farm operations and revenue. Extensive experiments conducted for multiple wind farms with 300 wind turbines - 1200 components - showcases the performance of the proposed framework over conventional methods.