# Fleetwide data-enabled reliability improvement of wind turbines

## TL;DR

This paper presents a comprehensive approach combining laboratory tests and real-world data analysis to understand and optimize wind turbine loads, aiming to improve reliability and reduce maintenance costs in challenging locations.

## Contribution

It introduces a multitiered methodology for analyzing wind farm load spectra and optimizing control protocols without full load understanding, demonstrated through case studies.

## Key findings

- Load spectrum analysis links operational zones with load distributions.
- A control protocol is developed to prevent cascading failures during storms.
- Operational zones are detected using high-frequency data analysis.

## Abstract

Wind farms are an indispensable driver toward renewable and nonpolluting energy resources. However, as ideal sites are limited, placement in remote and challenging locations results in higher logistics costs and lower average wind speeds. Therefore, it is critical to increase the reliability of the turbines to reduce maintenance costs. Robust implementation requires a thorough understanding of the loads subject to the turbine's control. Yet, such dynamically changing multidimensional loads are uncommon with other machinery, and generally underresearched. Therefore, a multitiered approach is proposed to investigate the load spectrum occurring in wind farms. Our approach relies on both fundamental research using controllable test rigs, as well as analyses of real-world loading conditions in high-frequency supervisory control and data acquisition data. A method is introduced to detect operational zones in wind farm data and link them with load distributions. Additionally, while focused research further investigates the load spectrum, a method is proposed that continuously optimizes the farm's control protocols without the need to fully understand the loads that occur. A case of gearbox failure is investigated based on a vast body of past experiments and suspect loads are identified. Starting from this evidence on the cause and effects of dynamic loads, the potential of our methods is shown by analyzing real-world farm loading conditions on a steady-state case of wake and developing a preventive row-based control protocol for a case of cascading emergency brakes induced by a storm.