Abstract

Advancements in wind turbine condition monitoring systems over the last decade have made it possible to optimise operational performance and reduce costs associated with component failure and other unplanned maintenance activities. While much research focuses on providing more automated and accurate fault diagnostics and prognostics in relation to predictive maintenance, efforts to quantify the impact of such strategies have to date been comparatively limited. Through time-based simulation of wind farm operation, this paper quantifies the cost benefits associated with predictive and condition-based maintenance strategies, taking into consideration both direct O&M costs and lost production. Predictive and condition-based strategies have been modelled by adjusting known component failure and repair rates associated with a more reactive approach to maintenance. Results indicate that up to 8% of direct O&M costs can be saved through early intervention along with up to 11% reduction in lost production, assuming 25% of major failures of the generator and gearbox can be diagnosed through advanced monitoring and repaired before major replacement is required. Condition-based approaches can offer further savings compared to predictive strategies by utilising more component life before replacement. However, if weighing up the risk between component failure and replacing a component too early, results suggest that it is more cost effective to intervene earlier if heavy lift vessels can be avoided, even if that means additional major repairs are required over the lifetime of the site.

Highlights

  • Accepted: 6 August 2021Maintenance strategies have evolved over the last decade in line with increased turbine size, changes in wind turbine technology and reduction in capital expenditure relative to power output [1,2]

  • Focusing on offshore wind farms, this paper aims to quantify the operational cost savings of Published: 11 August 2021

  • Two key parameters were analysed for a variety of sites—Pf to simulate a range of additional failures per turbine per year that are diagnosed early and are repairable without using a heavy lift vessel (HLV), and PRUL to simulate how early the component was repaired in relation to the expected remaining useful life before failure, at which time a HLV would be required

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Summary

Introduction

Accepted: 6 August 2021Maintenance strategies have evolved over the last decade in line with increased turbine size, changes in wind turbine technology and reduction in capital expenditure relative to power output [1,2]. One way of achieving this is by employing improved maintenance strategies, made possible with increased monitoring capabilities, improved digitilisation and more in-depth analysis of data [4] This allows engineers and operators to assess asset performance, understand reliability and make informed maintenance decisions that can drive down costs over the lifetime of the site, maximising availability. With this in mind, machine learning has a large role to play in automating both monitoring and analysis activities across a fleet, allowing engineers to focus their efforts on more complex decisions regarding faults and under-performance. Focusing on offshore wind farms (where the biggest costs savings can be made), this paper aims to quantify the operational cost savings of Published: 11 August 2021

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