energies
Article
Optimal Maintenance Policy for Offshore Wind Systems
Vincent F. Yu
1,2
, Thi Huynh Anh Le
1,3
, Tai-Sheng Su
4
and Shih-Wei Lin
5,6,7,
*
Citation: Yu, V.F.; Le, T.H.A.; Su,
T.-S.; Lin, S.-W. Optimal Maintenance
Policy for Offshore Wind Systems.
Energies 2021, 14, 6082. https://
doi.org/10.3390/en14196082
Academic Editor: Eugen Rusu
Received: 9 August 2021
Accepted: 14 September 2021
Published: 24 September 2021
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1
Department of Industrial Management, National Taiwan University of Science and Technology,
Taipei106, Taiwan; vincent@mail.ntust.edu.tw (V.F.Y.); D10901814@mail.ntust.edu.tw(T.H.A.L.)
2
Center for Cyber-Physical System Innovation, National Taiwan University of Science and Technology,
Taipei 106, Taiwan
3
Faculty of Project Management, The University of Danang, University of Science and Technology,
Danang 550000, Vietnam
4
Department of Industrial Management, National Pingtung University of Science and Technology,
Pingtung 912, Taiwan; tyson@mail.npust.edu.tw
5
Department of Information Management, Chang Gung University, Taoyuan 333, Taiwan
6
Department of Industrial Engineering and Management, Ming Chi University of Technology,
New Taipei 243, Taiwan
7
Department of Neurology, Linkou Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
* Correspondence: swlin@mail.cgu.edu.tw; Tel.: +886-3-211-8800
Abstract: Employing maintenance threshold plays a critical step in determining an optimal main-
tenance policy for an offshore wind system to reduce maintenance costs while increasing system
reliability. Considering the limited works on this topic, we propose a two-stage procedure to deter-
mine the optimal maintenance thresholds for multiple components of an offshore wind power system
in order to minimize maintenance costs while achieving the highest possible system reliability. First,
using genetic algorithms, a dynamic strategy is developed to determine the maintenance thresholds
of individual components where the cost of maintenance and the rate of failure are critical. Then,
fuzzy multi-objective programming is applied to find the system’s optimal maintenance threshold
considering all components. A variety of factors including weather conditions, system reliability,
power generation losses, and electricity market price are carefully considered to enhance the system’s
reliability and reduce the costs of maintenance. When maintenance threshold results are compared,
component-wise versus system-wise, an average system savings of 1.19% for maintenance cost is
obtained while the system reliability is increased by 1.62% on average.
Keywords: offshore wind system; fuzzy multi-objective programming; failure rate; maintenance
threshold; optimal maintenance policy
1. Introduction
Global warming and conventional energy resources have been in the spotlight due to
the latter’s scarcity and their negative environmental impact. The situation has led to new
energy resources being discovered to improve human life and the natural environment.
Renewable energy resources have been promoted for their tremendous potential as a
solution for alleviating environmental pollution caused by conventional energy resources.
Among the former, wind energy has received seemingly the most interest and investment
from many countries. The Global Wind Energy Council (GWEC) reported that annual
capacity installation rose from 2015 to 2020 (as shown in Figure 1)[1]. For instance, from the
GWEC report in 2020, the installed capacity of wind farms has increased from 435,284 GW
in 2015 to more than 650 GW in 2019 and about 744 GW in 2020. Wind farms reached a
global installed capacity of 744 GW in 2020, with a net installed capacity of 93 GW added
that year [1]. Offshore wind systems are located in harsher conditions, so the costs of
installation and maintenance are very high [2,3]. Lin et al. [4] noted that the maintenance
of an offshore wind turbine accounts for more than 30% of the total cost of its life cycle.
Energies 2021, 14, 6082. https://doi.org/10.3390/en14196082 https://www.mdpi.com/journal/energies