Published July 2019 | Version v1
Journal article

An opportunistic condition-based maintenance strategy for offshore wind farm based on predictive analytics

  • 1. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Avenue, Nanjing (China)
  • 2. School of Economics and Management, China University of Petroleum, 66 Changjiang West Road, Qingdao (China)

Description

Highlights: • A dynamic opportunistic condition-based maintenance strategy is formulated. • The varying maintenance lead time effect on the maintenance decision is analyzed. • A new maintenance basis is proposed to make maintenance plans for components. • We find that the maintenance cost is affected by the varying maintenance lead time. • The comparative analysis illustrates the capability of the proposed strategy. -- Abstract: The reduction of operation and maintenance cost plays a significant role in decreasing the cost of energy generated by offshore wind farm, which can be realized through better design of maintenance strategy. This paper proposes a dynamic opportunistic condition-based maintenance strategy for offshore wind farm by using predictive analytics. In the strategy, a new maintenance basis is developed by considering the varying maintenance lead time to make maintenance decisions for different wind turbine components. Meanwhile, the strategy also considers the economic dependence between the wind turbines and the components. We then present a maintenance model to derive the optimal maintenance plans for various turbine components under different weather and operation load conditions. A numerical example is used to illustrate the effectiveness of the maintenance model. It is found that the varying maintenance lead time has a significant effect on the annual maintenance cost which demonstrates the reasonableness of our proposed maintenance basis. Compared to the widely employed and the simple maintenance strategies, the proposed strategy can help reduce the annual maintenance cost by 39.24% and 32.46% respectively.

Additional details

Identifiers

DOI
10.1016/j.rser.2019.03.049;
PII
S1364032119301893;

Publishing Information

Journal Title
Renewable and Sustainable Energy Reviews
Journal Volume
109
Journal Page Range
p. 1-9
ISSN
1364-0321

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
55020259
Subject category
S17: WIND ENERGY;
Descriptors DEI
MAINTENANCE; OFFSHORE PLATFORMS; OPERATION; WIND TURBINE ARRAYS; WIND TURBINES
Descriptors DEC
EQUIPMENT; MACHINERY; TURBINES; TURBOMACHINERY

Optional Information

Copyright
Copyright (c) 2019 Elsevier Ltd. All rights reserved.