Research on maintenance strategy optimization of wind turbines based on operation risk

2015 ◽  
Author(s):  
Haifeng Liang ◽  
Lei Yan ◽  
Zijing Zhen ◽  
Lijie Yu
Keyword(s):  
Wind Turbines ◽  
Maintenance Strategy ◽  
Operation Risk

Prognosis Informed Design Framework for Operation and Maintenance of Wind Turbines

2012 ◽  
Author(s):  
Prasanna Tamilselvan ◽  
Yibin Wang ◽  
Pingfeng Wang
Keyword(s):  
Decision Making ◽  
Wind Turbines ◽  
High Performance ◽  
Wind Farm ◽  
Economic Loss ◽  
Environmental Benefits ◽  
Maintenance Strategy ◽  
System Failures ◽  
Operation And Maintenance ◽  
Failure Prognosis

Advances in high performance sensing and signal processing technology enable the development of failure prognosis tools for wind turbines to detect, diagnose, and predict the system-wide effects of failure events. Although prognostics can provide valuable information for proactive actions in preventing system failures, the benefits have not been fully utilized for the operation and maintenance decision making of wind turbines. This paper presents a generic failure prognosis informed decision making tool for wind farm operation and maintenance while considering the predictive failure information of individual turbine and its uncertainty. In the presented approach, the probabilistic damage growth model is used to characterize individual wind turbine performance degradation and failure prognostics, whereas the economic loss measured by monetary values and environmental performance measured by unified carbon credits are considered in the decision making process. Based on the customized wind farm information inputs, the developed decision making methodology can be used to identify optimum and robust strategies for wind farm operation and maintenance in order to maximize the economic and environmental benefits concurrently. The efficacy of proposed prognosis informed maintenance strategy is compared with the condition based maintenance strategy and demonstrated with the case study.

scholarly journals An improved opportunistic group replacement maintenance strategy for wind turbines

2020 ◽  
Vol 8 (10) ◽  
pp. 3627-3637
Author(s):  
Zhiyong Ma ◽  
Dameng Wang ◽  
Shiming Wu ◽  
Wei Teng ◽  
Yibing Liu
Keyword(s):  
Wind Turbines ◽  
Maintenance Strategy

scholarly journals Maintenance Optimization of Offshore Wind Turbines Based on an Opportunistic Maintenance Strategy

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2650 ◽  
Author(s):  
Lubing Xie ◽  
Xiaoming Rui ◽  
Shuai Li ◽  
Xin Hu
Keyword(s):  
Wind Turbines ◽  
Preventive Maintenance ◽  
Weather Conditions ◽  
Offshore Wind ◽  
Maintenance Optimization ◽  
Maintenance Strategy ◽  
Distribution Method ◽  
Opportunistic Maintenance ◽  
Maintenance Costs ◽  
Offshore Wind Turbines

Owing to the late development of offshore wind power in China, operational data and maintenance experience are relatively scarce. Due to the harsh environmental conditions, a reliability analysis based on limited sample fault data has been regarded as an effective way to investigate maintenance optimization for offshore wind farms. The chief aim of the present work is to develop an effective strategy to reduce the maintenance costs of offshore wind turbines in consideration of their accessibility. The three-parameter Weibull distribution method was applied to failure rate estimation based on limited data. Moreover, considering the impacts of weather conditions on the marine maintenance activities, the Markov method and dynamic time window were used to depict the weather conditions. The opportunistic maintenance strategy was introduced to cut down on the maintenance costs through optimization of the preventive maintenance age and opportunistic maintenance age. The simulation analysis we have performed showed that the maintenance costs of the opportunistic maintenance strategy were 10% lower than those of the preventive maintenance strategy, verifying the effectiveness of the proposed maintenance strategy.

An optimal integrated production and maintenance strategy for a multi-wind turbines system

2019 ◽  
Vol 58 (21) ◽  
pp. 6417-6440
Author(s):  
Zied Hajej ◽  
Rezg Nidhal ◽  
Chelbi Anis ◽  
Maryem Bouzoubaa
Keyword(s):  
Wind Turbines ◽  
Maintenance Strategy ◽  
Integrated Production

Optimal Redundancy and Maintenance Strategy Decisions for Offshore Wind Power Converters

2015 ◽  
Vol 22 (03) ◽  
pp. 1550015 ◽  
Author(s):  
Mahmood Shafiee ◽  
Michael Patriksson ◽  
Ann-Brith Strömberg ◽  
Lina Bertling Tjernberg
Keyword(s):  
Wind Power ◽  
Wind Turbines ◽  
Optimal Allocation ◽  
Power Converters ◽  
Power Converter ◽  
Offshore Wind ◽  
System Level ◽  
Offshore Wind Turbine ◽  
Maintenance Strategy ◽  
Offshore Wind Power

Analysis of field failure data collected from various wind farm databases indicates that the power converters are among the most critical components in offshore wind turbines, since they suffer from a high failure rate. One efficient approach to enhance the reliability and availability of the wind power systems is through using a redundant converter design, in which a set of power converters is placed together in parallel. The main advantage of a multiple parallel converter system is that the failure of one converter will not necessarily lead to the failure of the entire system. It may however increase the wind turbine's acquisition cost, volume, and weight. In this paper, we propose an approach of joint redundancy and maintenance strategy optimization for offshore wind power converters, aiming to simultaneously determine the "optimal allocation of redundant converters" and the "optimal threshold number of converters that are allowed to fail before sending a maintenance crew to the offshore platform". The optimal solution under various system-level constraints (such as reliability, weight, and the available space in nacelle) is derived and the conditions required to make using a redundant system beneficial are discussed. The proposed design is applied to an offshore wind turbine system and its performance is evaluated using a Monte-Carlo simulation technique. Finally, the results are compared with the conventional power converter system and a sensitivity analysis is conducted in order to make the proposed approach applicable for the next generation of wind turbines.

Prognosis Informed Stochastic Decision Making Framework for Operation and Maintenance of Wind Turbines

2012 ◽  
Author(s):  
Prasanna Tamilselvan ◽  
Yibin Wang ◽  
Pingfeng Wang ◽  
Janet M. Twomey
Keyword(s):  
Decision Making ◽  
Wind Turbines ◽  
High Performance ◽  
Wind Farm ◽  
Economic Loss ◽  
Environmental Benefits ◽  
Maintenance Strategy ◽  
System Failures ◽  
Operation And Maintenance ◽  
Failure Prognosis

Advances in high performance sensing and signal processing technology enable the development of failure prognosis tools for wind turbines to detect, diagnose, and predict the system-wide effects of failure events. Although prognostics can provide valuable information for proactive actions in preventing system failures, the benefits have not been fully utilized for the operation and maintenance decision making of wind turbines. This paper presents a generic failure prognosis informed decision making tool for wind farm operation and maintenance while considering the predictive failure information of individual turbine and its uncertainty. In the presented approach, the probabilistic damage growth model is used to characterize individual wind turbine performance degradation and failure prognostics, whereas the economic loss measured by monetary values and environmental performance measured by unified carbon credits are considered in the decision making process. Based on the customized wind farm information inputs, the developed decision making methodology can be used to identify optimum and robust strategies for wind farm operation and maintenance in order to maximize the economic and environmental benefits concurrently. The efficacy of proposed prognosis informed maintenance strategy is compared with the condition based maintenance strategy and demonstrated with the case study.

Sign in / Sign up

Export Citation Format

Share Document