Operational strategies for offshore wind turbines to mitigate failure rate uncertainty on operational costs and revenue

Several operational strategies for offshore wind farms have been established and explored in order to improve understanding of operational costs with a focus on heavy lift vessel strategies. Additionally, an investigation into the uncertainty surrounding failure behaviour has been performed identifying the robustness of different strategies. Four operational strategies were considered: fix on fail, batch repair, annual charter and purchase. A range of failure rates have been explored identifying the key cost drivers and under which circumstances an operator would choose to adopt them. When failures are low, the fix on fail and batch strategies perform best and allow flexibility of operating strategy. When failures are high, purchase becomes optimal and is least sensitive to increasing failure rate. Late life failure distributions based on mechanical and electrical components behaviour have been explored. Increased operating costs because of wear-out failures have been quantified. An increase in minor failures principally increase lost revenue costs and can be mitigated by deploying increased maintenance resources. An increase in larger failures primarily increases vessel and repair costs. Adopting a purchase strategy can negate the vessel cost increase; however, significant cost increases are still observed. Maintenance actions requiring the use of heavy lift vessels, currently drive train components and blades are identified as critical for proactive maintenance to minimise overall maintenance costs

Maintenance Optimization and Inspection Planning of Wind Energy Assets: Models, Methods and Strategies

Designing cost-effective inspection and maintenance programmes for wind energy farms is a complex task involving a high degree of uncertainty due to diversity of assets and their corresponding damage mechanisms and failure modes, weather-dependent transport conditions, unpredictable spare parts demand, insufficient space or poor accessibility for maintenance and repair, limited availability of resources in terms of equipment and skilled manpower, etc. In recent years, maintenance optimization has attracted the attention of many researchers and practitioners from various sectors of the wind energy industry, including manufacturers, component suppliers, maintenance contractors and others. In this paper, we propose a conceptual classification framework for the available literature on maintenance policy optimization and inspection planning of wind energy systems and structures (turbines, foundations, power cables and electrical substations). The developed framework addresses a wide range of theoretical and practical issues, including the models, methods, and the strategies employed to optimise maintenance decisions and inspection procedures in wind farms. The literature published to date on the subject of this article is critically reviewed and several research gaps are identified. Moreover, the available studies are systematically classified using different criteria and some research directions of potential interest to operational researchers are highlighted

Long-term research challenges in wind energy – a research agenda by the European Academy of Wind Energy

The European Academy of Wind Energy (eawe), representing universities and institutes with a significant wind energy programme in 14 countries, has discussed the long-term research challenges in wind energy. In contrast to research agendas addressing short- to medium-term research activities, this eawe document takes a longer-term perspective, addressing the scientific knowledge base that is required to develop wind energy beyond the applications of today and tomorrow. In other words, this long-term research agenda is driven by problems and curiosity, addressing basic research and fundamental knowledge in 11 research areas, ranging from physics and design to environmental and societal aspects. Because of the very nature of this initiative, this document does not intend to be permanent or complete. It shows the vision of the experts of the eawe, but other views may be possible. We sincerely hope that it will spur an even more intensive discussion worldwide within the wind energy community

Data-driven online monitoring of wind turbines

Condition based maintenance is a modern approach to maintenance which has been successfully used in several industrial sectors. In this paper we present a concrete statistical approach to condition based maintenance for wind turbine by applying ideas from statistical process control. A specific problem in wind turbine maintenance is that failures of a certain part may have causes that originate in other parts a long time ago. This calls for methods that can produce timely warnings by combining sensor data from different sources. Our method improves on existing methods used in wind turbine maintenance by using adaptive alarm thresholds for the monitored parameters that correct for values of other relevant parameters. We illustrate our method with a case study that shows that our method is able to predict upcoming failures much earlier than currently used methods