Abstract
Operation and maintenance constitute a substantial share of the lifecycle expenditures of an offshore renewable energy farm. A noteworthy number of methods and techniques have been developed to provide decision-making support in strategic planning and asset management. Condition monitoring instrumentation is commonly used, especially in offshore wind farms, due to the benefits it provides in terms of fault identification and performance evaluation and improvement. Incorporating technology advancements, a shift towards automation and digitalisation is taking place in the offshore maintenance sector. This paper reviews the existing literature and novel approaches in the operation and maintenance planning and the condition monitoring of offshore renewable energy farms, with an emphasis on the offshore wind sector, discussing their benefits and limitations. The state-of-the-art in industrial condition-based maintenance is reviewed, together with deterioration models and fault diagnosis and prognosis techniques. Future scenarios in robotics, artificial intelligence and data processing are investigated. The application challenges of these strategies and Industry 4.0 concepts in the offshore renewables sector are scrutinised, together with the potential implications of early-stage project integration. The identified technologies are ranked against a series of indicators, providing a reference for a range of industry stakeholders.
Highlights
The offshore wind sector has grown significantly over recent years, reaching a total of 29.1 GW [1]
In order to exploit stronger and more stable winds and to overcome the limitations related to the use of seabed-fixed foundations, there is a growing interest in floating offshore wind [3]
The main contribution of this paper is to enable the integration of state-of-the-art Operation and Maintenance (O&M) methods at an early stage of offshore wind installations, utilising industrial condition-based maintenance deterioration models, fault diagnosis/prognosis techniques, as well as recent advances in robotics, artificial intelligence and data processing
Summary
The offshore wind sector has grown significantly over recent years, reaching a total of 29.1 GW (over 10% of global wind installations) [1]. Production losses due to downtime over 20 years of operation have been estimated at around 12 m€/year for a 500 MW offshore wind farm situated 50 km offshore in the North Sea [5]. A suitable trade-off that allows us to keep the farm availability high while maintenance costs remain low is required [6]. To achieve this goal, a decision maker, such as an ORE farm operator, can generally act on two main aspects of the farm management: (i) the reliability and maintainability of the devices, e.g., number of interventions and redundancy measures, or (ii) the supportability, e.g., choice of the most suited vessels or helicopters and allocation of related routing and technicians
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