A Review of Predictive and Prescriptive Offshore Wind Farm Operation and Maintenance

Abstract

Offshore wind farms are a rapidly developing source of clean, low-carbon energy and as they continue to grow in scale and capacity, so does the requirement for their efficient and optimised operation and maintenance. Historically, approaches to maintenance have been purely reactive. However, there is a movement in offshore wind, and wider industry in general, towards more proactive, condition-based maintenance approaches which rely on operational data-driven decision making. This paper reviews the current efforts in proactive maintenance strategies, both predictive and prescriptive, of which the latter is an evolution of the former. Both use operational data to determine whether a turbine component will fail in order to provide sufficient warning to carry out necessary maintenance. Prescriptive strategies also provide optimised maintenance actions, incorporating predictions into a wider maintenance plan to address predicted failure modes. Beginning with a summary of common techniques used across both strategies, this review moves on to discuss their respective applications in offshore wind operation and maintenance. This review concludes with suggested areas for future work, underlining the need for models which can be simply incorporated by site operators and integrate live data whilst handling uncertainties. A need for further focus on medium-term planning strategies is also highlighted along with consideration of the question of how to quantify the impact of a proactive maintenance strategy.

Maritime logistics optimisation for predictive maintenance at offshore wind farms

“…A condition-based maintenance strategy is characterised by components being continuously monitored and that maintenance actions are executed according to the condition. The predictive maintenance strategy is then, according to [4], described as the aim to predict failures before occurrence and that real-time data and predictive analytics are used, leading to a prescriptive maintenance strategy where recommended actions are provided based on the predictions.…”

“…There exist several review papers on maintenance optimisation [11][12][13] [1], and one was found on predictive maintenance [4] at offshore wind farms. An early review of decision support models for offshore wind farms is provided in [14].…”

Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems

“…For example, the increasing maturity of machine learning approaches enables applications to improve the accuracy of demand, generation, and price forecasting [40]. Various other solutions have been proposed, such as intelligent energy management [42], demand response [43], operational optimization [44] and design optimization [45], fault prediction [46] as well as preventive and predictive maintenance for energy efficiency [47] and to extend the lifespan of machinery [48]. In the area of battery management, IoT and data science technologies enable numerous solutions to optimize battery manufacturing, operation and re-utilization [49].…”

Prescriptive maintenance for complex products with digital twin considering production planning and resource constraints

“…This is a maintenance technique specifically designed to reduce operational costs and asset risk, which aims at collecting and analyzing data concerning the condition of assets to prescribe relevant maintenance actions. To date, researchers have offered some valuable proposals from the management of heterogeneous multimodal asset data [3,4] and maintenance decisions making [5], covering areas such as wind farms [6], aviation industry [7], and residential buildings [8]. Choubey et al [9] presented a holistic end-to-end RxM framework, which integrates maintenance needs analysis, operational data, personnel scheduling, feedback information, and knowledge management.…”