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

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.

What’s in My Toolkit? A Review of Technologies for Assessing Changes in Habitats Caused by Marine Energy Development

Marine energy devices are installed in highly dynamic environments and have the potential to affect the benthic and pelagic habitats around them. Regulatory bodies often require baseline characterization and/or post-installation monitoring to determine whether changes in these habitats are being observed. However, a great diversity of technologies is available for surveying and sampling marine habitats, and selecting the most suitable instrument to identify and measure changes in habitats at marine energy sites can become a daunting task. We conducted a thorough review of journal articles, survey reports, and grey literature to extract information about the technologies used, the data collection and processing methods, and the performance and effectiveness of these instruments. We examined documents related to marine energy development, offshore wind farms, oil and gas offshore sites, and other marine industries around the world over the last 20 years. A total of 120 different technologies were identified across six main habitat categories: seafloor, sediment, infauna, epifauna, pelagic, and biofouling. The technologies were organized into 12 broad technology classes: acoustic, corer, dredge, grab, hook and line, net and trawl, plate, remote sensing, scrape samples, trap, visual, and others. Visual was the most common and the most diverse technology class, with applications across all six habitat categories. Technologies and sampling methods that are designed for working efficiently in energetic environments have greater success at marine energy sites. In addition, sampling designs and statistical analyses should be carefully thought through to identify differences in faunal assemblages and spatiotemporal changes in habitats.

Composing Vessel Fleets for Maintenance at Offshore Wind Farms by Solving a Dual-Level Stochastic Programming Problem Using GRASP

Background: Dual-level stochastic programming is a technique that allows modelling uncertainty at two different levels, even when the time granularity differs vastly between the levels. In this paper we study the problem of determining the optimal fleet size and mix of vessels performing maintenance operations at offshore wind farms. In this problem the strategic planning spans decades, while operational planning is performed on a day-to-day basis. Since the operational planning level must somehow be taken into account when making strategic plans, and since uncertainty is present at both levels, dual-level stochastic programming is suitable. Methods: We present a heuristic solution method for the problem based on the greedy randomized adaptive search procedure (GRASP). To evaluate the operational costs of a given fleet, a novel fleet deployment heuristic (FDH) is embedded into the GRASP. Results: Computational experiments show that the FDH produces near optimal solutions to the operational day-to-day fleet deployment problem. Comparing the GRASP to exact methods, it produces near optimal solutions for small instances, while significantly improving the primal solutions for larger instances, where the exact methods do not converge. Conclusions: The proposed heuristic is suitable for solving realistic instances, and produces near optimal solution in less than 2 h.

Model for evaluation of technical and economic indicators of offshore wind farms

THE PURPOSE. An urgent problem in the development of offshore wind energy is the high cost of generating electricity, which is due to large capital investments. The solution to this problem is possible by increasing efficiency while reducing costs as much as possible, which requires optimal design of offshore wind farms.GOAL. Development of model for the technical and economic indicators of offshore wind farms based on configuration data, taking into account the factors of climatic conditions and the topography of the seabed at the site of the planned wind farm location.METHODS. Mathematical modeling using Matlab software environment.RESULTS. The model evaluates the impact of wake and electrical losses in the main components of the electrical system on the operation of an offshore wind farm, and also allows to take into account the influence of the seabed relief on the economic characteristics of wind turbine foundations. The model was tested on the example of calculating two existing offshore wind farms «Horns Rev 1» and «Horn Rev 2» by comparing the calculated indicators of the average annual electricity generation, capacity factor, capital expenditures and normalized cost of electricity with the actual indicators obtained during their operation. The comparison results show slight deviations within 5% of the actual values.CONCLUSION. The model for assessing the technical and economic indicators of offshore wind farms was developed and tested on the basis of data on the wind farm configuration and layout, as well as factors of climatic conditions and terrain. Evaluation of the computational speed showed a sufficiently high efficiency of the algorithm, which allows the model to be applied to optimize large offshore wind farms.

A Review of Rapid Distortion Theory

With the depleting non-renewable fuel sources like coal and an ever-increasing demand for energy, we need to start looking into renewable energy sources. These are of paramount importance for a sustainable and green future. Wind Energy is one of the most important sources of renewable energy. But, setting up a wind farm requires considerable land area and land acquisitions are often faced with legal hurdles. This necessitates setting up offshore wind turbines. But, when we talk about offshore wind farms, we need to address the age-old phenomenon: “Turbulence”. Presently, we are trying to develop enhanced controllers for wind farms which will increase the efficiency of the wind farms. The effects of rapidly changing wake aerodynamics i.e. breakdown of strong tip and hub vortices mixed up with low intensity turbulence in the inflow of the rotor and counter-rotation of the wake i.e. determinate velocity component in wake turbulence field will affect the overall performance of the wind farm. This paper provides a brief review on Rapid Distortion Theory (RDT) to model the turbulence.

Incorporating Public Participation in Offshore Wind Farm Siting in Greece

The public acceptance of Offshore Wind Farms (OWFs) is an important issue that is expected to depend highly on their site location. Public involvement in decision-making processes is recommended as it may contribute to the mitigation of opposing, delaying and even blocking OWF projects, as well as increasing future public confidence and support. The aim of this study is to identify the most suitable sites for OWFs deployment in Greece based on citizens’ preferences and judgments. The methodology consists of three phases: (i) identification of Eligible Marine Areas (EMAs) for OWF siting by deploying ten exclusion criteria, (ii) prioritization of six evaluation criteria and ranking of EMAs according to citizens’ judgments through an Online Questionnaire Survey (OQS) and (iii) overall prioritization of EMAs. The Analytic Hierarchy Process (AHP), supported by Geographic Information Systems (GIS) and the OQS are used for the analysis. The results illustrate the priority ranking of thirteen EMAs for OWFs deployment in the Greek marine environment under five different scenarios. The most suitable sites are located in the South-West zone offshore of Rhodes in all the examined scenarios. Sustainable development is a challenging social process, and the different preferences of the society should be integrated in planning processes.