Operation Of Offshore Wind Farms Research Articles

Offshore wind farm operation contributed to a slight improvement in seawater quality along the Jiangsu Coast, China

The rapid growth of offshore wind farms (OWFs) is driven by concerns for energy security and climate change mitigation. However, their impact on marine environments remains poorly understood due to limited research. This study analyzes the effects of an OWF along China's Jiangsu Coast on seawater quality using data from different development phases. Results show the major pollutants were different across phases. Heavy metal pollution reached alert levels during construction compared to the safe levels observed in the pre-construction and operational phases, mainly due to increases in Pb, Cd, and Hg concentrations. Eutrophication was mild throughout all periods but exhibited a continuous decrease, primarily attributed to reductions in PH and COD concentrations. As a result, the comprehensive pollution level during construction was increased, but it was improved to a clean level during the operational phase. Besides, significant variations were observed in the spatial distribution patterns of major pollutant indices across different scenarios. These changes may stem from a combination effect of land-based pollution, aquaculture, OWF-induced disturbances to atmosphere and hydrodynamics, OWF-related drain and leakage contamination, and marine management policies. Understanding these effects informs OWF optimization, rational wind resource utilization, and marine ecology protection.

Research on the Short-Term Prediction of Offshore Wind Power Based on Unit Classification

The traditional power prediction methods cannot fully take into account the differences and similarities between units. In the face of the complex and changeable sea climate, the strong coupling effect of atmospheric circulation, ocean current movement, and wave fluctuation, the characteristics of wind processes under different incoming currents and different weather are very different, and the spatio-temporal correlation law of offshore wind processes is highly complex, which leads to traditional power prediction not being able to accurately predict the short-term power of offshore wind farms. Therefore, aiming at the characteristics and complexity of offshore wind power, this paper proposes an innovative short-term power prediction method for offshore wind farms based on a Gaussian mixture model (GMM). This method considers the correlation between units according to the characteristics of the measured data of units, and it divides units with high correlation into a category. The Bayesian information criterion (BIC) and contour coefficient method (SC) were used to obtain the optimal number of groups. The average intra-group correlation coefficient (AICC) was used to evaluate the reliability of measurements for the same quantized feature to select the representative units for each classification. Practical examples show that the short-term power prediction accuracy of the model after unit classification is 2.12% and 1.1% higher than that without group processing, and the mean square error and average absolute error of the short-term power prediction accuracy are reduced, respectively, which provides a basis for the optimization of prediction accuracy and economic operation of offshore wind farms.

A new method for quantifying redistribution of seabirds within operational offshore wind farms finds no evidence of within-wind farm displacement

The climate crisis is driving a rapid increase in size and number of offshore wind farms to reduce carbon emissions from electricity generation. However, there are concerns about the potential impact of offshore wind farms on the marine environment. Seabirds are considered to be at risk of being displaced from preferred foraging habitat, by construction and operation of offshore wind farms, resulting in reduced energy intake or elevated energetic costs and consequent decreases in survival and/or productivity. Typically, displacement or avoidance behaviour is assessed by comparing abundance and spatial distributions of seabirds before and after an offshore wind farm is constructed. However, seabird distributions are highly variable through time and space and so discerning a change in distribution caused by an offshore wind farm from other environmental variables can be challenging. We present a new method that controls for temporal variation by examining the location of individual seabirds relative to turbines. Mean seabird density at different distances from individual turbines (0-400m) was calculated from data collected on a total of 12 digital aerial surveys of the Beatrice Offshore Wind Farm (UK), in May-August in 2019 and 2021. Mean densities of common guillemot (Uria aalge), razorbill (Alca torda), Atlantic puffin (Fratercula arctica) and black-legged kittiwake (Rissa tridactyla), both flying and sat on the water, were calculated. If the presence of turbines had no effect on seabird distribution, there should be no relationship between distance from turbine and seabird density. This was tested by simulating a relocation of turbines, relative to seabird distribution, and recalculating seabird density over 0-400m from simulated turbine locations. This was repeated to generate a bootstrapped distribution of expected densities against which observed density was compared. If displacement was occurring, mean observed density close to turbines would be significantly lower than expected density, derived from the bootstrap distribution. Overall, observed mean density did not differ significantly from expected density, i.e. no displacement effect was detected. There was a slight tendency for guillemot and razorbill, when sat on the water, to be at higher densities than expected, near turbines, suggestive of possible attraction to turbines, and for flying birds to be at lower densities than expected, near turbines, suggestive of possible avoidance. No flying razorbills were recorded within 100m of turbines but sample sizes were small. Kittiwake tended to show no avoidance or attraction behaviour, although flying kittiwake density was slightly lower than expected at 200m from turbines. Puffins sat on the water were recorded in densities similar to the expected density. Overall, no effect of turbine rotor speed was found, i.e. birds were not more likely to be displaced/avoid turbines at higher or lower rotor speeds. The results of the turbine relocation analysis gave a more consistent and more easily interpreted assessment of displacement/avoidance behaviour than the typical approaches of comparing abundance and seabird distribution through time. We strongly encourage application of this new approach to post-construction spatial distribution data from other offshore wind farms, to build the evidence base on the effects of offshore wind farms on seabirds.

Optimising O&M scheduling in offshore wind farms considering weather forecast uncertainty and wake losses

Offshore wind farms, while strategically positioned to maximise wind energy capture, are exposed to harsh environments, impacting their reliability and accessibility. Low reliability and accessibility in turn translate into high Operation and Maintenance (O&M) costs, which represent approximately 30% of the total lifetime costs of the project. One approach to reduce O&M costs is to improve maintenance planning decisions. The present paper presents a novel O&M scheduling methodology for offshore wind farm operations based on wind and wave forecasts. The methodology forecasts farm yield considering farm wake effects to identify optimal operation schedules, vessel routing, and turbine maintenance sequences that minimise costs and revenue losses due to downtime. Weather forecast uncertainty is modelled and integrated into the decision-making process to minimise weather risks. A case study inspired by the WindFloat Atlantic floating wind project was used to demonstrate the optimisation potential. Results suggest that adopting the proposed methodology over business-as-usual scheduling significantly boosts offshore wind farm operational profits during the forecasted period, with increases ranging from 2% to 24%, depending on the scenario considered. It was found that integrating the wind farm wake losses into the scheduling decisions can improve the optimal scheduling solution and further minimise total operation costs.

Uncertainty Prediction of Offshore Wind Power Based on Outlier Processing, Data Noise Reduction and the Deep Learning Method

Background: Accurate prediction of offshore wind power is the basis for promoting the safe and economic operation of offshore wind farms. Objective: This paper proposes an uncertainty prediction learning model based on outlier processing, synchronous wavelet denoising and attention mechanism optimization to achieve accurate prediction of offshore wind power. Method: Firstly, the isolated forest is adopted to filter the outliers of offshore wind power data and delete the error data caused by equipment or humans. Secondly, a syn-chrosqueezing wavelet neural network (SWT) is applied to denoise historical wind power data, improve data quality, and lay a foundation for accurate prediction. Next, the offshore wind power prediction method based on IP-SO-LSTM-Attention is constructed to realize offshore wind power prediction, in which the attention mechanism is applied to focus on the influence of important features on the output of offshore wind power, and the improved particle swarm optimization algorithm is adopted to find the best network structure of LSTM-Attention to optimize the prediction effect. After predicting the point prediction results based on the SWT-IPSO-LSTM-Attention model, this paper sets MAPE, RMSE, MAE and other indicators to evaluate the prediction effect. Results: The prediction error MAPE of the proposed model is 4.12%. It is 63.21% higher than the benchmark model (SWT-BP) and 56.35% higher than the benchmark model (SWT-LSTM). Conclusion: Based on the point prediction results, this paper uses KDE (Gaussian) prediction results to calculate the out-put curve under different confidence levels and provides decisionmaking reference information for accurate decision-making.

Developing cetacean-friendly guidelines, from whale watching to offshore wind farm operation

Developing offshore wind farms may impact cetaceans due to vessel collisions and underwater noise. Therefore, it is critical to protect cetaceans while developing offshore wind farms. We first studied the code of conduct/guidelines for whale watching and then interviewed members of the local whale-watching industry to learn about the interaction between cetaceans and vessels. After that, we compared current whale-watching guidelines in Taiwan with 69 published guidelines from other countries and locations, then developed guidelines for the offshore wind farm industry. The results show that rules related to approaching and interacting with cetaceans in Taiwan are similar to those in other countries. However, swimming with cetaceans and approaching calves are prohibited in Taiwan. From the survey of the whale-watching industry, most whale-watching guidelines in Taiwan were found to be feasible, and the guidelines should be described in the premise with different phenotypic traits of various cetaceans. Based on the whale-watching guidelines, we developed a code of conduct for protecting cetaceans from the impact of vessels, specifically in offshore wind farm operations in Taiwan.

An Integrated Scheme for Forecasting and Controlling Ramps in Offshore Wind Farms Considering Wind Power Uncertainties during Extreme Storms

Global warming-induced extreme tropical storms disrupt the operation of offshore wind farms, causing wind power ramp events and threatening the safety of the interconnected onshore grid. In order to attenuate the impact of these ramps, this paper proposes an integrated strategy for forecasting and controlling ramps in offshore wind farms. First, the characteristics of wind power ramps during tropical storms are studied, and a general ramp control framework is established. Second, a wind power ramp prediction scheme is designed based on a minimal gated memory network (MGMN). Third, by taking into account the wind power ramp prediction results and wind power uncertainties, a chance-constraint programming-based optimal ramp control scheme is developed to simultaneously maximize wind power absorption and minimize ramp control costs. Finally, we use real-world offshore wind farm data to validate the effectiveness of the proposed strategy.

Modeling offshore wind farm disturbances and maintenance service responses within the scope of resilience

Offshore wind farms are becoming ever more important means of energy production. Accordingly, they are started to be considered critical infrastructures with heightened attention on their protection and resilience. This paper studies how the maintenance service can sustain or recover offshore wind farm operations under different stressors. We conduct this study by modeling the failures in an offshore wind farm and how maintenance service is able to correct them. Our model enhances the traditional cause-consequence trees by including dynamical aspects, and the modeling of the maintenance process. Special attention in the maintenance model is given to limited personnel and material resources, as well as limited access to wind turbines. This limit is a result of occasional harsh weather conditions that make conducting repairs unsafe. The model is applied for two representative disturbance scenarios: a cyber-attack leading to high failure rates and a high-impact incident causing numerous simultaneous failures. The second scenario integrates results from a physical power system simulation that are used for depicting the incident. The results show that our model can present scenarios where different stressors challenge the operations. These can be used for testing and defining requirements for future countermeasures to improve the resilience.

Towards fully autonomous floating offshore wind farm operation & maintenance

Wind energy is one of the most versatile and promising sustainable energy solutions. Wind energy can be harvested in both onshore and offshore environments. Due to the environmental and societal impacts of onshore wind farms, the focus on developing offshore wind farms has been steadily increasing. For deep-water areas, floating wind turbine solutions have been developed in the past two decades. Each wind turbine is mounted on a floating structure and connected to a mooring system. While the floating wind turbine technology enables electricity generation in water depths where fixed-foundation turbines are not feasible, operation and maintenance (O&M) have become serious issues. This paper investigates the challenges and potential enabling technologies for the development of autonomous floating offshore wind farms. In particular, the paper explores the potential utilization of information and communication technology (ICT) and robotics toward fully autonomous floating offshore wind farm O&M, aiming for cost reduction and improved operational safety. The presented solutions cover fundamental aspects in floating platform design, remote operation in the form of digital twins, autonomous underwater robots and surface vehicles, and eco-friendly energy storage.

Hybrid Particle Swarm and Gravitational Search Algorithm-Based Optimal Fractional Order PID Control Scheme for Performance Enhancement of Offshore Wind Farms

This article aimed to introduce a novel application of a hybrid particle swarm optimizer and gravitational search algorithm (HPSOGSA) that can be used for optimal control of offshore wind farms’ voltage source converter connected to HVDC transmission lines. Specifically, the algorithm was used to design fractional-order proportional-integral-derivative (FOPID) controller parameters designed to minimize the system’s objective function based on an integral squared error. The proposed FOPID controller was applied to improve offshore wind farm performance under different transient conditions, and its results were compared with a PI controller that was designed using a genetic algorithm and grey wolf optimization algorithm. The fault ride-through capabilities of the proposed control strategy were also evaluated. The findings suggest that the HPSOGSA-based FOPID controller outperformed the other two methods, significantly enhancing offshore wind farm operations. The control strategy was thoroughly tested using MATLAB/Simulink under various operating scenarios.

Characterization of static voltage stability region of offshore wind farms considering submarine cable capacitance parameters and converter saturation

Accurately characterizing the static voltage stability region (SVSR) is the key to ensure the safe and stable operation of offshore wind farms (OWFs). However, the traditional SVSR evaluation methods ignore the influence of capacitance parameters, and the stability criteria fail after d-axis current saturation, leading to a large deviation in the evaluation results. Therefore, this paper proposes an improved method to characterize the SVSR of OWFs, which takes the cable capacitance parameters and the saturation effect of the converter into account. Firstly, based on the equivalent circuit method, the power common connection (PCC) voltage charact- erization model considering cable capacitance parameters was derived. Then, the DC voltage stability was defined to describe the static voltage instability caused by the current limiting of the converter. The SVSR constraints are summarized into three categories: power flow stability, DC voltage stability and dq-axis stability, and stability criteria are designed according to different stability. Based on the method of intersection of multiple stability regions and Z-plane projection, the SVSR is characterized. Finally, the influence of different parameter settings on the SVSR is analyzed, and the accuracy of the proposed method is verified by RT-Lab experimental platform.

Offshore wind farming in Taiwan

Offshore wind farming in Taiwan int his question and answer with Open Access Government, Dr Song GwoShyh from MMA Global Aqua Co. Ltd. discusses his company's role in offshore wind farming in Taiwan and their ambitions for the future. In response to Taiwan's special environment, for underwater foundation design, geological survey, marine exploration and future official operation, the monitoring and maintenance of the operation of offshore wind farms and wind turbines are worthy of Taiwan's sustained investment and development of soft power, but also can be implemented in Taiwan's offshore wind power localisation field.

Reliability of Communication Systems Used in Offshore Wind Farms

In the era of renewable energy, offshore wind farms play a very important role. The number of such installations in Europe is increasing rapidly. With the growing capacity of wind turbines installed in these farms (3, 5, 10 MW), the profitability of this type of energy systems plays an increasing role. The number of wind energy turbines installed at offshore wind farms is growing constantly as well. Once installed, the power plants must be under constant technical supervision, with reliability of electronic communication systems being a particularly important aspect in the operation of offshore wind farms. Considerations focusing on this subject form the very core of this paper. After an introduction to offshore wind farms, the following aspects will be discussed: redundant topologies, e.g. multiple HiPERRings, redundant switches and routers within the backbone networks, redundancy of the transmission media used, alternative transmission technologies, e.g. WLANs (IEEE 802.11h, IEEE 802.11g). Finally, requirements applicable to reliable electronic communication systems used in offshore wind farms will be formulated.

Joint optimization of preventive and condition-based maintenance for offshore wind farms

High costs of maintenance and lost production during downtime are a challenge to the offshore wind industry, and there is a great potential to improve cost efficiency by improved maintenance and control strategies utilizing condition monitoring information. As wind farms get older, there is also an increased need to find ways of extending the lifetime of wind turbines allowing continued operation. This may be obtained by de-rating strategies, meaning adjustments of the power production to reduce the fatigue loads on the turbines. This subsequently means wind farm operators are faced with a trade-off between maximizing power production while limiting the degradation of the turbines. To investigate the best trade-off, this paper presents an optimization framework that considers component condition and planned power production to find the best times to perform predetermined preventive and condition-based maintenance on an offshore wind farm. To solve the scheduling problem, it is formulated as a constrained integer linear program, maximizing the net income for the planning horizon. The proposed method considers logistic restrictions, wind and electricity price forecasts, control strategies, component condition and probability of failure. Moreover, the method uses a short time horizon (days) to utilise weather forecasts and a long time horizon (weeks) to better capture the impact of deteriorating condition. The model is presented in a general framework for accounting for component condition in offshore wind farm operation and maintenance. It is illustrated for a specific potential application, considering condition monitoring of main bearings and corrosion of structural elements as examples.

Applications of robotics in floating offshore wind farm operations and maintenance: Literature review and trends

AbstractMarine operations required to transfer technicians and equipment represent a significant proportion of the total cost of offshore wind. The profile of sites being considered for floating offshore wind farms (FOWFs), e.g., further from the shore and in harsher environments, indicates that these costs need to be assessed by taking into account the maintenance requirements and restricted weather windows. There is an immediate need to investigate the potential use of robotic systems in the wind farm's operations and maintenance (O&M) activities, to reduce the need for costly manned visits. The use of robotic systems can be critical, not only to replace repetitive activities and bring down the levelised cost of energy but also to reduce the health and safety risks by supporting human operators in performing the desired inspections. This paper provides a review of the state of the art in the applications of robotics for O&M of FOWFs. Emerging technology trends and associated challenges and opportunities are highlighted, followed by an outline of the agenda for future research in this domain.

Analysis of Life Extension Performance Metrics for Optimal Management of Offshore Wind Assets

Abstract The main objective of this study is to develop an optimal life extension management strategy for ageing offshore wind farms. Finding the appropriate performance metric for an operation is essential for a durable, reliable, and profitable offshore wind farm operation. To this end, the key metrics to evaluate the life extension performance of an offshore wind farm are investigated. The mean value and the standard deviation of each performance metric are calculated using a probabilistic techno-economic assessment framework for a single offshore wind asset, which is later extended to evaluate the whole offshore wind farm by the multi-asset portfolio optimization. In this regard, the Markowitz modern portfolio theory is applied to estimate a risk-adjusted return parameter, the Sharpe ratio of the overall portfolio of offshore wind assets. Later on, the key performance metrics are compared to identify the most suitable metrics at different stages of life extension, and a further discussion is given for different offshore wind farm sizes. Moreover, the optimal management strategy, which maximizes the Sharpe ratio of the overall offshore wind farm, is analyzed using one of the key performance metrics under optimistic, moderate, and pessimistic scenarios. Finally, the optimal allocation (portfolio) of offshore wind assets attained based on the mean-variance optimization is presented for the different stages of the life extension of the offshore wind farms accounting for the uncertainty propagation during the life extension.

Use of drones in offshore wind farm inspections

Abstract: With the increase in global investments in offshore wind energy and the rapid implementation of wind technologies in hazardous deep water environments, operational inspection of wind turbines and related infrastructure plays an important role in the safe and efficient operation of offshore wind farms. In recent years, much attention has been paid to the use of unmanned aerial vehicles (UAVs) and remotely piloted unmanned aerial vehicles (RPAs) commonly referred to as "drones" for remote inspection of renewable energy infrastructure, i.e. photovoltaic farms and onshore wind farms. Drones have significant potential also in in offshore wind energy. Inspection with drones allows for to reduce not only the number of flight operations (involvement of aircraft and flight crews) and the transport of personnel carrying out the maintenance and repair of offshore wind turbines. With drones is possible carry the equipment transported for hazardous inspection work. The involvement of UAVs also reduces the plant downtime needed to detect faults and collect diagnostic information from the entire wind farm. The benefits of inspection technology in the offshore wind energy industry based on drones are confirmed by the previous tests, and the prospect of offshore energy development encourages further work with the use of UAVs. At the same time, it should be borne in mind that any unexpected failure of the drone system during its mission may interrupt control works (during inspections), and thus reduce the electricity generated by wind turbines. The article presents the potential of drones in the process of inspecting wind farms, including offshore wind farms, presents examples of UAV models used for inspections, indicates methods of conducting inspections with the use of drones and highlights a significant reduction in the costs of the operation of offshore wind farms as a result of limiting the use of manned aviation (helicopters and flight crews) and the elimination of the risk associated with the involvement of personnel to perform inspections of wind farms at sea. The potential of unmanned floating platforms as part of cooperation with UAV in the process of inspecting offshore wind farms was also indicated. Keywords: wind energy, offshore, drones, wind farms, drone inspections, maritime inspections, marine aviation, offshore, offshore wind energy, safety air operations, risk analysis

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.

Research on Operation Safety of Offshore Wind Farms

The operation of offshore wind farms is characterized by a complicated operational environment, long project cycle, and complex vessel traffic, which lead to safety hazards. To identify the key factors affecting the operational safety of offshore wind farms, the risk characteristics of offshore wind farm operations are analyzed based on comprehensive identification of hazards and risk assessment theory. A systematic fault tree analysis of the offshore wind farm operation is established. The assessment shows that the key risk factors that induce offshore wind power collapse, corrosion, fire, lightning strikes, blade failure, personal injury, ship collision, and submarine cable damage accidents are gale, untimely overhauling, improper fire stopping methods, high average number of thunderstorm days, the loose internal structure of fan, working at height, collision avoidance failure, and insufficient buried depth of cables.

Uncertain accessibility estimation method for offshore wind farm based on multi‐step probabilistic wave forecasting

Accessibility estimation is significant to the offshore wind farm operation and maintenance (O&M) due to the extremely limited weather window and its sensitive effects on O&M tasks. Wave forecasting can be one solution to help maintenance decision-making. However, the uncertain and dynamic properties of wave forecasts are seldom considered in the accessibility estimation process. This paper presents an uncertain accessibility estimation method based on a multi-step probabilistic wave height forecasting (MPWHF) model and Monte Carlo simulation. First, an MPWHF model is proposed using the wavelet decomposition and the sequence to sequence (Seq2Seq) network with quantile outputs. Second, the O&M missions are randomly given a start time and simulated in the O&M flow chart by the Monte Carlo method. Finally, several access indexes, including accessibility probability, delay time, and delay probability, are evaluated based on the simulation results. Verification of the proposed MPWHF model and uncertain accessibility estimation is based on 7-year observation data of a buoy station. The results show that the MPWHF model outperforms other counterparts and the probability of offshore accessibility is nonlinearly dependent on the weather limits and the O&M required time.