Wind turbine icing characteristics and icing-induced power losses to utility-scale wind turbines.

A field campaign was carried out to investigate ice accretion features on large turbine blades (50 m in length) and to assess power output losses of utility-scale wind turbines induced by ice accretion. After a 30-h icing incident, a high-resolution digital camera carried by an unmanned aircraft system was used to capture photographs of iced turbine blades. Based on the obtained pictures of the frozen blades, the ice layer thickness accreted along the blades' leading edges was determined quantitatively. While ice was found to accumulate over whole blade spans, outboard blades had more ice structures, with ice layers reaching up to 0.3 m thick toward the blade tips. With the turbine operating data provided by the turbines' supervisory control and data acquisition systems, icing-induced power output losses were investigated systematically. Despite the high wind, frozen turbines were discovered to rotate substantially slower and even shut down from time to time, resulting in up to 80% of icing-induced turbine power losses during the icing event. The research presented here is a comprehensive field campaign to characterize ice accretion features on full-scaled turbine blades and systematically analyze detrimental impacts of ice accumulation on the power generation of utility-scale wind turbines. The research findings are very useful in bridging the gaps between fundamental icing physics research carried out in highly idealized laboratory settings and the realistic icing phenomena observed on utility-scale wind turbines operating in harsh natural icing conditions.

Spatial constraints in large-scale expansion of wind power plants.

When wind turbines are arranged in clusters, their performance is mutually affected, and their energy generation is reduced relative to what it would be if they were widely separated. Land-area power densities of small wind farms can exceed 10 W/m2, and wakes are several rotor diameters in length. In contrast, large-scale wind farms have an upper-limit power density in the order of 1 W/m2 and wakes that can extend several tens of kilometers. Here, we address two important questions: 1) How large can a wind farm be before its generation reaches energy replenishment limits and 2) How far apart must large wind farms be spaced to avoid inter-wind-farm interference? We characterize controls on these spatial and temporal scales by running a set of idealized atmospheric simulations using the Weather and Research Forecasting model. Power generation and wind speed within and over the wind farm show that a timescale inversely proportional to the Coriolis parameter governs such transition, and the corresponding length scale is obtained by multiplying the timescale by the geostrophic wind speed. A geostrophic wind of 8 m/s and a Coriolis parameter of 1.05 × 10-4 rad/s (latitude of ∼46°) would give a transitional scale of about 30 km. Wind farms smaller than this result in greater power densities and shorter wakes. Larger wind farms result instead in power densities that asymptotically reach their minimum and wakes that reach their maximum extent.

Ultra-Short-Term Offshore Wind Power Prediction Based on PCA-SSA-VMD and BiLSTM.

In order to realize the economic dispatch and safety stability of offshore wind farms, and to address the problems of strong randomness and strong time correlation in offshore wind power forecasting, this paper proposes a combined model of principal component analysis (PCA), sparrow algorithm (SSA), variational modal decomposition (VMD), and bidirectional long- and short-term memory neural network (BiLSTM). Firstly, the multivariate time series data were screened using the principal component analysis algorithm (PCA) to reduce the data dimensionality. Secondly, the variable modal decomposition (VMD) optimized by the SSA algorithm was applied to adaptively decompose the wind power time series data into a collection of different frequency components to eliminate the noise signals in the original data; on this basis, the hyperparameters of the BiLSTM model were optimized by integrating SSA algorithm, and the final power prediction value was obtained. Ultimately, the verification was conducted through simulation experiments; the results show that the model proposed in this paper effectively improves the prediction accuracy and verifies the effectiveness of the prediction model.

A New Short-Circuit Current Calculation and Fault Analysis Method Suitable for Doubly Fed Wind Farm Groups.

The transient characteristics of wind farms in groups are quite different; in addition, there is a strong coupling between the wind farms and the grid, and these factors make the fault analysis of the grid with wind farm groups complicated. In order to solve this problem, a mathematical model of the converter is established based on the input-output external characteristics of the converter, and a transient model of a doubly fed wind turbine (DFIG) is presented considering the influence of the low-voltage ride-through control (LVRT) of the converter, and the effect mechanism of the LVRT strategy on the short-circuit current is analyzed. Finally, a short-circuit current calculation model of a doubly fed wind turbine with low-voltage crossing control is established. The interaction mechanism between wind farms during the fault is analyzed, and a short-circuit current calculation method of doubly fed wind farm groups is proposed. RTDS is used to verify the accuracy of the proposed short-circuit current calculation method for doubly fed field groups. On this basis, a method of power grid fault analysis after doubly fed field group access is discussed and analyzed.

Small-Signal Stability Analysis and MOSMA-Based Optimization Control Strategy of OWF with MMC-HVDC Grid Connection.

The recent oscillation events in offshore wind farms (OWFs) connected via a modular multilevel-converter-based HVDC (MMC-HVDC) system are developing towards a wider frequency band, which causes complex a small-signal interaction phenomenon and difficulties in the stability analysis and control. In this paper, the wideband dynamic interaction mechanism is investigated based on the impedance analysis method and an improved control strategy using an optimization algorithm is proposed to improve the small-signal stability and reduce the oscillation risks. First, the detailed impedance models of the grid-connected system are established considering the distribution characteristics of the submarine cable, control delay and frequency coupling effect. Then, combined with the active damping control method, the wideband resonance mechanism is analyzed, and the stability constraints of controller parameters are obtained using the impedance stability criterion. Finally, an improved multi-objective slime mold algorithm (MOSMA)-based coordinated optimization control strategy is proposed to enhance the adaptability of the controller parameters and the wideband damping ability of a grid-connected system, which can improve the wideband stability of the system. The simulation and experimental results verify the proposed control strategy.

Decommissioning of offshore wind farms and its impact on benthic ecology.

At the end of their operational life time offshore wind farms need to be decommissioned. How and to what extent the removal of the underwater structures impairs the ecosystem that developed during the operational phase of the wind farm is not known. So, decision makers face a knowledge gap, making the consideration of such ecological impacts challenging when planning decommissioning. This study evaluates how complete or partial decommissioning of foundation structure and scour protection layer impacts local epibenthic macrofauna biodiversity. We assessed three decommissioning alternatives (one for complete and two for partial removal) regarding their impact on epibenthic macrofauna species richness. The results imply that leaving the scour protection layer in situ will preserve a considerable number of species while cutting of the foundation structure above seabed will be beneficial for the fauna of such foundation structures where no scour protection is installed. These results should be taken with a grain of salt, as the current data base is rather limited. Data need to be improved substantially to allow for reliable statements and sound advice regarding the ecological impact of offshore wind farm decommissioning.

Decision Support System for Prioritization of Offshore Wind Farm Site by Utilizing Picture Fuzzy Combined Compromise Solution Group Decision Method.

The selection of offshore wind farm site (OWFS) has important strategic significance for vigorously developing offshore new energy and is deemed as a complicated uncertain multicriteria decision-making (MCDM) process. To further promote offshore wind power energy planning and provide decision support, this paper proposes a hybrid picture fuzzy (PF) combined compromise solution (CoCoSo) technique for prioritization of OWFSs. To begin with, a fresh PF similarity measure is proffered to estimate the importance of experts. Next, the novel operational rules for PF numbers based upon the generalized Dombi norms are defined, and four novel generalized Dombi operators are propounded. Afterward, the PF preference selection index (PSI) method and PF stepwise weights assessment ratio analysis (SWARA) model are propounded to identify the objective and subjective weight of criteria, separately. In addition, the enhanced CoCoSo method is proffered via the similarity measure and new operators for ranking OWFSs with PF information. Lastly, the applicability and feasibility of the propounded PF-PSI-SWARA-CoCoSo method are adopted to ascertain the optimal OWFS. The comparison and sensibility investigations are also carried out to validate the robustness and superiority of our methodology. Results manifest that the developed methodology can offer powerful decision support for departments and managers to evaluate and choose the satisfying OWFSs.

An Improved Deep Reinforcement Learning Method for Dispatch Optimization Strategy of Modern Power Systems.

As a promising information theory, reinforcement learning has gained much attention. This paper researches a wind-storage cooperative decision-making strategy based on dueling double deep Q-network (D3QN). Firstly, a new wind-storage cooperative model is proposed. Besides wind farms, energy storage systems, and external power grids, demand response loads are also considered, including residential price response loads and thermostatically controlled loads (TCLs). Then, a novel wind-storage cooperative decision-making mechanism is proposed, which combines the direct control of TCLs with the indirect control of residential price response loads. In addition, a kind of deep reinforcement learning algorithm called D3QN is utilized to solve the wind-storage cooperative decision-making problem. Finally, the numerical results verify the effectiveness of D3QN for optimizing the decision-making strategy of a wind-storage cooperation system.

EEG power spectral responses to wind farm compared with road traffic noise during sleep: A laboratory study.

Wind turbine noise is dominated by low frequencies for which effects on sleep relative to more common environmental noise sources such as road traffic noise remain unknown. This study examined the effect of wind turbine noise compared with road traffic noise on sleep using quantitative electroencephalogram power spectral analysis. Twenty-three participants were exposed to 3-min samples of wind turbine noise and road traffic noise at three sound pressure levels (33, 38 and 43 dBA) in randomised order during established sleep. Acute (0-30 s) and more sustained (30-180 s) effects of noise presentations during N2 and N3 sleep were examined using spectral analysis of changes in electroencephalogram power frequency ranges across time in 5-s intervals. Both noise types produced time- and sound pressure level-dependent increases in electroencephalogram power, but with significant noise type by sound pressure level interactions in beta, alpha, theta and delta frequency bands (all p < 0.05). Wind turbine noise showed significantly lower delta, theta and beta activity immediately following noise onset compared with road traffic noise (all p < 0.05). However, alpha activity was higher for wind turbine noise played at lower sound pressure levels (33 dBA [p = 0.001] and 38 dBA [p = 0.003]) compared with traffic noise during N2 sleep. These findings support that spectral analyses show subtle effects of noise on sleep and that electroencephalogram changes following wind turbine noise and road traffic noise onset differ depending on sound pressure levels; however, these effects were mostly transient and had little impact on conventionally scored sleep. Further studies are needed to establish if electroencephalogram changes associated with modest environmental noise exposures have significant impacts on sleep quality and next-day functioning.

Modeling Epistemic Uncertainty in Offshore Wind Farm Production Capacity to Reduce Risk.

Financial stakeholders in offshore wind farm projects require predictions of energy production capacity to better manage the risk associated with investment decisions prior to construction. Predictions for early operating life are particularly important due to the dual effects of cash flow discounting and the anticipated performance growth due to experiential learning. We develop a general marked point process model for the times to failure and restoration events of farm subassemblies to capture key uncertainties affecting performance. Sources of epistemic uncertainty are identified in design and manufacturing effectiveness. The model then captures the temporal effects of epistemic and aleatory uncertainties across subassemblies to predict the farm availability-informed relative capacity (maximum generating capacity given the technical state of the equipment). This performance measure enables technical performance uncertainties to be linked to the cost of energy generation. The general modeling approach is contextualized and illustrated for a prospective offshore wind farm. The production capacity uncertainties can be decomposed to assess the contribution of epistemic uncertainty allowing the value of gathering information to reduce risk to be examined.

Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms.

In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean, renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind power has been developing in the direction of digitization and intelligence. It is of great significance to carry out research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit for the reduction of the operation and maintenance costs, the improvement of the power generation efficiency, improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms. This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of "offshore wind power engineering and biological and environment", the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored, especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of power equipment, and digital platforms.

Non-Destructive Techniques for the Condition and Structural Health Monitoring of Wind Turbines: A Literature Review of the Last 20 Years.

A complete surveillance strategy for wind turbines requires both the condition monitoring (CM) of their mechanical components and the structural health monitoring (SHM) of their load-bearing structural elements (foundations, tower, and blades). Therefore, it spans both the civil and mechanical engineering fields. Several traditional and advanced non-destructive techniques (NDTs) have been proposed for both areas of application throughout the last years. These include visual inspection (VI), acoustic emissions (AEs), ultrasonic testing (UT), infrared thermography (IRT), radiographic testing (RT), electromagnetic testing (ET), oil monitoring, and many other methods. These NDTs can be performed by human personnel, robots, or unmanned aerial vehicles (UAVs); they can also be applied both for isolated wind turbines or systematically for whole onshore or offshore wind farms. These non-destructive approaches have been extensively reviewed here; more than 300 scientific articles, technical reports, and other documents are included in this review, encompassing all the main aspects of these survey strategies. Particular attention was dedicated to the latest developments in the last two decades (2000-2021). Highly influential research works, which received major attention from the scientific community, are highlighted and commented upon. Furthermore, for each strategy, a selection of relevant applications is reported by way of example, including newer and less developed strategies as well.

Northern gannets (Morus bassanus) are strongly affected by operating offshore wind farms during the breeding season.

Northern gannets (Morus bassanus) have been ranked as one of the most vulnerable species in terms of collision with offshore wind farm (OWF) turbines, and strong avoidance of OWFs has been documented for this species. Gannets increasingly encounter OWFs within the ranges of their largest breeding colonies along the European coasts. However, information on their actual reactions to OWFs during the breeding season is lacking. We investigated the possible effects of OWFs located 23-35 km north of the colony on Helgoland in the southern North Sea on breeding gannets. GPS tags were applied to 28 adult gannets breeding on Helgoland for several weeks over 2 years. Most gannets (89%) predominantly avoided the OWFs in both years, but 11% frequently entered them when foraging or commuting between the colony and foraging areas. Flight heights inside the OWFs were close to the rotor-blade zone, especially for individuals predominantly avoiding the OWFs. Gannets preferred distances of 250-450 m to the turbines when being inside the OWF. A point process modelling approach revealed that the gannets resource selection of the OWF area compared with the surroundings (outside OWF = up to 15 km from the OWF border) was reduced by 21% in 2015 and 37% in 2016. This study provides the first detailed characterisation of individual reactions of gannets to OWFs during the breeding season and one of the first comprehensive studies of OWF effects on this species based on telemetry data. The documented effects need to be considered during the planning processes for future OWFs, especially those located close to large seabird breeding colonies.

The Influence of Climate Parameters on Maintenance of Wind Farms-A Galician Case Study.

There are different monitoring procedures in wind farms with two main objectives: (i) to improve energy production by the capability of the national electrical network and (ii) to reduce the stooped hours due to preventive and or corrective maintenance activities. In this sense, different sensors are employed to sample in real-time the working conditions of equipment, the electrical production and the weather conditions. Despite this, just the anemometer measurement can be related to the more important errors of interruption of power regulation and anemometer errors. Both errors are related to gusty winds and contribute to more than 33% of the cost of a wind farm. The present paper reports some mathematical relations between weather and maintenance but there are no extreme values of each variable that let us predict a near failure and its corresponding loss of working hours. To achieve this, statistical analysis identifies the relation between weather variables and errors and different models are obtained. What is more, due to the difficulty and economic implications involving the implementation of complex algorithms and techniques of artificial intelligence, it is still a challenge to optimize this process. Finally, the obtained results show a particular case study that can be extrapolated to other wind farms after different case studies to adjust the model to different weather regions, and serve as a useful tool for weather maintenance.

A study on offshore wind farm siting criteria using a novel interval-valued fuzzy-rough based Delphi method.

This study investigates the degree of importance of criteria affecting the optimal site selection of offshore wind farms. Firstly, forty two different influential criteria have been selected by reviewing the scientific literature on offshore wind farm site selection. Secondly, a survey has been conducted receiving a response from thirty four internationally renowned experts across seventeen countries. Each participant is asked to indicate the importance and relevance of each criterion based on their experience. Finally, the importance of each criterion for offshore wind farm site selection is determined using a novel Decision Making-Level Based Weight Assessment (LBWA) approach based on interval-valued fuzzy-rough numbers (IVFRN). The proposed method allows exploitation of the uncertainties and subjectivity that exist in the decision-making process. The results from this study improve our understanding of the importance and impact of each criterion which we believe would be invaluable for the future studies on the site selection of offshore wind farms.

Assessing the cumulative exposure of wildlife to offshore wind energy development.

Governments and developers are pursuing offshore wind energy to address climate change, but multiple wind farms may cumulatively affect wildlife populations. Assessments of cumulative effects must first calculate the cumulative exposure of a wildlife population to a hazard and then estimate how the exposure will affect the population. Our research responds to the first need by developing a model designed to assess how different wind farm siting scenarios cumulatively expose wildlife. The model assesses cumulative exposure by identifying all locations where development could occur, placing wind farms within this suitability layer, and then overlaying wind engineering and biological data sets. The first model output is a graphical representation of how offshore wind farm siting decisions affect wildlife cumulative exposure. The second output is an index that ranks which offshore wind farm siting decisions will have the greatest influence on wildlife cumulative exposure. Together these outputs provide stakeholders with valuable information that could be used to guide siting and management decisions.

Is wind energy increasing the impact of socio-ecological change on Mediterranean mountain ecosystems? Insights from a modelling study relating wind power boost options with a declining species.

The growing concern about future challenges of energy security and climate change has led to the expansion of renewable energy production, with a special emphasis on wind power. Despite the environmental advantages of wind power, it's important to assess the impacts caused by the presence of wind farms on wildlife, particularly on species also affected by habitat loss and degradation. In Mediterranean Europe, the skylark (Alauda arvensis) is a declining passerine that breeds in mountain habitats vulnerable to the abandonment of traditional management practices and climate change. We have created a spatially explicit agent-based model (ABM) in order to replicate the selection of territories, evaluating the effect of wind farms on the mortality rate of breeding males. We were especially interested in assessing the mortality rates related with the interplay between habitat loss due to socio-ecological change and increasing wind power using alternative strategies: adding wind turbines or substituting existing wind turbines by more powerful ones, i.e. repowering. Several known aspects related with the risk of collision of A. arvensis with wind turbines were considered, particularly regarding the male habitat selection and behaviour displayed throughout the breeding season. By simulating a sequential contraction of suitable habitat for the species, we found a substantial increase in the breeding territories superimposed to the wind farm influence zone. In these conditions males' relative mortality was predicted to suffer significant increases. For equivalent wind power, adding wind turbines produced significant increases in the males' relative mortality, whereas repowering didn't. Based on our findings we propose repowering as a defensible strategy to increase wind energy production without increasing A. arvensis collision risk. We highlight that this strategy might also benefit other vulnerable bird and bat species associated with declining habitats of mountain ridges in the Mediterranean region.

A new method for wind speed forecasting based on copula theory.

How to determine representative wind speed is crucial in wind resource assessment. Accurate wind resource assessments are important to wind farms development. Linear regressions are usually used to obtain the representative wind speed. However, terrain flexibility of wind farm and long distance between wind speed sites often lead to low correlation. In this study, copula method is used to determine the representative year's wind speed in wind farm by interpreting the interaction of the local wind farm and the meteorological station. The result shows that the method proposed here can not only determine the relationship between the local anemometric tower and nearby meteorological station through Kendall's tau, but also determine the joint distribution without assuming the variables to be independent. Moreover, the representative wind data can be obtained by the conditional distribution much more reasonably. We hope this study could provide scientific reference for accurate wind resource assessments.

The effects of continuously acoustical stress on cortisol in milkfish (Chanos chanos).

Strong underwater acoustic noise has been known that may cause hearing loss and actual stress in teleost. However, the long-term physiological effects of relatively quiet but continuously noise on fish were less understood. In present study, milkfish, Chanos chanos, were exposed to the simulated-wind farm noise either quiet (109dB re 1µPa/125.4Hz; approx. 10-100m distant from the wind farm) or noisy (138dB re 1µPa/125.4Hz; nearby the wind farm) conditions for 24h, 3days and 1week. Comparing to the control group (80dB re 1µPa/125.4Hz), the fish exposed to noisy conditions had higher plasma cortisol levels in the first 24h. However, the cortisol levels of 24h spot returned to the resting levels quickly. The fish exposed under noisy condition had significantly higher head kidney star (steroidogenic acute regulatory) and hsd11b2 (11-ß-hydroxysteroid dehydrogenase 2) mRNA levels at the following treatment time points. In addition, noise exposure did not change hypothalamus crh (Corticotropin-releasing hormone) mRNA levels in this experiment. The results implied that the weak but continuously noise was a potential stressor to fish, but the impacts may be various depending on the sound levels and exposure time. Furthermore, this study showed that the continuous noise may up-regulate the genes that are related to cortisol synthesis and possibly make the fish more sensitive to ambient stressors, which may influence the energy allocation appearance in long-term exposures.

Constraints on Development of Wind Energy in Poland due to Environmental Objectives. Is There Space in Poland for Wind Farm Siting?

The objective of the study was to evaluate spatial effects of adopting environmental criteria for wind farm siting, i.e., the criteria related to the settlement system and those with regards to landscape values. The set of criteria was elaborated on the basis of literature and experience-based knowledge. Some of the criteria selected are legally binding. The analyses were carried out with the use of GIS tools. Settlement areas with 1000 and 2000 m wide buffer zones, and the areas with the highest landscape values, were assumed as particularly sensitive receptors to wind farm impacts. The results show significant constraints on wind farm siting in Poland. Although the constraints are regionally diversified, they concern 93.9 % of the total country area (1000 m buffer zone) or 99.1 % (2000 m buffer zone). Presumably even greater constraints would be revealed by an additional detailed analysis at a local level. The constraints on wind farm siting in Poland cannot be decreased, because of both social attitudes and demand for appropriate environmental standards, which should be taken into account in spatial and energy policies at all decision making level.