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Wind power generation is a renewable energy source, and its power output is influenced by multiple factors such as wind speed, direction, meteorological conditions, and the characteristics of wind turbines. Therefore, accurately predicting wind power is crucial for the grid operation and maintenance management of wind power plants. This paper proposes a hybrid model to improve the accuracy of wind power prediction. Accurate wind power forecasting is critical for the safe operation of power systems. To improve the accuracy of wind power prediction, this paper proposes a hybrid model incorporating variational modal decomposition (VMD), a Sparrow Search Algorithm (SSA), and a temporal-convolutional-network-based bi-directional gated recurrent unit (TCN-BiGRU). The model first uses VMD to break down the raw power data into several modal components, and then it builds an SSA-TCN-BIGRU model for each component for prediction, and finally, it accumulates all the predicted components to obtain the wind power prediction results. The proposed short-term wind power prediction model was validated using measured data from a wind farm in China. The proposed VMD-SSA-TCN-BiGRU forecasting framework is compared with benchmark models to verify its practicability and reliability. Compared with the TCN-BiGRU, the symmetric mean absolute percentage error, the mean absolute error, and the root mean square error of the VMD-SSA-TCN-BiGRU model reduced by 34.36%, 49.14%, and 55.94%. [ABSTRACT FROM AUTHOR]

The article focuses on the conflicts that arise in rural America when companies attempt to build wind farms, and how the law can be used to resolve these conflicts. The article discusses the legal and social forces that interact with the transition to a green economy and offers recommendations on how to improve the process, recognize the burden, and ensure fair compensation for those involved.

Variable renewable generation and load fluctuations induce significant balancing cost in power system operation. To overcome this issue, this paper proposes a control architecture that leverages inherent regulation capabilities of wind turbines to minimize the system‐wide balancing costs. Instead of handling wind power fluctuations via power filtering algorithms that are agnostic to system‐wide power imbalance, this paper aims to optimize the wind power generation profile from system perspective. In the proposed method, wind turbines are modelled as semi‐dispatchable units, where the dispatch command is dynamically generated at every automatic generation control cycle by considering mileage payments as an indicator of system‐wide imbalance. As a result, local resources of wind turbines are optimally leveraged in real‐time to mitigate system‐wide power imbalances. The proposed strategy and state‐of‐the‐art techniques are compared in comprehensive high‐fidelity case studies. Our simulation results demonstrate that the proposed system‐aware regulation scheme can alleviate system balancing costs without investments into energy storage systems. [ABSTRACT FROM AUTHOR]

Wind farm layout optimisation (WFLO) is carried out in this study considering the wake effect, and cabling connections and losses. The wind farm micro‐siting optimisation problem is formulated with the aid of Jensen's wake model. Cabling between the wind turbines and the point of common coupling is an important aspect of the wind farm design as it affects the capital investment as well as income over the lifetime of the wind farm. The cabling layout must satisfy the connection of the wind turbines to the point of common coupling in such a way that the total cable length is reduced while reliability is maintained. Introducing the cabling layout optimisation to the WFLO, further complicates the optimisation problem. An integrated tool is developed to optimise the wind farm layout and cabling simultaneously. The main contribution of this work is the development of an integrated tool that maximizes the energy production of the wind farm via optimal allocation of wind turbines with optimal cable routing. This tool considers the capital cost of wind turbines and cabling, wind farm power production, and power losses in the cabling over the lifetime of the wind farm. The proposed co‐optimisation problem is solved using genetic algorithm. The decision variables are the wind farm layout, cable paths and sizes, and the location of the point of common coupling within the land perimeter. A case study incorporating a multi‐speed and multi‐direction wind profile is carried out to demonstrate the applicability of the proposed approach. Moreover, the proposed methodology is compared to the separate optimisation method where the WFLO and cabling optimisation are solved sequentially with two separate steps. It is shown that the co‐optimisation method is superior in terms of cable power losses, overall wind farm cost, and compactness (land use). [ABSTRACT FROM AUTHOR]

One of the most important obstacles in improving the resilience of critical infrastructures is the timely replacement of critical components. However, the shortage of spare parts often keeps practitioners from achieving this goal. Moreover, it is common that spare parts may deteriorate on the shelf. In this paper, we focus on a one-component deteriorating system carrying one deteriorating spare part. Both failure-switching and preventive-switching strategies are considered for component replacement during each operating cycle to minimize the long-run cost rates. A case study on gearbox replacements for an offshore wind farm is provided to illustrate the proposed joint component replacement and reordering policies in improving the resilience of critical infrastructures. Although the chance of failure of such a system may be reduced by the preventive-switching strategy, the failure-switching strategy may still result in better economic performance due to the on-shelf deterioration and salvage of spare parts. Abbreviations: CDF: cumulativedistributionfunction; PDF: probabilitydensityfunction; PR: preventivereplacement; CE: cumulativeexposure [ABSTRACT FROM AUTHOR]

Wind power forecasting has gained significant attention due to advances in wind energy generation in power frameworks and the uncertain nature of wind. In this manner, to maintain an affordable, reliable, economical, and dependable power supply, accurately predicting wind power is important. In recent years, several investigations and studies have been conducted in this field. Unfortunately, these examinations disregarded the significance of data preprocessing and the impact of various missing values, thereby resulting in poor performance in forecasting. However, long short-term memory (LSTM) network, a kind of recurrent neural network (RNN), can predict and process the time-series data at moderately long intervals and time delays, thereby producing good forecasting results using time-series data. This article recommends a hybrid forecasting model for forecasting wind power to improve the performance of the prediction. An improved long short-term memory network-enhanced forget-gate network (LSTM-EFG) model, whose appropriate parameters are optimized using cuckoo search optimization algorithm (CSO), is used to forecast the subseries data that is extracted using ensemble empirical mode decomposition (EEMD). The experimental results show that the proposed forecasting model overcomes the limitations of traditional forecasting models and efficiently improves forecasting accuracy. Furthermore, it serves as an operational tool for wind power plants management. [ABSTRACT FROM AUTHOR]

The major drawback of wind energy relies in its variability in time, which necessitates specific strategies to be settled. One such strategy can be the coordination of wind power production with a co-located power generation of dispatchable energy source (DES), e.g., thermal power station, combined heat and power plant, gas turbine or compressed air energy storage. In this paper, we consider an energy producer that generates power by means of a wind park and of a DES and sells the produced energy to an isolated grid. We determine the optimal quantity of energy produced by a DES, given the unit cost of this energy, that a power producer should buy and use to hedge against the risk inherent in the production of energy through wind turbines. We determine the optimal quantity by solving a static optimization problem taking into account the possible dependence between the amount of energy produced by wind turbines and electricity prices by using a copula function. Several particular cases are studied that allow the determination of the optimal solution in an analytical closed form. Finally, a numerical example concerning a real 48 MW wind farm located in Poland and Polish Power Exchange shows the possibility of implementing the model in real-life problems. [ABSTRACT FROM AUTHOR]

This study is based on the simulation and optimization of renewable energy system of a Police Control Room Sagar Central India. The meteorological data of solar insolation, hourly wind speed, are taken from Sagar Central India (Longitude 78° 45' and Latitude 23°50) and the pattern of load consumption of Police control room is studied and suitably modeled for the optimization of the hybrid energy system using HOMER software. The report offers a reliability assessment of hybrid renewable energy system of the field region. Weather data and the load profile of the study area are recorded and fault tree analysis is used for the reliability assessment of hybrid renewable energy system which further provides a static depiction of the combinations of failures and actions that can cause the specified critical fault to occur. [ABSTRACT FROM AUTHOR]

Highlights • An adaptive active fault-tolerant control strategy is proposed to improve work reliability. • A new generalized-perturbation based dynamic model for MPPT control is established. • The control law doesn't require specific information about actuator's fault pattern or degree. • The control law gains can adaptively adjust by feedback to reduce fault compensation delay. • It ensures a smooth and robust control process whether in actuator normal or fault condition. Abstract In order to improve the dynamic performance and reliability of the maximum power point tracking (MPPT), this paper proposes an adaptive active fault-tolerant control (AFTC) strategy of the wind power generation system to overcome uncertain problems, including potential partial loss of actuator effectiveness, unknown modeling errors and external disturbances. The AFTC method is designed according to a new dynamic model of angular speed tracking for wind power generation system based on generalized perturbation. It neither requires the actuator fault information detection, nor relies on the system model parameters and external disturbance identification. In terms of the on-line estimation of the disturbance boundary amplitude and the nonlinear state feedback based on MPPT tracking error, the gain of the switching control is adaptively adjusted to speed up the convergence of the system and reduce the delay of the fault compensation. A first-order integral process is involved in the AFTC law to further weaken the chattering of output signal amplitude, hence smooth the torque and improve the tracking accuracy during generation. In addition, the global stability of the closed-loop control system based on AFTC is proved by the Lyapunov approach. By comparing with the conventional linear PID control and the nonlinear dynamic state feedback control (NDSFC), the case studies simulation results validate the great MPPT fault-tolerant capability even subject to partial loss of actuator effectiveness, and separately show the strong robustness and self-adaptation of the proposed adaptive AFTC method. [ABSTRACT FROM AUTHOR]

The wind energy penetration rate is being increased in majority of European countries. However, a high penetration rate could endanger the stability of power systems, particularly in small islands. Hydropower plays an important role in the regulation and control of isolated power systems with renewable sources, but it may not be able to maintain the frequency within grid requirements. This is the case of El Hierro power system (Canary archipelago), where a hybrid wind–pumped storage hydropower plant (PSHP) was committed to reduce the use of fossil fuels. Currently, frequency regulation is only provided by the PSHP and diesel generators. Therefore, it is proposed that variable-speed wind turbines (VSWTs) contribute to frequency regulation, thereby minimizing the need for fossil fuels. This study aims to conduct an analysis on the effect of the VSWT contribution to frequency regulation in the power system of El Hierro. It is based on classical control tools from a linearized mathematical model considering different VSWT regulation strategies. The eigenvalues, damping ratio, and participation factors of the state variables have been obtained. The more significant oscillation modes in the dynamic response of the system have been identified. According to this modal analysis, a methodology for the adjustment of the PSHP and VSWT controller gains is proposed. An improvement in the quality of frequency regulation while maintaining the El Hierro system frequency within grid requirements has been proved based on simulating different events related to wind speed or variations in the power demand, using a nonlinear model of the combined wind–hydro power plant. [ABSTRACT FROM AUTHOR]

The increasing penetration of wind power in power systems is an ongoing trend. In wind farms, harmonic distortion and resonances concern both wind turbine manufacturers and power system operators. The frequency of occurrence and amplification factors of resonances are affected by the number of wind turbines in operation, their connection in series or in parallel through the collecting grid and the type of active front-end converter employed. This paper focuses on the latter and proposes the use of the True Unity Power Factor converter as an alternative, in which the resonance avoidance feature is built-in the wind turbines and is independent of external circumstances. Besides suppressing current harmonics below the 50th order without using filter capacitors, the TUPF converter modifies the parallel resonance characteristics of the wind farm by pushing the frequencies of their occurrences to higher values, making them less likely to be excited. The nearly sinusoidal output current behavior is demonstrated via simulation and experimental results and the variation in resonance peak characteristics is corroborated by modal analysis in a case study wind farm. It is concluded that there is a significant advantage in employing these converters in wind power generation, especially at the buses identified as the most prone to being the center of resonances. [ABSTRACT FROM AUTHOR]

Wind power is a renewable source of great value for the sustainable development of society in the context of global environmental changes. This power source features many advantages such as accessibility, inexhaustibility and sustainability, and its harvest and conversion have experienced a rapid growth in both developed and developing countries. The present study aims to geographically analyze the distribution and intensity of onshore wind resources (assessed using the wind power density parameter) globally, continentally and nationally. Using the most recent and detailed global spatial data on wind power density, which were classified after NREL (National Renewable Energy Laboratory) in seven potential classes, our results showed that, globally, there are five major hotspots of abundant wind resources, delimited based on the large-scale presence of the most favourable potential classes, i.e. Excellent (500–600 W/m 2 ), Outstanding (600–800 W/m 2 ) and Superb (>800 W/m 2 ), also called in our paper EOS classes. These hotspots consist of the central-northern region of North America, southern South America, northern/north-western Europe, northern Asia and the central-south-eastern region of the Asian continent. These regions bring together most of the entire global area of EOS classes, which totals ∼4 mil km 2 or ∼3% of the world's land area. Continentally, the most extensive EOS areas are found in Asia (∼1.5 mil km 2 , 3.5% of the continent), followed by North and Central America (∼1 mil km 2 , 4.3%), South America (∼0.7 mil km 2 , 4.1%), Europe (∼0.5 mil km 2 , 5%), Africa (∼0.1 mil km 2 , 0.4%), and Australia and Oceania (∼0.1 mil km 2 , 1.3%). Nationally, it was found there are 8 epicentre-countries for wind potential (with absolute EOS areas that range between 0.1 and almost 1 mil km 2 across the countries), namely China (∼0.9 mil km 2 , 10% of the country), followed by Canada (∼0.6 mil km 2 , 6%), the United States (∼0.5 mil km 2 , 5%), Argentina (∼0.4 mil km 2 , 14%), Russia (∼0.4 mil km 2 in total, in Europe and Asia, 2%), Chile (0.2 mil km 2 , 28%), Norway (∼0.1 mil km 2 , 30%) and Afghanistan (∼0.1 mil km 2 , 16%). Results show these countries are the most suitable for large-scale wind power installations in the world. Also, our findings not only provide an overall image of wind resources availability worldwide, but they can also be a useful tool for the transition towards a carbon-free global economy, by helping world governments better understand – and subsequently use – their wind resources at regional or local levels. [ABSTRACT FROM AUTHOR]

Abstract—It is extremely difficult to realize real-time active-reactive optimal power flow (RT-AR-OPF) in distribution networks (DNs) with wind stations (WSs) due to the conflict between the fast changes in wind power and the slow response from the optimization computation. To address this problem, a new lookup-table-based RT-AR-OPF framework is developed in this paper. According to the forecasted wind power for a prediction horizon, scenarios are generated based on its stochastic distribution. The corresponding mixed-integer nonlinear programming (MINLP) problems are solved online which simultaneously optimize the active and reactive power dispatch of WSs, active-reactive reverse power flow, and discrete slack bus voltage, resulting in a lookup table. Based on the actual wind power available in a sampling time, one of the solutions will be selected and realized to the DN. A new reconciliation algorithm is proposed to ensure both the feasibility and optimality of the realized operation strategy. The applicability of the proposed framework is shown using a medium-voltage DN. [ABSTRACT FROM AUTHOR]

This paper presents the results of the second edition of the Wind Farm Layout Optimization Competition, which was held at the 22nd Genetic and Evolutionary Computation COnference (GECCO) in 2015. During this competition, competitors were tasked with optimizing the layouts of five generated wind farms based on a simplified cost of energy evaluation function of the wind farm layouts. Online and offline APIs were implemented in C++, Java, Matlab and Python for this competition to offer a common framework for the competitors. The top four approaches out of eight participating teams are presented in this paper and their results are compared. All of the competitors' algorithms use evolutionary computation, the research field of the conference at which the competition was held. Competitors were able to downscale the optimization problem size (number of parameters) by casting the wind farm layout problem as a geometric optimization problem. This strongly reduces the number of evaluations (limited in the scope of this competition) with extremely promising results. [ABSTRACT FROM AUTHOR]

The wind speed forecasting is crucial for the wind power conversion and management. In this study, a new hybrid model consisting of the EWT ( Empirical Wavelet Transform ) decomposition, the LSTM ( Long Short Term Memory ) network, the RELM ( Regularized Extreme Learning Machine ) network and the IEWT ( Inverse Empirical Wavelet Transform ) reconstruction is proposed. The hybrid model are carried out as follows: the EWT is employed to decompose the raw wind speed series into several sub-layers; the LSTM network is executed as the principal predictor of each sub-layer; the RELM network is utilized as the subordinate predictor to model the forecasting error series of each sub-layer; the IEWT is adopted to construct the final forecasting series and filter the outliers. To validate the forecasting capacity of the proposed hybrid EWT-LSTM-RELM-IEWT model, seven different forecasting models are implemented on five wind speed time series. The experimental results demonstrate that: (1) the single LSTM model cannot provide satisfactory wind speed forecasting results in the involved wind speed time series; (2) the EWT can promote the wind speed forecasting accuracy and stability of the LSTM network significantly; (3) the RELM network based error modeling method improves the performance of the proposed EWT-LSTM-IEWT forecasting structure significantly; (4) the IEWT based outlier correction method is effective in promoting the wind speed forecasting accuracy and stability in the proposed EWT-LSTM-RELM structure; and (5) among all the involved models, the proposed hybrid model has the best performance in one-step to five-step predictions. [ABSTRACT FROM AUTHOR]

In order to satisfy the diversified control requirements, improve the controllability of modern power systems and accommodate solar and wind sources with stochastic output power. The response analysis has been conducted for the centralized control and operation cockpit based on fuzzy control algorithm for the operation smart system (OS2) proposed by power grid. In particular, the KPI information is select as the key input of the fuzzy control system. Meanwhile, the commands are determined by using the fuzzy logic to perform the actual control on the power system. At the same time, apart from achieving the aforementioned requirements, feedback control schemes are involved to correct the determination in real-time so that the performance of the control system can be guaranteed and the high accuracy objective can be fulfilled. Simulation results verify the proposed control diagram based fuzzy control logic. It can be clearly seen from the theoretical analysis and simulation results that the centralized control and operation cockpit based on fuzzy control can make significant improvements in system response. [ABSTRACT FROM AUTHOR]

Offshore wind farm assets require regular inspections. Studies and industry experience have highlighted the importance of selecting the appropriate inspection and maintenance (I&M) plan, as it directly impacts the reliability of the components and the associated maintenance cost. If inspections are carried out too frequently, the associated risks will be low and reliability will be high, but the cost will also be high. On the contrary, if no or very few inspections are carried out, unexpected failures of the structures could occur. This paper presents a risk-based inspection (RBI) framework for offshore wind farms, building on existing knowledge from other industries including nuclear, oil & gas, chemical and aerospace. This approach considers the probability, the consequences and the cost of the operational or maintenance activity via a criticality analysis that allows optimal selection and prioritisation of I&M activities. A case study is presented where this framework is implemented on the transition pieces (TPs) of the wind turbines, by investigating information received from design, operation and inspection reports as well as monitoring equipment. Guidelines are also proposed on how to utilise novel monitoring and visual inspection techniques to further improve the implementation of RBI. The results of this paper suggest a less frequent I&M strategy, which could reduce the associated TP inspection costs by up to £0.7 million/MW installed and increase the safety of personnel. The study will be of interest to offshore wind farm developers, operators and maintenance providers, to better prioritise I&M activities and increase the operating revenue of their assets. [ABSTRACT FROM AUTHOR]

Historical wind power production figures are not available when a new wind farm goes into power production. It is thus difficult to forecast power productions of such wind farms that is required for demand management. Wind power is a function of weather variables and it is likely that weather patterns of the new station is similar to some existing operational wind farms. It will thus be interesting to investigate how the forecast/prediction models of the existing wind farms can be adapted to generate a prediction model for new stations. On this regard, we explore a particular branch of machine learning called Multi Source Domain Adaptation (MSDA). MSDA approaches identify a weighing mechanism to fuse the predictions from the source models (i.e. existing stations) to produce a prediction for the target (i.e. new station). The weights are computed based on similarity of data distributions between source and target. Conventional MSDA approaches utilise an instance based weighting scheme and we identified that fails to capture the data distribution of wind data sets appropriately. We thus propose a novel cluster based MSDA approach that captures wind data distribution in terms of natural groups that exist within data and compute distribution similarity (and source weight) in terms of cluster distributions. Experimental results demonstrate that cluster based MSDA approach can reduce regression error by 20.63% over instance based MSDA approach. [ABSTRACT FROM AUTHOR]

Prognostics and Health Management (PHM) technologies have been introduced into wind turbines to forecast the Remaining Useful Life (RUL). PHM with RUL predictions enables predictive maintenance for wind turbines prior to failure, thus avoiding corrective maintenance that may be expensive and cause long downtimes. For a wind farm managed using a power purchase agreement (PPA), a simulation-based European real options analysis model is used to schedule predictive maintenance by maximizing the predictive maintenance option value. For multiple wind turbines indicating RULs concurrently, the predictive maintenance value for each turbine depends on the operational state of all the other turbines, the amount of energy delivered, and the energy delivery target, prices and penalization mechanism for under-delivery defined in the PPA. A case study is presented in which the optimum predictive maintenance opportunity is determined for a wind farm managed using a PPA. To the authors' knowledge, this is the first wind farm maintenance model including a PPA, and the case study demonstrates that the optimum predictive maintenance opportunity for a PPA-managed farm is different from the same farm managed using an “as-delivered” contract, and also differs from the optimum predictive maintenance opportunities for the individual turbines with RULs managed in isolation. [ABSTRACT FROM AUTHOR]

An efficient windfarm layout to harness maximum power out of the wind is highly desirable from technical and commercial perspectives. A bit of flexibility on layout gives leeway to the designer of windfarm in planning facilities for erection, installation and future maintenance. This paper proposes an approach where several options of optimized usable windfarm layouts can be obtained in a single run of decomposition based multi-objective evolutionary algorithm (MOEA/D). A set of Pareto optimal vectors is obtained with objective as maximum output power at minimum wake loss i.e. at maximum efficiency. Maximization of both output power and windfarm efficiency are set as two objectives for optimization. The objectives thus formulated ensure that in any single Pareto optimal solution the number of turbines used are placed at most optimum locations in the windfarm to extract maximum power available in the wind. Case studies with actual manufacturer data for wind turbines of same as well as different hub heights and with realistic wind data are performed under the scope of this research study. [ABSTRACT FROM AUTHOR]

Large Eddy Simulations of the turbulent flow over an array of wind turbines have been performed to evaluate a model-free approach to power optimization. Two different implementations have been tested: (i) individual extremum-seeking control (IESC), which optimizes the power of the single turbines individually; (ii) nested ESC (NESC), which coordinates the single controllers to seek a farm-level optimum. Both schemes provide a gain over the baseline, which operates all the turbines with ideal design set-points. These settings are found to be sub-optimal for waked turbines. The NESC provides a slightly larger power production than the independent ESC, albeit it has a slower convergence to the optimum. Therefore, depending on wind variability, both strategies may be employed. IESC is more appropriate for sites with wind conditions changing on a short time scale, while NESC should be preferred when the wind conditions are quite stable. Since the extremum-seeking algorithm is model-free, uncertainties in atmospheric conditions, aging of the turbine or numerical dissipation due to the sub-grid model should not change the general conclusions reached in this paper. This methodology can provide reliable results and permits to gain, through the analysis, a useful knowledge on the mechanisms leading to the performance enhancement. [ABSTRACT FROM AUTHOR]

Wind has been one of the popular renewable energy generation methods in the last decades. Foreknowledge of power to be generated from wind is crucial especially for planning and storing the power. It is evident in various experimental data that wind speed time series has non-linear characteristics. It has been reported in the literature that nonlinear prediction methods such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) perform better than linear autoregressive (AR) and AR moving average models. Polynomial AR (PAR) models, despite being non-linear, are simpler to implement when compared with other non-linear AR models due to their linear-in-the-parameters property. In this study, a PAR model is used for one-day ahead wind speed prediction by using the past hourly average wind speed measurements of Çeşme and Bandon and performance comparison studies between PAR and ANN-ANFIS models are performed. In addition, wind power data which was published for Global Energy Forecasting Competition 2012 has been used to make power predictions. Despite having lower number of model parameters, PAR models outperform all other models for both of the locations in speed predictions as well as in power predictions when the prediction horizon is longer than 12 h. [ABSTRACT FROM AUTHOR]

The strong linkage of electric power and heat supplies can be decoupled to reduce wind power curtailment by exploiting the energy storage and regulation capabilities of the district heating network (DHN), electric boilers and heat storage tanks. In this paper, the coordinated dispatch of integrated electrical and heating systems (CDIEHS), considering the energy storage of both pipelines in the DHN and heat storage tanks, is formulated as a convex quadratic program. Since the electric power system (EPS) and the district heating system (DHS) are controlled separately by different operation organizations, CDIEHS is solved in a decentralized manner using optimality condition decomposition (OCD). The OCD algorithm, with guaranteed convergence for convex programs, can achieve a fully distributed solution for the CDIEHS and requires only minor boundary information exchange between the EPS and the DHS. The simulation results are discussed for two test systems, including a real power system, to demonstrate the effectiveness of the proposed decentralized method, which achieves the same solution as the centralized method in a moderate number of iterations. [ABSTRACT FROM AUTHOR]

Aero-elasticity is a term that refers to the interaction between the aerodynamic, inertial and elastic loads when a structure is exposed to fluid flow such as turbulent wind inflow. Various commercial and research-based simulators are available to compute the wind turbine aero-elastic loads. These aero-elastic simulators are of varying complexity and might bear different underlying assumptions, pertaining to physics, mathematical and computational formulations. However, currently established practice dictates that the adopted aero-elastic simulators are verified and validated on the basis of measurements from test wind turbines. As a result, it is generally hard to establish one simulator as superior to another in terms of their predicted output. The objective in this paper is to statistically aggregate the fatigue load on a wind turbine blade when simultaneous simulations are performed using multiple simulators. The simulators of the wind turbine blade are of varying fidelity, and uncertainty in the modelling and assumptions on the model inputs are implicitly included, and taken into account in the statistical analysis. The main concept followed here is that rather than treating the output of the simulators as individual information sources, we consider them as part of an ensemble, which can be clustered and then aggregated to predict the “most likely” fatigue load, hence reducing the inherent model-form uncertainty. [ABSTRACT FROM AUTHOR]

Wind power ramp events are sudden increases or decreases of wind speed within a short period of time. Their prediction is nowadays one of the most important research trends in wind energy production because they can potentially damage wind turbines, causing an increase in wind farms management costs. In this paper, 6-h and 24-h binary (ramp/non-ramp) prediction based on reservoir computing methodology is proposed. This forecasting may be used to avoid damages in the turbines. Reservoir computing models are used because they are able to exploit the temporal structure of data. We focus on echo state networks, which are one of the most successfully applied reservoir computing models. The variables considered include past values of the ramp function and a set of meteorological variables, obtained from reanalysis data. Simulations of the system are performed in data from three wind farms located in Spain. The results show that our algorithm proposal is able to correctly predict about 60% of ramp events in both 6-h and 24-h prediction cases and 75% of the non-ramp events in the next 24-h case. These results are compared against state of the art models, obtaining in all cases significant improvements in favour of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The study is a qualitative approach and looks into new ways for the effective energy management of a wind farm (WF) operation in a suburban or near-urban environment in order the generated electricity to be utilised for hydroponic farming purposes as well. Since soilless hydroponic indoor systems gain more and more attention one basic goal, among others, is to take advantage of this not typical electricity demand and by managing it, offering to the grid a less fluctuating electricity generation signal. In this paper, a hybrid business model is presented where the Distributed Energy Resources (DER) producer is participating in the electricity markets under competitive processes (spot market, real-time markets etc.) and at the same time acts as a retailer offering – based on the demand – to the hydroponic units for their mass deployment in an area, putting forward an integrated energy-food nexus approach. [ABSTRACT FROM AUTHOR]

With increasing penetration of wind power in power systems, it is important to track scheduled wind power output as much as possible during ramping events to ensure security of the system. In this paper, a non-cooperative coordination strategy based on the game theory is proposed for the regulation of wind farm clusters (WFCs) in order to track scheduled wind power of the WFC during ramping events. In the proposed strategy, a non-cooperative game is formulated and wind farms compete to provide regulation to the WFC during ramping events. A regulation revenue function is proposed to evaluate the competition process of wind farms to provide regulation to the WFC which includes revenue of effective regulation (ER), power support regulation and punishment regulation. The multi-time-interval Nash equilibrium condition is derived for the regulation competition process of wind farms. By setting parameters of the regulation revenue function according to the derived Nash equilibrium condition, the ER strategy is the Nash equilibrium of the regulation competition. Case studies were conducted with the power output data of wind farms from State Grid Jibei Electric Power Company Limited of China to demonstrate the efficacy of the proposed coordination strategy during ramping events. [ABSTRACT FROM AUTHOR]

This paper investigates the load frequency control (LFC) for wind power systems with modeling uncertainties and variant loads. Since the system state is difficult to be accurately measured due to perturbation of nonlinear load, an observer is designed for reconstructing a substitution system state. Afterwards, an integral sliding surface is designed and a sliding mode LFC (SMLFC) strategy is proposed for reducing frequency deviations of the overall power system. Remarkably, it has been pointed out that a larger convergence rate of the observer error system has positive influences on the SMLFC performances, while the larger observer gain deteriorates the dynamic behavior. For seeking an acceptable balance so as to determine the optimal controller parameters, a collaborative design algorithm is proposed. The proposed method not only guarantees the asymptotical stability of overall power systems but also capable of improving the system robustness. Numerical examples are provided to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

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In recent years, wind power has been developing rapidly, and it also has achieved large-scale commercial application of the promotion. However, the operation and maintenance technology of the wind farm fails to match the demand for wind power development. Which particular in: the utilization rate of resource in wind farm is low and the operation and maintenance management level is not high enough. Resulting in longer downtime of the wind turbine, seriously affects the economic efficiency of wind farms. In this paper, arming to resolving the wind farm operation and maintenance problems, build a wind farm operation and maintenance video supportive system (MVSS) to improve the utilization rate by the mobile communications technology and networking technology. With the operation and maintenance video supportive platform, the utilization of wind farm resources and the level of management can be improved. The practical application shows that the proposed wind farm operation and maintenance video supportive platform could improve the efficiency of operation and maintenance, reduce the downtime by an average 10-20 hours, which has significant economic and promotional value. [ABSTRACT FROM AUTHOR]

The siting of both onshore and offshore wind farms has caused controversy in a number of countries, particularly in Europe, which currently has the world’s largest array of offshore wind turbines. Like the rest of the world there has been a dramatic increase in wind farm development In Australia over the last two decades but none of this has occurred in the offshore environment. Australian wind farms are predominantly located onshore around the southern part of the continent where wind energy is high. This has specifically affected rural coasts where it has created planning and land use policy issues along with environmental impacts and associated public and political reaction. This paper examines the geographic spread, timing and concentration of Australian wind farms around the coast. It also examines the extent to which scientific knowledge on wind farm impacts is incorporated into different state and federal land use and environmental policy and legislative frameworks. The paper concludes that the rapidity in the expansion of the Australian wind energy sector has outpaced strategic land use planning resulting in a piece-meal and cumulative wind farm development on rural coasts. [ABSTRACT FROM AUTHOR]

In this paper a 1.5 kW flux switching permanent magnet (FSPM) generator is presented for direct drive small scale wind turbine applications. For maximizing induced voltage and the output torque while minimizing cogging torque and unbalanced radial magnetic force (UMF), the proposed machine exhibits a new 6/19 stator pole/rotor teeth number and an outer rotor configuration. At first, in the paper an analytical design has been developed, then a finite element method (FEM) analysis is carried out for validating the analytical procedure and for design improvement. The simulation results extracted by FEM confirm the theoretical analysis procedure and help in the understanding of the performance analysis of the machine against the variations of the design variables. Furthermore, an experimental laboratory prototype of the proposed FSPM is implemented to confirm the analytical design and FEM modelling approaches. A comparison of induced voltage, torque, UMF and cogging torque produced by different FSPM configurations present in literature respect to the proposed generator has been developed. The results show the goodness of the adopted methodology and prove that, because of suitable electromagnetic performance of the proposed FSPM generator, it could be counted as a proper candidate for small scale wind turbine applications. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In a wind farm, the upstream wind turbine may cause power loss to the downstream wind turbines due to the wake effect. Meanwhile, the energy production is determined by the power generation and the lifetime of the wind turbine. In this paper, an optimal active power control method is proposed to maximize the energy production of wind farms by considering the wake effect and the lifetime of wind turbine. It starts with the analysis of the pitch angle curve and active power curve seen from the Maximum Power Point Tracking (MPPT) of individual wind turbines. Taking the wake effect into account, the pitch angle curve and active power curve are optimized with the aim of Maximum Power Generation (MPG) of the wind farm. Afterwards, considering the lifetime of wind turbines, a comparison is offered between the MPPT method and the MPG method for energy production using a simplified two-turbine wind farm as an example. Due to the small range of the effective wake area, it is found that the energy production is almost the same. Finally, the pitch angle curve and active power curve are optimized according to the Maximum Energy Production (MEP) of a wind farm. Upon considering and contrasting the MPPT method and the MEP method, it can be seen that the energy production of wind farms can be increased even in the case of there not being an effective wake area. [ABSTRACT FROM AUTHOR]

We characterise the long-term variability of European near-surface wind speeds using 142 years of data from the Twentieth Century Reanalysis (20CR), and consider the potential of such long-baseline climate data sets for wind energy applications. The low resolution of the 20CR would severely restrict its use on its own for wind farm site-screening. We therefore perform a simple statistical calibration to link it to the higher-resolution ERA-Interim data set (ERAI), such that the adjusted 20CR data has the same wind speed distribution at each location as ERAI during their common period. Using this corrected 20CR data set, wind speeds and variability are characterised in terms of the long-term mean, standard deviation and corresponding trends. Many regions of interest show extremely weak trends on century timescales, but contain large multidecadal variability. Since reanalyses such as ERAI are often used to provide the background climatology for wind farm site assessments, but contain only a few decades of data, our results can be used as a way of incorporating decadal-scale wind climate variability into such studies, allowing investment risks for wind farms to be reduced. [ABSTRACT FROM AUTHOR]

This paper presents a Decision Support System (DSS) for maintenance cost optimisation at an Offshore Wind Farm (OWF). The DSS is designed for use by multiple stakeholders in the OWF sector with the overall goal of informing maintenance strategy and hence reducing overall lifecycle maintenance costs at the OWF. Two optimisation models underpin the DSS. The first is a deterministic model that is intended for use by stakeholders with access to accurate failure rate data. The second is a stochastic model that is intended for use by stakeholders who have less certainty about failure rates. Solutions of both models are presented using a UK OWF that is in construction as an example. Conclusions as to the value of failure rate data are drawn by comparing the results of the two models. Sensitivity analysis is undertaken with respect to the turbine failure rate frequency and number of turbines at the site, with near linear trends observed for both factors. Finally, overall conclusions are drawn in the context of maintenance planning in the OWF sector. [ABSTRACT FROM AUTHOR]

The randomness of the wind source is a concerning issue for managing power plants in reliable conditions. High values of wind speed are undesirable since wind farms provide zero power for values greater than their cut-off thresholds. Also, the mechanical safety of the installations can be seriously compromised by extreme values of wind speed. Therefore, a reliable estimation of extreme values of wind speed is mandatory. An Inverse Burr distribution is proposed as an useful alternative for the probabilistic modeling of extreme values of wind speed. Distribution parameters were estimated through maximum likelihood and moment estimation procedures, and through a new proposal, the quantile estimation procedure. The proposed model is validated on several real wind datasets, comparing the proposed model with commonly-used extreme value models. Numerical applications showed that the proposed model is a valid and feasible alternative to the classical extreme value distributions for extreme values of wind speed. [ABSTRACT FROM AUTHOR]

Scanning LiDARs can be used to obtain three-dimensional wind measurements in and beyond the atmospheric surface layer. In this work, metrics characterizing wind turbine wakes are derived from LiDAR observations and from large-eddy simulation (LES) data, which are used to recreate the LiDAR scanning geometry. The metrics are calculated for two-dimensional planes in the vertical and cross-stream directions at discrete distances downstream of a turbine under single-wake conditions. The simulation data are used to estimate the uncertainty when mean wake characteristics are quantified from scanning LiDAR measurements, which are temporally disjunct due to the time that the instrument takes to probe a large volume of air. Based on LES output, we determine that wind speeds sampled with the synthetic LiDAR are within 10% of the actual mean values and that the disjunct nature of the scan does not compromise the spatial variation of wind speeds within the planes. We propose scanning geometry density and coverage indices, which quantify the spatial distribution of the sampled points in the area of interest and are valuable to design LiDAR measurement campaigns for wake characterization. We find that scanning geometry coverage is important for estimates of the wake center, orientation and length scales, while density is more important when seeking to characterize the velocity deficit distribution. [ABSTRACT FROM AUTHOR]

Abnormality detection and prediction is a critical technique to identify wind turbine failures at an early stage, thus avoiding catastrophes. In this study, we propose a new abnormality detection and prediction technique based on heterogeneous signals and information, such as output power signals and wind turbines downtime event information collected from the supervisory control and data acquisition (SCADA) system. First, discriminant statistical feature extraction is performed on the power signals in both the time-domain and frequency-domain. Then, a sideband expression is derived for normalized statistical data based on quartiles. In addition, a dissimilarity metric is defined to calculate the distances between downtime time intervals, and a higher dimension feature space is obtained. To reduce the dimension of the feature space, the Laplacian Eigenmaps (LE) nonlinear dimensionality reduction method is implemented. Afterwards, a Linear Mixture Self-organizing Maps (LMSOM) classifier is applied to differentiate abnormal types and a cumulative trend difference method is utilized to predict the faults in wind turbine. The method is validated and applied to data collected from a wind farm in north China. The results show that the proposed technique can effectively detect and predict wind turbine abnormalities. [ABSTRACT FROM AUTHOR]