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Wind energy has gained tremendous attention recently due to its abundance potential and to obviate the adverse impacts of fossil fuel consumption. However, the efficient development and operation of wind energy systems require precise and accurate wind speed prediction. To fulfill this aim, machine learning algorithms have shown its prowess in accurately predicting the wind speed and this study proposes an interpretable stacked ensemble with ridge regressor utilizing ML algorithms such as catboost (CB), gradient boost (GB), multilayer perceptron (MLP), and random forest (RF) as a base model. The base models' estimations are integrated by applying a ridge regressor to enhance the accuracy of wind speed estimation. The dataset consists of 87 600 samples from which 80% samples are used for training. The proposed approach provides interpretability of the prediction results through Shapley additive explanations and feature importance analysis. The result of the proposed method is calculated by comparing the estimation outcomes with each model such as CB, GB, MLP, and RF in terms of error metrics such as root mean squared error (RMSE) and mean absolute error (MAE). The proposed model outperforms the others due to a reduction of error in RMSE by 19.89% and MAE by 24% respectively. [ABSTRACT FROM AUTHOR]

For large offshore wind turbines, pitch control is usually used for regulating generated power to the rated value and for mitigating the dynamic loads that at the wind speeds above the rated speeds. However, tracking the pitch angle accurately and quickly can hardly be realised due to complex operating environments, uncertain system parameters, various disturbances, and coupled effects between wind, wave, and turbine structure. In this study, an individual pitch control system based on a neural adaptive strategy is proposed to address the problems related to uncertain system parameters and various disturbances. The proposed control method can achieve zero error tracking for the pitch angle in a predefined finite time. The design and stability analysis for the proposed method is elaborated. A simulation model is established in Matlab/Simulink, and by comparing it with the traditional proportional–integral–derivative control method, the merit of the proposed control scheme is verified. [ABSTRACT FROM AUTHOR]

A multilayer frequency adaptive fundamental signal extractor‐based filter is used for the improvement of power quality of a single phase utility grid interfaced to a wind–photovoltaic (PV) system. This filter is used for the grid synchronisation, serving multiple objectives such as mitigating the lower‐order harmonics, DC offset elimination, load reactive power compensation, and power flow management. As this filtering technique is based on frequency adaptation, it adapts the changes in the grid fundamental frequency. The effectiveness of this filter is studied with simulations using MATLAB Simulink under varying solar irradiance, wind speeds, and loads also validated on the test setup developed in the laboratory. [ABSTRACT FROM AUTHOR]

This study proposes a robust sub‐synchronous damping controller based on fractional‐order sliding mode control (FOSMC) method to mitigate sub‐synchronous control interaction (SSCI) induced by DFIG‐based wind park connected to series‐compensated transmissions. Firstly, the nonlinearity of DFIG is cancelled through feedback linearisation. Secondly, FOSMC is designed and applied to the control loop of the DFIG rotor‐side converter. During the design process, the extra degree of freedom, provided by the fractional operator, is employed to achieve fast SSCI‐damping. Thirdly, a genetic algorithm is used for parameter optimisation with the aim of making system eigenvalues approach the left plane. The effectiveness of the proposed damping controller is evaluated based on the adapted IEEE first benchmark model, non‐aggregated wind system model with a realistic configuration, and experimental platform. Simulation and experiment results demonstrate the superior damping performance of FOSMC under different wind speeds, compensation levels, and the number of online wind turbines. Moreover, FOSMC also shows the robustness under parameter uncertainty, control parameter perturbation, and symmetric (three‐phase short circuit) fault. [ABSTRACT FROM AUTHOR]

This paper provides an analytical assessment of the feasibility of wind energy for Saudi Arabia's envisioned NEOM city, which plans to use only renewable energy. A probability density function was fit to winds simulated for the NEOM region during 2014‐2018. Using this distribution, the optimal wind turbine was selected as the one with the largest capacity factor and smallest levelized cost of energy (LCOE). Financial, environmental, and risk analysis of a wind farm consisting of 100 of these units was also performed. A Weibull distribution determined by changing the shape and scale parameters to minimize the mean squared error offered the best fit to the measured wind speeds at this location. The estimated power density showed that the NEOM site warrants a Class 3 classification, which means wind energy systems would be suitable for commercial operation. A 3.2‐MW wind turbine, which is optimal for this location, has a capacity factor varying from 31.9% to 41.4% and LCOE that ranges from 6.99¢ to 8.32¢ per kWh. The 320‐MW wind farm has a positive net present value, a simple payback period of 13.8 years, and a LCOE of 6.6¢ per kWh. By installing this farm, potential annual savings of CO2 are around 106 metric tons. [ABSTRACT FROM AUTHOR]

This study modifies the IEEE first benchmark model (FBM) by connecting nearby doubly‐fed induction generator (DFIG) wind farms to utilize the inbuilt convertor controller for damping the sub‐synchronous resonance (SSR) oscillations. The overall model was evaluated to determine the induction generator effect (IGE) and torsional interaction (TI) of sub‐synchronous resonance (SSR) at various compensation levels, wind speeds, and system parameter settings both with and without a DFIG wind farm. The results of this detailed analysis demonstrated that augmenting an IEEE FBM with a DFIG wind farm suppresses SSR‐induced IGE in the power system. An optimal orientation was investigated from rotor‐side converter (RSC) and grid‐side converter (GSC) of DFIG wind farm using residue configuration to design a SSR supplementary damping controller (SSDC). To suppress the TI effect of SSR, the SSDC implements damping control using three different feedback signals: line current, line power, and voltage across a series capacitor. A robust signal for achieving smooth damping under various operating conditions was defined in terms of several key characteristics, namely, high average residue magnitude, average phase angle of residue and avoidance of destabilisation of other dominant modes. The proposed model was validated using eigenvalue analysis, participation factor calculation and time‐domain simulation. [ABSTRACT FROM AUTHOR]

A data‐based estimation of the wind–power curve in wind turbines may be a challenging task due to the presence of anomalous data, possibly due to wrong sensor reads, operation halts, malfunctions or other. In this study, the authors describe a data‐based procedure to build a robust and accurate estimate of the wind–power curve. In particular, they combine a joint clustering procedure, where both the wind speeds and the power data are clustered, with an Earth Mover Distance‐based Extreme Learning Machine algorithm to filter out data that poorly contribute to explain the unknown curve. After estimating the cut‐in and the rated speed, they use a radial basis function neural network to fit the filtered data and obtain the curve estimate. They extensively compared the proposed procedure against other conventional methodologies over measured data of nine turbines, to assess and discuss its performance. [ABSTRACT FROM AUTHOR]

Series capacitor compensation technology has been widely used in long‐distance wind power transmission, which can improve the power transmission capacity and enhance the static stability of the power system. However, adding a series capacitor in the line may present a potential risk of subsynchronous resonance (SSR) to the system. This study focuses on the SSR caused by induction generator effect of the doubly‐fed induction generator‐based wind farms. The SSR phenomenon of wind power system is researched by the impedance analysis method. A mitigation strategy of adding a virtual inductance based on integral control is proposed for enhancing the system stability and alleviating the SSR. It has clear and intuitive physical concept, which is beneficial to system integration and provides a simpler and more economic method for mitigating SSR. Moreover, the principle and process of controller design are elaborated. Moreover, the time‐domain simulation is carried out to prove the effectiveness of the proposed control strategy under different operating conditions considering various compensation levels and wind speeds. Finally, the suppression effects of the virtual inductance in rotor‐side converter and grid‐side converter are compared. [ABSTRACT FROM AUTHOR]

Abstract: We present wind speeds at the ~ 1 bar level at both Jovian polar regions inferred from the 5‐μm infrared images acquired by the Jupiter InfraRed Auroral Mapper (JIRAM) instrument on the National Aeronautics and Space Administration Juno spacecraft during its fourth periapsis (2 February 2017). We adopted the criterion of minimum mean absolute distortion (Gonzalez & Woods, 2008) to quantify the motion of cloud features between pairs of images. The associated random error on speed estimates is 12 m/s in the northern polar region and 9.8 m/s at the south. Assuming that polar cyclones described by Adriani et al. (2018, https://doi.org/10.1038/nature25491) are in rigid motion with respect to System III, tangential speeds in the interior of the vortices increase linearly with distance from the center. The annulus of maximum speed for the main circumpolar cyclones is located at approximatively 1,000 km from their centers, with peak cyclonic speeds typically between 80 and 110 m/s and ~50 m/s in at least two cases. Beyond the annulus of maximum speed, tangential speed decreases inversely with the distance from the center within the Southern Polar Cyclone and somewhat faster within the Northern Polar Cyclone. A few small areas of anticyclonic motions are also identified within both polar regions. [ABSTRACT FROM AUTHOR]

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In this study, the probabilistic collocation method (PCM) is proposed to construct a stochastic correlation model of wind speeds at neighbouring wind farms and solve probabilistic power flow (PPF) of South Australia (SA) grid. Based on the historical sampled wind source data, the model is developed to reduce the number of uncertain parameters of the power system model by considering the spatial correlation of wind speeds between neighbouring wind farms. Furthermore, this model aims to increase the computational efficiency of PCM when dealing with PPF simulation. Finally, the computation efficiency and accuracy of the PCM, compared with traditional Monte Carlo simulation method, are validated by the simulation results of aggregated power flow model of SA case studies. [ABSTRACT FROM AUTHOR]

This study presents the impact on power system frequency control in small power systems based on different generator topologies with a large penetration (50%) of variable speed wind turbines. The impact of a proposed controller is investigated versus various wind speeds. In particular, wind speeds with an average wind speed just below rated wind speed proves to cause the worst frequency fluctuations regardless of the type of backup generation topology investigated during 50% wind‐penetration ratio. For this wind‐speed session and a hydro‐based system, the proposed control system improves the frequency duration outside of the specified range [49.9, 50.1]Hz from 81 to 53% while reducing its delivered energy by only 6%. Furthermore, the proposed control reduces the absolute value of requested reserve by 49% from the hydro unit responsible for primary frequency control. [ABSTRACT FROM AUTHOR]

Wind energy systems with permanent magnet generators, especially axial flux permanent magnet generators (AFPMG), have been quite popularly studied and employed recently. However, a known shortcoming of such systems is that they can operate efficiently only in a very limited range of wind speeds, which corresponds to about 70 to 110% of the rated speed of the generator employed and the energy available outside of this wind speed bracket is not accessed at all, and in fact, wasted. Moreover, the absence of any means of excitation control to maintain a regulated output voltage under varying speed conditions calls for expensive power converters. This study presents a novel methodology to obtain a constant output voltage from an AFPMG under varying wind conditions, through the integration of field poles and their control, along with modifications in stator winding. This significantly enhances the power extraction capability of the system over a much wider operational range (corresponding to 25–125% of the rated speed) while eliminating the requirement of a DC–DC converter to maintain constant output voltage. The details of the design of the constant voltage AFPMG and the results establishing the effectiveness of the methodology have also been presented. [ABSTRACT FROM AUTHOR]

The transient stability analysis incorporating wind power intermittency and volatility is studied in this study. The wind turbines with doubly fed induction generator (DFIG) and direct‐driven permanent magnet synchronous generator (PMSG) are considered, respectively, during analysis. In modelling of DFIG and PMSG, the default GE wind turbine model and the Western Electricity Coordinating Council generic wind turbine model are employed, respectively. Based on the Jensen model, which is applied to describe the wake effect in a wind farm located on flat terrain, an equivalent simplified model of a wind farm is established regarding wind farm layout. On the basis of the built wind farm model, the Monte Carlo simulation technique combined with two evaluation indices, namely angle‐based margin index and critical clearing time (CCT) is applied on the IEEE 10‐generator‐39‐bus test system and a real‐sized China‐Jiangxi power grid as a benchmark to exploit and explore the effects of wind power intermittency and volatility on power system transient stability. The frequency distributions of transient stability evaluation index, CCT, probability of system transient instability and the range of wind speeds causing system transient instability are simulated. In addition, the influence of wake effect on transient stability is discussed as well. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the relationship between return levels of a process and the strength of serial correlation present in the extremes of that process. Estimates of long period return levels are often used as design requirements, and peaks over thresholds analyses have, in the past, been used to obtain such estimates. However, analyses based on such declustering schemes are extremely wasteful of data, often resulting in great estimation uncertainty represented by very wide confidence intervals. Using simulated data, we show that-provided the extremal index is estimated appropriately-using all threshold excesses can give more accurate and precise estimates of return levels, allowing us to avoid altogether the sometimes arbitrary process of cluster identification. We then apply our method to two data examples concerning sea-surge and wind-speed extremes. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Understanding and quantifying the behaviour of extreme wind speeds has important applications for design in civil engineering. As in the extremal analysis of any environmental process, estimates are often required of the probability of events that are rarer than those already recorded. Consequently, research has focused on the development of techniques that make optimal use of the available data. One such approach lies in threshold methods, which, unlike the more traditional annual maxima approach to the modelling of extremes, takes into consideration all extreme events, extreme in the sense that they exceed some high threshold. However, the implications of using all extremes in an analysis include problems of temporal dependence and non-stationarity. Several pragmatic ways of circumventing the problem of temporal dependence have been developed, though these often include the deletion of many extreme observations, for example, filter out a set of independent extremes. This paper looks at another approach to inference—one which explicitly models the temporal dependence of the process and so can use information on all extremes—and investigates the appropriateness of assumptions of short-term temporal dependence for wind speeds. We also examine the success of such methods at estimating some extreme events commonly studied for wind-speed data. Throughout this paper extreme wind speeds are analysed within a Bayesian framework, which can be argued to be particularly advantageous for extreme value analyses. For example, the objective of an extreme value analysis is usually an estimate of the probability of future events reaching extreme levels—something which is handled quite naturally in a Bayesian analysis through predictive distributions. [ABSTRACT FROM AUTHOR]