Статті в журналах: "Wind farm estimation"

1

Celeska, Maja. "EQUIVALENT WIND FARM POWER CURVE ESTIMATION." Journal of Electrical Engineering and Information Technologies 2, no. 2 (2017): 105–11. http://dx.doi.org/10.51466/jeeit172105c.

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2

Celeska, Maja. "EQUIVALENT WIND FARM POWER CURVE ESTIMATION." Journal of Electrical Engineering and Information Technologies 2, no. 2 (2017): 105–11. http://dx.doi.org/10.51466/jeeit172105c.

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3

Annoni, Jennifer, Christopher Bay, Kathryn Johnson, Emiliano Dall'Anese, Eliot Quon, Travis Kemper, and Paul Fleming. "Wind direction estimation using SCADA data with consensus-based optimization." Wind Energy Science 4, no. 2 (June 20, 2019): 355–68. http://dx.doi.org/10.5194/wes-4-355-2019.

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Abstract. Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. To enable cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving a more efficient wind farm is to develop algorithms that ensure reliable, robust, real-time, and efficient operation of wind turbines in a wind farm using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This article presents a framework for developing a cooperative wind farm that incorporates information from nearby turbines in real time to better align turbines in a wind farm. SCADA data from multiple turbines can be used to make better estimates of the local inflow conditions at each individual turbine. By incorporating measurements from multiple nearby turbines, a more reliable estimate of the wind direction can be obtained at an individual turbine. The consensus-based approach presented in this paper uses information from nearby turbines to estimate wind direction in an iterative way rather than aggregating all the data in a wind farm at once. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction. This estimated wind direction signal has implications for potentially decreasing dynamic yaw misalignment, decreasing the amount of time a turbine spends yawing due to a more reliable input to the yaw controller, increasing resiliency to faulty wind-vane measurements, and increasing the potential for wind farm control strategies such as wake steering.

4

ARINAGA, Shinji, Masaaki SHIBATA, Shigeto HIRAI, Toshiya NANAHARA, Takamitsu SATO, and Koji YAMAGUCHI. "Estimation of Fluctuating Output in Wind Farm." Proceedings of the JSME annual meeting 2004.3 (2004): 293–94. http://dx.doi.org/10.1299/jsmemecjo.2004.3.0_293.

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5

Becker, Marcus, Dries Allaerts, and Jan-Willem van Wingerden. "Ensemble-Based Flow Field Estimation Using the Dynamic Wind Farm Model FLORIDyn." Energies 15, no. 22 (November 16, 2022): 8589. http://dx.doi.org/10.3390/en15228589.

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Wind farm control methods allow for a more flexible use of wind power plants over the baseline operation. They can be used to increase the power generated, to track a reference power signal or to reduce structural loads on a farm-wide level. Model-based control strategies have the advantage that prior knowledge can be included, for instance by simulating the current flow field state into the near future to take adequate control actions. This state needs to describe the real system as accurately as possible. This paper discusses what state estimation methods are suitable for wind farm flow field estimation and how they can be applied to the dynamic engineering model FLORIDyn. In particular, we derive an Ensemble Kalman Filter framework which can identify heterogeneous and changing wind speeds and wind directions across a wind farm. It does so based on the power generated by the turbines and wind direction measurements at the turbine locations. Next to the states, this framework quantifies uncertainty for the resulting state estimates. We also highlight challenges that arise when ensemble methods are applied to particle-based flow field simulations. The development of a flow field estimation framework for dynamic low-fidelity wind farm models is an essential step toward real-time dynamic model-based closed-loop wind farm control.

6

Meglic, Antun, and Ranko Goic. "Impact of Time Resolution on Curtailment Losses in Hybrid Wind-Solar PV Plants." Energies 15, no. 16 (August 17, 2022): 5968. http://dx.doi.org/10.3390/en15165968.

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Curtailment losses for large-scale hybrid wind–solar photovoltaic (PV) plants with a single grid connection point are often calculated in 1 h time resolution, underestimating the actual curtailment losses due to the flattening of power peaks occurring in shorter time frames. This paper analyses the curtailment losses in hybrid wind–PV plants by utilising different time resolutions of wind and PV production while varying the grid cut-off power, wind/solar PV farm sizes, and shares of wind/PV capacity. Highly resolved 1 s measurements from the operational wind farm and pyranometer are used as an input to specialized wind and PV farm power production models that consider the smoothing effect. The results show that 15 min resolution is preferred over 1 h resolution for large-scale hybrid wind–PV plants if more accurate assessment of curtailment losses is required. Although 1 min resolution additionally increases the estimation accuracy over 15 min resolution, the improvement is not significant for wind and PV plants with capacity above approx. 10 MW/10 MWp. The resolutions shorter than 1 min do not additionally increase the estimation accuracy for large-scale wind and PV plants. More attention is required when estimating curtailment losses in wind/PV plants with capacity below approx. 10 MW/10 MWp, where higher underestimation can be expected if lower time resolutions are used.

7

TSUCHIYA, Manabu, Yukinari FUKUMOTO, and Takeshi ISHIHARA. "The Wind Observation and Energy Production Estimation for Offshore Wind Farm." Wind Engineers, JAWE 2008, no. 115 (2008): 119–22. http://dx.doi.org/10.5359/jawe.2008.119.

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8

S, Fredy H. Martínez, César A. Hernández S, and Fernando Martínez S. "Multivariate Wind Speed Forecasting with LSTMs for Wind Farm Performance Estimation." International Journal of Engineering and Technology 10, no. 6 (December 31, 2018): 1626–32. http://dx.doi.org/10.21817/ijet/2018/v10i6/181006025.

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9

Petkovic, Dalibor. "Estimation of wind farm efficiency by ANFIS strategy." Godisnjak Pedagoskog fakulteta u Vranju, no. 7 (2016): 91–105. http://dx.doi.org/10.5937/gufv1607091p.

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10

Farrell, W., T. Herges, D. Maniaci, and K. Brown. "Wake state estimation of downwind turbines using recurrent neural networks for inverse dynamics modelling." Journal of Physics: Conference Series 2265, no. 3 (May 1, 2022): 032094. http://dx.doi.org/10.1088/1742-6596/2265/3/032094.

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Abstract Presented in this work is a novel approach to estimate absolute lateral wake center position on the rotor plane of a waked turbine using turbine load and operating state information. The approach formulates the estimation of the absolute lateral wake position as an inverse dynamics problem and utilizes a recurrent neural network to model the inverse mapping between the wake center position and select turbine output channels. The technique is validated on experimental data collected from experiments at the Scaled Wind Farm Technology (SWiFT) facility and numerical simulations of the site in the wind farm simulator FAST.Farm. Estimator performance and analysis of optimal conditions for estimation are discussed.

11

Grande, Olatz, Josune Cañizo, Itziar Angulo, David Jenn, Laith R. Danoon, David Guerra, and David de la Vega. "Simplified Formulae for the Estimation of Offshore Wind Turbines Clutter on Marine Radars." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/982508.

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The potential impact that offshore wind farms may cause on nearby marine radars should be considered before the wind farm is installed. Strong radar echoes from the turbines may degrade radars’ detection capability in the area around the wind farm. Although conventional computational methods provide accurate results of scattering by wind turbines, they are not directly implementable in software tools that can be used to conduct the impact studies. This paper proposes a simple model to assess the clutter that wind turbines may generate on marine radars. This method can be easily implemented in the system modeling software tools for the impact analysis of a wind farm in a real scenario.

12

Maheshwari, Priyank, Julien Haize, and Maxime Pallud. "Modeling of Blockage and Wake Effect: Comparison with Field data." Journal of Physics: Conference Series 2767, no. 9 (June 1, 2024): 092021. http://dx.doi.org/10.1088/1742-6596/2767/9/092021.

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Abstract We apply engineering approaches for estimating the induction zone in front of a wind turbine and escalating to the global blockage at wind farm and cluster of farms scale as well accounting for wind farm wake. The methods include the vortex cylinder model and the self-similar model for blockage, and Jensen and Eddy-viscosity models for the wake estimation. To calibrate each model, Reynold Average Navier-Stokes with Actuator disc simulations are employed. Every model’s efficacy is assessed in comparison to the validation data of an operated wind farm in Scotland with more than 100 turbines of 10 MW each. We also examine the widely used process within the wind industry for transferring the application of calibrated wake and blockage models from one wind farm. According to our simulation analysis, blockage causes sideways acceleration and upstream slowdown. This also result in a gradient in the power produced by each turbine on the farm, which, if not properly accounted for, appears to be a wake model constraint. Furthermore, the response of different turbine type in another environment is not effectively predicted by the blockage and wake model, which is based on a particular turbine type and environment.

13

Doekemeijer, Bart M., Sjoerd Boersma, Lucy Y. Pao, Torben Knudsen, and Jan-Willem van Wingerden. "Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control." Wind Energy Science 3, no. 2 (October 24, 2018): 749–65. http://dx.doi.org/10.5194/wes-3-749-2018.

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Abstract. Wind farm control often relies on computationally inexpensive surrogate models to predict the dynamics inside a farm. However, the reliability of these models over the spectrum of wind farm operation remains questionable due to the many uncertainties in the atmospheric conditions and tough-to-model dynamics at a range of spatial and temporal scales relevant for control. A closed-loop control framework is proposed in which a simplified model is calibrated and used for optimization in real time. This paper presents a joint state-parameter estimation solution with an ensemble Kalman filter at its core, which calibrates the surrogate model to the actual atmospheric conditions. The estimator is tested in high-fidelity simulations of a nine-turbine wind farm. Exclusively using measurements of each turbine's generated power, the adaptability to modeling errors and mismatches in atmospheric conditions is shown. Convergence is reached within 400 s of operation, after which the estimation error in flow fields is negligible. At a low computational cost of 1.2 s on an 8-core CPU, this algorithm shows comparable accuracy to the state of the art from the literature while being approximately 2 orders of magnitude faster.

14

Bingöl, Ferhat. "Comparison of Weibull Estimation Methods for Diverse Winds." Advances in Meteorology 2020 (July 6, 2020): 1–11. http://dx.doi.org/10.1155/2020/3638423.

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Wind farm siting relies on in situ measurements and statistical analysis of the wind distribution. The current statistical methods include distribution functions. The one that is known to provide the best fit to the nature of the wind is the Weibull distribution function. It is relatively straightforward to parameterize wind resources with the Weibull function if the distribution fits what the function represents but the estimation process gets complicated if the distribution of the wind is diverse in terms of speed and direction. In this study, data from a 101 m meteorological mast were used to test several estimation methods. The available data display seasonal variations, with low wind speeds in different seasons and effects of a moderately complex surrounding. The results show that the maximum likelihood method is much more successful than industry standard WAsP method when the diverse winds with high percentile of low wind speed occur.

15

Feijóo, Andrés, and Daniel Villanueva. "Contributions to wind farm power estimation considering wind direction-dependent wake effects." Wind Energy 20, no. 2 (June 30, 2016): 221–31. http://dx.doi.org/10.1002/we.2002.

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16

Marti-Puig, Pere, Jose Ángel Hernández, Jordi Solé-Casals, and Moises Serra-Serra. "Enhancing Reliability in Wind Turbine Power Curve Estimation." Applied Sciences 14, no. 6 (March 15, 2024): 2479. http://dx.doi.org/10.3390/app14062479.

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Accurate power curve modeling is essential to continuously evaluate the performance of a wind turbine (WT). In this work, we characterize the wind power curves using SCADA data acquired at a frequency of 5 min in a wind farm (WF) consisting of five WTs. Regarding the non-parametric methods, we select artificial neural networks (ANNs) to make curve estimations. Given that, we have the curves provided by the manufacturer of the WTs given by some very precisely measured pair of wind speed and power points. We can evaluate the difference between the manufacturer characterization and the ones estimated with the data provided by the SCADA system. Before the estimation, we propose a method of filtering the anomalies based on the characteristics provided by the manufacturer. We use three-quarters of the available data for curve estimation and one-quarter for the test. One WT suffered a break in the test part, so we can check how the test estimates reflect this problem in its wind-power curve compared to the estimations obtained in the WTs that worked adequately.

17

Mirzaei, Mahmood, Tuhfe Göçmen, Gregor Giebel, Poul Ejnar Sørensen, and Niels K. Poulsen. "Estimation of the Possible Power of a Wind Farm." IFAC Proceedings Volumes 47, no. 3 (2014): 6782–87. http://dx.doi.org/10.3182/20140824-6-za-1003.02253.

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18

Badger, Jake, Helmut Frank, Andrea N. Hahmann, and Gregor Giebel. "Wind-Climate Estimation Based on Mesoscale and Microscale Modeling: Statistical–Dynamical Downscaling for Wind Energy Applications." Journal of Applied Meteorology and Climatology 53, no. 8 (August 2014): 1901–19. http://dx.doi.org/10.1175/jamc-d-13-0147.1.

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AbstractThis paper demonstrates that a statistical–dynamical method can be used to accurately estimate the wind climate at a wind farm site. In particular, postprocessing of mesoscale model output allows an efficient calculation of the local wind climate required for wind resource estimation at a wind turbine site. The method is divided into two parts: 1) preprocessing, in which the configurations for the mesoscale model simulations are determined, and 2) postprocessing, in which the data from the mesoscale simulations are prepared for wind energy application. Results from idealized mesoscale modeling experiments for a challenging wind farm site in northern Spain are presented to support the preprocessing method. Comparisons of modeling results with measurements from the same wind farm site are presented to support the postprocessing method. The crucial element in postprocessing is the bridging of mesoscale modeling data to microscale modeling input data, via a so-called generalization method. With this method, very high-resolution wind resource mapping can be achieved.

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Kartal, Serkan, Sukanta Basu, and Simon J. Watson. "A decision-tree-based measure–correlate–predict approach for peak wind gust estimation from a global reanalysis dataset." Wind Energy Science 8, no. 10 (October 16, 2023): 1533–51. http://dx.doi.org/10.5194/wes-8-1533-2023.

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Abstract. Peak wind gust (Wp) is a crucial meteorological variable for wind farm planning and operations. However, for many wind farm sites, there is a dearth of on-site measurements of Wp. In this paper, we propose a machine-learning approach (called INTRIGUE, decIsioN-TRee-based wInd GUst Estimation) that utilizes numerous inputs from a public-domain reanalysis dataset and, in turn, generates multi-year, site-specific Wp series. Through a systematic feature importance study, we also identify the most relevant meteorological variables for Wp estimation. The INTRIGUE approach outperforms the baseline predictions for all wind gust conditions. However, the performance of this proposed approach and the baselines for extreme conditions (i.e., Wp>20 m s−1) is less satisfactory.

20

Velázquez Medina, Sergio, José A. Carta, and Ulises Portero Ajenjo. "Performance Sensitivity of a Wind Farm Power Curve Model to Different Signals of the Input Layer of ANNs: Case Studies in the Canary Islands." Complexity 2019 (March 26, 2019): 1–11. http://dx.doi.org/10.1155/2019/2869149.

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Improving the estimation of the power output of a wind farm enables greater integration of this type of energy source in electrical systems. The development of accurate models that represent the real operation of a wind farm is one way to attain this objective. A wind farm power curve model is proposed in this paper which is developed using artificial neural networks, and a study is undertaken of the influence on model performance when parameters such as the meteorological conditions (wind speed and direction) of areas other than the wind farm location are added as signals of the input layer of the neural network. Using such information could be of interest, either to study possible improvements that could be obtained in the performance of the original model, which uses exclusively the meteorological conditions of the area where the wind farm is located, or simply because no reliable meteorological data for the area of the wind farm are available. In the study developed it is deduced that the incorporation of meteorological data from an additional weather station other than that of the wind farm site can improve by up to 17.6% the performance of the original model.

21

Rajeevan, A. K., P. V. Shouri, and Usha Nair. "ARIMA Based Wind Speed Modeling for Wind Farm Reliability Analysis and Cost Estimation." Journal of Electrical Engineering and Technology 11, no. 4 (July 1, 2016): 869–77. http://dx.doi.org/10.5370/jeet.2016.11.4.869.

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22

Liu, Li Yang, Jun Ji Wu, and Shao Liang Meng. "The Application of Wind Speed Numerical Simulation in Wind Power Generation." Applied Mechanics and Materials 380-384 (August 2013): 3370–73. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3370.

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With the massive development and application of wind energy, wind power is having an increasing proportion in power grid. The changes of the wind speed in a wind farm will lead to fluctuations in the power output which would affect the stable operation of the power grid. Therefore the research of the characteristics of wind speed has become a hot topic in the field of wind energy. In the paper, the wind speed at the wind farm was simulated in a combination of wind speeds by which wind speed was decomposed of four components including basic wind, gust wind, stochastic wind and gradient wind which denote the regularity, the mutability, the gradual change and the randomness of a natural wind respectively. The model is able to reflect the characteristics of a real wind, easy for engineering simulation and can also estimate the wind energy of a wind farm through the wind speed and wake effect model. This paper has directive significance in the estimation of wind resource and the layout of wind turbines in wind farms.

23

Paik, Chunhyun, Yongjoo Chung, and Young Jin Kim. "Power Curve Modeling of Wind Turbines through Clustering-Based Outlier Elimination." Applied System Innovation 6, no. 2 (March 15, 2023): 41. http://dx.doi.org/10.3390/asi6020041.

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The estimation of power curve is the central task for efficient operation and prediction of wind power generation. It is often the case, however, that the actual data exhibit a great deal of variations in power output with respect to wind speed, and thus the power curve estimation necessitates the detection and proper treatment of outliers. This study proposes a novel procedure for outlier detection and elimination for estimating power curves of wind farms by employing clustering algorithms of vector quantization and density-based spatial clustering of applications with noise. Testing different parametric models of power output curve, the proposed methodology is demonstrated for obtaining power curves of individual wind turbines in a Korean wind farm. It is asserted that the outlier elimination procedure for power curve modeling outlined in this study can be highly efficient at the presence of noises.

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Kelly, Mark, and Hans E. Jørgensen. "Statistical characterization of roughness uncertainty and impact on wind resource estimation." Wind Energy Science 2, no. 1 (April 25, 2017): 189–209. http://dx.doi.org/10.5194/wes-2-189-2017.

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Abstract. In this work we relate uncertainty in background roughness length (z0) to uncertainty in wind speeds, where the latter are predicted at a wind farm location based on wind statistics observed at a different site. Sensitivity of predicted winds to roughness is derived analytically for the industry-standard European Wind Atlas method, which is based on the geostrophic drag law. We statistically consider roughness and its corresponding uncertainty, in terms of both z0 derived from measured wind speeds as well as that chosen in practice by wind engineers. We show the combined effect of roughness uncertainty arising from differing wind-observation and turbine-prediction sites; this is done for the case of roughness bias as well as for the general case. For estimation of uncertainty in annual energy production (AEP), we also develop a generalized analytical turbine power curve, from which we derive a relation between mean wind speed and AEP. Following our developments, we provide guidance on approximate roughness uncertainty magnitudes to be expected in industry practice, and we also find that sites with larger background roughness incur relatively larger uncertainties.

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Jiao, Chunlei, Zhao Pu zhi, Hou bing, Wang Zhen, and Cai Yongjun. "Wind Farm Harmonic Impedance Estimation Based on Stochastic Subspace Method." IOP Conference Series: Earth and Environmental Science 371 (December 13, 2019): 022052. http://dx.doi.org/10.1088/1755-1315/371/2/022052.

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Lingad, M. V., M. Rodrigues, S. Leonardi, and A. Zare. "Three-dimensional stochastic dynamical modeling for wind farm flow estimation." Journal of Physics: Conference Series 2767, no. 5 (June 1, 2024): 052065. http://dx.doi.org/10.1088/1742-6596/2767/5/052065.

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Abstract Modifying turbine blade pitch, generator torque, and nacelle direction (yaw) are conventional approaches for enhancing energy output and alleviating structural loads. However, the efficacy of such methods is challenged by the lag in adjusting such settings after atmospheric variations are detected. Without reliable short-term wind forecasting tools, current practice, which mostly relies on data collected at or just behind turbines, can result in sub-optimal performance. Data-assimilation strategies can achieve real-time wind forecasting capabilities by correcting model-based predictions of the incoming wind using various field measurements. In this paper, we revisit the development of a class of prior models for real-time estimation via Kalman filtering algorithms that track atmospheric variations using ground-level pressure sensors. This class of models is given by the stochastically forced linearized Navier-Stokes equations around the three-dimensional waked velocity profile defined by a curled wake model. The stochastic input to these models is devised using convex optimization to achieve statistical consistency with high-fidelity large-eddy simulations. We demonstrate the ability of such models in reproducing the second-order statistical signatures of the turbulent velocity field. In support of assimilating ground-level pressure measurements with the predictions of said models, we also highlight the significance of the wall-normal dimension in enhancing two-point correlations of the pressure field between the ground and the computational domain.

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Christodoulou, Christos A., Vasiliki Vita, George-Calin Seritan, and Lambros Ekonomou. "A Harmony Search Method for the Estimation of the Optimum Number of Wind Turbines in a Wind Farm." Energies 13, no. 11 (June 1, 2020): 2777. http://dx.doi.org/10.3390/en13112777.

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During the last decades, renewable energy production has significantly increased in an effort to produce clean energy that will not affect the environment. Governments around the world are focusing on reducing greenhouse gas emissions by increasing the utilization of renewable energy sources in the power chain. Wind farms and wind generators are the main renewable technology that are used worldwide. The main scope of wind farm designers is the achievement of the maximum possible power, restraining the installation cost that is related to the use of a specific number of wind turbines for specific power production, and considering the area of land to be occupied. A harmony search method is presented in this paper for the determination of the optimum number of wind turbines in a wind farm and the total electric power produced. The method is applied for comparison purposes on data from previously published methodologies proving its accuracy and effectiveness. The harmony research method can be used in the studies of wind farm designers aiming to reduce installation costs.

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Boersma, Sjoerd, Bart Doekemeijer, Mehdi Vali, Johan Meyers, and Jan-Willem van Wingerden. "A control-oriented dynamic wind farm model: WFSim." Wind Energy Science 3, no. 1 (March 6, 2018): 75–95. http://dx.doi.org/10.5194/wes-3-75-2018.

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Abstract. Wind turbines are often sited together in wind farms as it is economically advantageous. Controlling the flow within wind farms to reduce the fatigue loads, maximize energy production and provide ancillary services is a challenging control problem due to the underlying time-varying non-linear wake dynamics. In this paper, we present a control-oriented dynamical wind farm model called the WindFarmSimulator (WFSim) that can be used in closed-loop wind farm control algorithms. The three-dimensional Navier–Stokes equations were the starting point for deriving the control-oriented dynamic wind farm model. Then, in order to reduce computational complexity, terms involving the vertical dimension were either neglected or estimated in order to partially compensate for neglecting the vertical dimension. Sparsity of and structure in the system matrices make this model relatively computationally inexpensive. We showed that by taking the vertical dimension partially into account, the estimation of flow data generated with a high-fidelity wind farm model is improved relative to when the vertical dimension is completely neglected in WFSim. Moreover, we showed that, for the study cases considered in this work, WFSim is potentially fast enough to be used in an online closed-loop control framework including model parameter updates. Finally we showed that the proposed wind farm model is able to estimate flow and power signals generated by two different 3-D high-fidelity wind farm models.

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Luo, Yilan, Deniz Sezer, David Wood, Mingkuan Wu, and Hamid Zareipour. "Estimation of the Daily Variability of Aggregate Wind Power Generation in Alberta, Canada." Energies 12, no. 10 (May 24, 2019): 1998. http://dx.doi.org/10.3390/en12101998.

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This paper describes a hierarchy of increasingly complex statistical models for wind power generation in Alberta applied to wind power production data that are publicly available. The models are based on combining spatial and temporal correlations. We apply the method of Gaussian random fields to analyze the wind power time series of the 19 existing wind farms in Alberta. Following the work of Gneiting et al., three space-time models are used: Stationary, Separability, and Full Symmetry. We build several spatio-temporal covariance function estimates with increasing complexity: separable, non-separable and symmetric, and non-separable and non-symmetric. We compare the performance of the models using kriging predictions and prediction intervals for both the existing wind farms and a new farm in Alberta. It is shown that the spatial correlation in the models captures the predominantly westerly prevailing wind direction. We use the selected model to forecast the mean and the standard deviation of the future aggregate wind power generation of Alberta and investigate new wind farm siting on the basis of reducing aggregate variability.

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De Blasis, Riccardo, Giovanni Batista Masala, and Filippo Petroni. "A Multivariate High-Order Markov Model for the Income Estimation of a Wind Farm." Energies 14, no. 2 (January 12, 2021): 388. http://dx.doi.org/10.3390/en14020388.

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The energy produced by a wind farm in a given location and its associated income depends both on the wind characteristics in that location—i.e., speed and direction—and the dynamics of the electricity spot price. Because of the evidence of cross-correlations between wind speed, direction and price series and their lagged series, we aim to assess the income of a hypothetical wind farm located in central Italy when all interactions are considered. To model these cross and auto-correlations efficiently, we apply a high-order multivariate Markov model which includes dependencies from each time series and from a certain level of past values. Besides this, we used the Raftery Mixture Transition Distribution model (MTD) to reduce the number of parameters to get a more parsimonious model. Using data from the MERRA-2 project and from the electricity market in Italy, we estimate the model parameters and validate them through a Monte Carlo simulation. The results show that the simulated income faithfully reproduces the empirical income and that the multivariate model also closely reproduces the cross-correlations between the variables. Therefore, the model can be used to predict the income generated by a wind farm.

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Sales-Setién, Ester, and Ignacio Peñarrocha-Alós. "Robust estimation and diagnosis of wind turbine pitch misalignments at a wind farm level." Renewable Energy 146 (February 2020): 1746–65. http://dx.doi.org/10.1016/j.renene.2019.07.133.

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32

Blondel, Frédéric. "Brief communication: A momentum-conserving superposition method applied to the super-Gaussian wind turbine wake model." Wind Energy Science 8, no. 2 (February 8, 2023): 141–47. http://dx.doi.org/10.5194/wes-8-141-2023.

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Abstract. Accurate wind farm flow predictions based on analytical wake models are crucial for wind farm design and layout optimization. In this regard, wake superposition methods play a key role and remain a substantial source of uncertainty. Recently, new models based on mass and momentum conservation have been proposed in the literature. In the present work, such methods are extended to the superposition of super-Gaussian-type velocity deficit models, allowing the full wake velocity deficit estimation and design of closely packed wind farms.

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LoCascio, Michael J., Christopher J. Bay, Majid Bastankhah, Garrett E. Barter, Paul A. Fleming, and Luis A. Martínez-Tossas. "FLOW Estimation and Rose Superposition (FLOWERS): an integral approach to engineering wake models." Wind Energy Science 7, no. 3 (June 1, 2022): 1137–51. http://dx.doi.org/10.5194/wes-7-1137-2022.

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Abstract. Annual energy production (AEP) is often the objective function in wind plant layout optimization studies. The conventional method to compute AEP for a wind farm is to first evaluate power production for each discrete wind direction and speed using either computational fluid dynamics simulations or engineering wake models. The AEP is then calculated by weighted-averaging (based on the wind rose at the wind farm site) the power produced across all wind directions and speeds. We propose a novel formulation for time-averaged wake velocity that incorporates an analytical integral of a wake deficit model across every wind direction. This approach computes the average flow field more efficiently, and layout optimization is an obvious application to exploit this benefit. The clear advantage of this new approach is that the layout optimization produces solutions with comparable AEP performance yet is completed 2 orders of magnitude faster. The analytical integral and the use of a Fourier expansion to express the wind speed and wind direction frequency create a relatively smooth solution space for the gradient-based optimizer to excel in comparison to the existing weighted-averaging power calculation.

34

Vollmer, Lukas, Gerald Steinfeld, and Martin Kühn. "Transient LES of an offshore wind turbine." Wind Energy Science 2, no. 2 (December 8, 2017): 603–14. http://dx.doi.org/10.5194/wes-2-603-2017.

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Abstract. The estimation of the cost of energy of offshore wind farms has a high uncertainty, which is partly due to the lacking accuracy of information on wind conditions and wake losses inside of the farm. Wake models that aim to reduce the uncertainty by modeling the wake interaction of turbines for various wind conditions need to be validated with measurement data before they can be considered as a reliable estimator. In this paper a methodology that enables a direct comparison of modeled with measured flow data is evaluated. To create the simulation data, a model chain including a mesoscale model, a large-eddy-simulation (LES) model and a wind turbine model is used. Different setups are compared to assess the capability of the method to reproduce the wind conditions at the hub height of current offshore wind turbines. The 2-day-long simulation of the ambient wind conditions and the wake simulation generally show good agreements with data from a met mast and lidar measurements, respectively. Wind fluctuations due to boundary layer turbulence and synoptic-scale motions are resolved with a lower representation of mesoscale fluctuations. Advanced metrics to describe the wake shape and development are derived from simulations and measurements but a quantitative comparison proves to be difficult due to the scarcity and the low sampling rate of the available measurement data. Due to the implementation of changing synoptic wind conditions in the LES, the methodology could also be beneficial for case studies of wind farm performance or wind farm control.

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Bhatt, Aditya H., Mireille Rodrigues, Federico Bernardoni, Stefano Leonardi, and Armin Zare. "Stochastic Dynamical Modeling of Wind Farm Turbulence." Energies 16, no. 19 (September 30, 2023): 6908. http://dx.doi.org/10.3390/en16196908.

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Low-fidelity engineering wake models are often combined with linear superposition laws to predict wake velocities across wind farms under steady atmospheric conditions. While convenient for wind farm planning and long-term performance evaluation, such models are unable to capture the time-varying nature of the waked velocity field, as they are agnostic to the complex aerodynamic interactions among wind turbines and the effects of atmospheric boundary layer turbulence. To account for such effects while remaining amenable to conventional system-theoretic tools for flow estimation and control, we propose a new class of data-enhanced physics-based models for the dynamics of wind farm flow fluctuations. Our approach relies on the predictive capability of the stochastically forced linearized Navier–Stokes equations around static base flow profiles provided by conventional engineering wake models. We identify the stochastic forcing into the linearized dynamics via convex optimization to ensure statistical consistency with higher-fidelity models or experimental measurements while preserving model parsimony. We demonstrate the utility of our approach in completing the statistical signature of wake turbulence in accordance with large-eddy simulations of turbulent flow over a cascade of yawed wind turbines. Our numerical experiments provide insight into the significance of spatially distributed field measurements in recovering the statistical signature of wind farm turbulence and training stochastic linear models for short-term wind forecasting.

36

Gudmestad, Ove Tobias, and Anja Schnepf. "Design Basis Considerations for the Design of Floating Offshore Wind Turbines." Sustainable Marine Structures 5, no. 2 (September 16, 2023): 26–34. http://dx.doi.org/10.36956/sms.v5i2.913.

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The wind farm owner/operator must prepare a Design Basis to facilitate the design of floating offshore wind turbines. The Design Basis is crucial to ensure that the individual elements of the wind farm are designed according to the relevant standards and the actual site conditions. In case of under-design, systematic failures can occur across the wind turbines, which can result in progressive damage to the turbines of the wind farm. This paper focuses on the safety and overall economics, including limiting potential excessive costs of heavy maintenance caused by damage due to under-design. Thus, this paper highlights critical aspects of particular importance to be implemented in the Design Basis document. Meeting all required constraints for developing offshore wind farms in deep water may result in higher costs than initially anticipated. Nonetheless, a realistic cost estimation for all phases of the project, engineering, construction, transport, and installation on site, remains essential for all engineering projects, including those involving renewable energy.

37

Li, Jing, Ya Di Luo, Yan Sheng Lang, Cheng Long Dou, Yu Zou, Zi Ming Guo, Dong Sheng Wang, and Xin Peng Li. "Research of Fine and Robust State Estimation." Advanced Materials Research 1008-1009 (August 2014): 202–6. http://dx.doi.org/10.4028/www.scientific.net/amr.1008-1009.202.

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According to the characteristics of the wind farm measuration, this paper presents a Fine and Robust State Estimation method for solving residual contamination problem caused by large-scale wind power integration. This method introduces the reference value of measurement type into the weight function and uses the pre-check information of state estimation measurement to do SCADA measurement pretreatment, and then form the bad data reference factor to assist positioning the bad data of measurement. Finally, the simulation tests of a regional power grid to prove that the proposed method can effectively identify telemetry bad data of wind farms eliminate residual pollution caused by it, which improve the accuracy of the State Estimation.

38

Gupta, Deepak, Vikas Kumar, Ishan Ayus, M. Vasudevan, and N. Natarajan. "Short-term prediction of wind power density using convolutional LSTM network." FME Transactions 49, no. 3 (2021): 653–63. http://dx.doi.org/10.5937/fme2103653g.

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Efficient extraction of renewable energy from wind depends on the reliable estimation of wind characteristics and optimization of wind farm installation and operation conditions. There exists uncertainty in the prediction of wind energy tapping potential based on the variability in wind behavior. Thus the estimation of wind power density based on empirical models demand subsequent data processing to ensure accuracy and reliability in energy computations. Present study analyses the reliability of the ANN-based machine learning approach in predicting wind power density for five stations (Chennai, Coimbatore, Madurai, Salem, and Tirunelveli) in the state of Tamil Nadu, India using five different non-linear models. The selected models such as Convolutional Neural Network (CNN), Dense Neural Network (DNN), Recurrent Neural Network (RNN), Bidirectional Long Short Term Memory (LSTM) Network, and linear regression are employed for comparing the data for a period from Jan 1980 to May 2018. Based on the results, it was found that the performance of (1->Conv1D|2->LSTM|1-dense) is better than the other models in estimating wind power density with minimum error values (based on mean absolute error and root mean squared error).

39

Cañadillas, Beatriz, Richard Foreman, Gerald Steinfeld, and Nick Robinson. "Cumulative Interactions between the Global Blockage and Wake Effects as Observed by an Engineering Model and Large-Eddy Simulations." Energies 16, no. 7 (March 23, 2023): 2949. http://dx.doi.org/10.3390/en16072949.

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By taking into account the turbine type, terrain, wind climate and layout, the effects of wind turbine wakes and other losses, engineering models enable the rapid estimation of energy yields for prospective and existing wind farms. We extend the capability of engineering models, such as the existing deep-array wake model, to account for additional losses that may arise due to the presence of clusters of wind farms, such as the global blockage effect and large-scale wake effects, which become more significant with increasing thermal stratification. The extended strategies include an enhanced wind-farm-roughness approach which assumes an infinite wind farm, and recent developments account for the upstream flow blockage. To test the plausibility of such models in capturing the additional blockage and wake losses in real wind farm clusters, the extended strategies are compared with large-eddy simulations of the flow through a cluster of three wind farms located in the German sector of the North Sea, as well as real measurements of wind power within these wind farms. Large-eddy simulations and wind farm measurements together suggest that the extensions of the Openwind model help capture the different flow features arising from flow blockage and cluster effects, but further model refinement is needed to account for higher-order effects, such as the effect of the boundary-layer height, which is not currently included in standard engineering models.

40

Roy, Asish, and Kalyan Chatterjee. "Availability estimation of a multi-state wind farm in fuzzy environment." International Journal of Green Energy 15, no. 2 (January 15, 2018): 80–95. http://dx.doi.org/10.1080/15435075.2018.1423977.

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41

Jiang, Wang, and Jiping Lu. "Frequency estimation in wind farm integrated systems using artificial neural network." International Journal of Electrical Power & Energy Systems 62 (November 2014): 72–79. http://dx.doi.org/10.1016/j.ijepes.2014.04.027.

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42

Hamilton, Nicholas, Dennice Gayme, and Raúl Bayoán Cal. "Wind plant controls." Journal of Renewable and Sustainable Energy 14, no. 6 (November 2022): 060401. http://dx.doi.org/10.1063/5.0133996.

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The development of operational strategies for wind farms as an integrated plant system to achieve a variety of goals from elevating power production to reducing maintenance needs has generated a great deal of interest in recent years. Achieving these operational goals requires an estimate of the energy available and the wind conditions affecting each turbine. The importance of the aerodynamic interaction of wind turbines with the dynamic atmospheric resource means that wakes (the momentum deficit due to power extraction) and their interactions through the farm have the largest influence on the available energy. Predicting the influence of wakes and their interactions, therefore, form the basis of wind farm control strategies to reduce power production losses, track a power signal, mitigate structural loading, or balance the wear and tear on wind turbines to decrease operation and maintenance costs. The articles in the “Advances in Wind Plant Controls: Strategies, Implementation, and Validation” Special Topic in the Journal of Renewable and Sustainable Energy describe the further development and evaluation of wake models and new approaches to wake steering that exploit advances in sensing or estimation to improve control performance.

43

Nikolić, Vlastimir, Shahaboddin Shamshirband, Dalibor Petković, Kasra Mohammadi, Žarko Ćojbašić, Torki A. Altameem, and Abdullah Gani. "Wind wake influence estimation on energy production of wind farm by adaptive neuro-fuzzy methodology." Energy 80 (February 2015): 361–72. http://dx.doi.org/10.1016/j.energy.2014.11.078.

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44

Busse, Przemysław. "Methodological Procedure For Pre-Investment Wind Farm Ornithological Monitoring Based On Collision Risk Estimation." Ring 35, no. 1 (March 12, 2014): 3–30. http://dx.doi.org/10.2478/ring-2013-0001.

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ABSTRACT Even though the proportion of wind farm victims compared to general bird species mortality is relatively low, there is necessity to limit direct and indirect losses to the bird populations caused by this kind of human activity. Estimation of threats to the birds resulting from building of wind farms is a very difficult task and it must take into account several constrains. The basic task is to build farms in localities that are the safest to birds. This can be achieved by pre-investment monitoring and direct observations at the spot and then evaluation of potential threats and risks. Field methods typical for the studies on bird populations are usually applied in such monitoring. The procedure described below includes four steps: screening (starts the process and sets preliminary constrains of the location), monitoring (standardised data are collected at the location), estimations of potential collision risk and evaluation of the location. The key parameters determining collision risk of bird species are: (1) the number of individuals utilising the monitored area in different seasons, (2) air space utilization (height and directions of flights), as well as (3) characteristics of the species behaviour. The starting data set contains: species name, number of individuals, height of flight (three layers - below, in, above the rotor), and distance from the observer. The final estimation of the collision index (the most probable number of collisions per turbine a year) is based on (1) estimation of the total number of individuals that use the defined area during a year and (2) estimation of probability that the individual will collide. In the latter (i.e. 2) the most important is that birds can actively avoid passing through the rotor swept (active avoidance rate) and that even birds, which crossed the rotor swept area not necessarily will be killed. Calculations are performed for each species separately and then are summarised to get the farm index as well as season indices. Some values of indices for raptors studied at 76 localities in Poland are given in the table. The final evaluation of the site is made as shown in a parametric analysis table, discussion of cumulative and barrier effects and the discussion of species specific risk to species of high conservation concern.

45

Santos, Francisco de N., Gregory Duthé, Imad Abdallah, Pierre-Élouan Réthoré, Wout Weijtjens, Eleni Chatzi, and Christof Devriendt. "Multivariate prediction on wake-affected wind turbines using graph neural networks." Journal of Physics: Conference Series 2647, no. 11 (June 1, 2024): 112006. http://dx.doi.org/10.1088/1742-6596/2647/11/112006.

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Abstract Modern wind turbines are large and slender dynamical structures with a fatigue loading profile of complex nature. The guarantee of their structural integrity is paramount for materializing cost efficient and more reliable wind energy. The measurement of the global dynamic response and loads of wind turbines is fundamental for achieving this goal. However, an industry-wide, cost-effective direct sensing framework is yet to arise. Moreover, deploying physical sensors and measurement systems on every structural component of interest of a wind turbine induces prohibitive costs in deployment, maintenance and data management. Considering that direct fluid-structure interaction simulations on a farm level are not computationally feasible, the preferred path for structural response estimation on wind farms has been surrogate modelling. Within this landscape, new model architectures have risen in recent years which are able to take into account graph structured data (i.e. non-euclidean data). Wind turbines positioned in a farm, where there is a layout- and topology-dependant interplay of aerodynamic wake affecting the loading profile and power production, lend themselves perfectly to this paradigm. Thus, in this contribution, we introduce the use of graph neural networks (GNN) for layout agnostic saptio-temporal joint modelling of fatigue loads effects, rotor-averaged wind speed and power production on individual turbines of wind farms. To this end, we generate stochastic dependent samples of inflow conditions for wind speed, wind direction, wind shear and nacelle yaw angles. Additionally, wind farm layouts are randomly generated based on different geometric shapes (rectangle, triangle, ellipse and sparse circles) with random parametrization (varying orientations, length/width ratio) for different numbers of turbines and minimal distance (based on the rotor diameter). Both the arbitrary layouts and the random inflow conditions are used as inputs for PyWake, a wind farm simulation tool capable of calculating wind farm flow fields, power and fatigue loads. In our analysis, we develop and compare the performance of the GENeralized Aggregation Networks (GEN), the Graph Attention Networks (GAT) and the Graph Isomorphism Network with Edges (GINE) in their accuracy and ability to generalize their joint predictions for unseen layouts, uncertain inflow conditions and fatigue load estimation on the blade root, tower top and tower base of any wind turbine in the farm. Our results indicate that the GEN layer yields the best performance, followed by GINE, while the GAT layer under-performs and is unable to differentiate between different wake conditions. We further observe that the GAT layer causes a latent space collapse, due to the coupled effect of the manner in which we initialise node features and the way in which its messages are computed.

46

Galinos, Christos, Jonas Kazda, Wai Hou Lio, and Gregor Giebel. "T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case." Energies 13, no. 6 (March 11, 2020): 1306. http://dx.doi.org/10.3390/en13061306.

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Wind farm load assessment is typically conducted using Computational Fluid Dynamics (CFD) or aeroelastic simulations, which need a lot of computer power. A number of applications, for example wind farm layout optimisation, turbine lifetime estimation and wind farm control, requires a simplified but sufficiently detailed model for computing the turbine fatigue load. In addition, the effect of turbine curtailment is particularly important in the calculation of the turbine loads. Therefore, this paper develops a fast and computationally efficient method for wind turbine load assessment in a wind farm, including the wake effects. In particular, the turbine fatigue loads are computed using a surrogate model that is based on the turbine operating condition, for example, power set-point and turbine location, and the ambient wind inflow information. The Turbine to Farm Loads (T2FL) surrogate model is constructed based on a set of high fidelity aeroelastic simulations, including the Dynamic Wake Meandering model and an artificial neural network that uses the Bayesian Regularisation (BR) and Levenberg–Marquardt (LM) algorithms. An ensemble model is used that outperforms model predictions of the BR and LM algorithms independently. Furthermore, a case study of a two turbine wind farm is demonstrated, where the turbine power set-point and fatigue loads can be optimised based on the proposed surrogate model. The results show that the downstream turbine producing more power than the upstream turbine is favourable for minimising the load. In addition, simulation results further demonstrate that the accumulated fatigue damage of turbines can be effectively distributed amongst the turbines in a wind farm using the power curtailment and the proposed surrogate model.

47

Perdana, Abram, and Ola Carlson. "Factors Influencing Design of Dynamic Reactive Power Compensation for an Offshore Wind Farm." Wind Engineering 33, no. 3 (May 2009): 273–85. http://dx.doi.org/10.1260/0309-524x.33.3.273.

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This paper investigates factors that influence the design of dynamic reactive power compensation (DRPC) for an offshore wind farm consisting of fixed-speed wind turbines. Several influencing factors are considered including DRPC device types and locations, network strength, and fault ride-through (FRT) capability of individual wind turbines in the wind farm. The DRPC alternatives discussed in this paper comprise of a STATCOM and an SVC. It is found that the exclusion of wind turbine FRT capability results in too large DRPC size estimation. In the case of wind turbines equipped with FRT capability, it was found that the investment cost for both STATCOM and SVC options are comparable. The study also emphasizes the necessity of performing dynamic analysis to correctly design the size and location of the DRPC device.

48

Luo, Ya Di, Jing Li, Zi Ming Guo, Gui Rong Shi, Dong Sheng Wang, and Bo Yan. "Research of Robust State Estimation Method and Program Implementation Considering Large-Scale Wind Power Integration." Applied Mechanics and Materials 672-674 (October 2014): 361–66. http://dx.doi.org/10.4028/www.scientific.net/amm.672-674.361.

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According to the characteristics of the wind farm measuration and the impact of bad data on the state estimation, this paper introduces the reference value of measurement type and the bad data reference factor into the weight function, and then presents the calculation method of state estimation method for solving residual contamination problem caused by large-scale wind power integration. In order to improve the software computing speed and the data section real-time performance of robust state estimation, using parallel algorithms to do Givens transformation. Finally, the simulation tests of a regional power grid to prove that the proposed method can effectively identify telemetry bad data of wind farms eliminate residual pollution caused by it, which improve the speed and accuracy of the State Estimation.

49

Li, Shuhui, Donald C. Wunsch, Edgar O’Hair, and Michael G. Giesselmann. "Comparative Analysis of Regression and Artificial Neural Network Models for Wind Turbine Power Curve Estimation." Journal of Solar Energy Engineering 123, no. 4 (July 1, 2001): 327–32. http://dx.doi.org/10.1115/1.1413216.

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This paper examines and compares regression and artificial neural network models used for the estimation of wind turbine power curves. First, characteristics of wind turbine power generation are investigated. Then, models for turbine power curve estimation using both regression and neural network methods are presented and compared. The parameter estimates for the regression model and training of the neural network are completed with the wind farm data, and the performances of the two models are studied. The regression model is shown to be function dependent, and the neural network model obtains its power curve estimation through learning. The neural network model is found to possess better performance than the regression model for turbine power curve estimation under complicated influence factors.

50

Sterle, Arnold, Christian A. Hans, and Jörg Raisch. "Model predictive control of wakes for wind farm power tracking." Journal of Physics: Conference Series 2767, no. 3 (June 1, 2024): 032005. http://dx.doi.org/10.1088/1742-6596/2767/3/032005.

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Abstract In this paper, a model predictive control scheme for wind farms is presented. Our approach considers wake dynamics including their influence on local wind conditions and allows the tracking of a given power reference. In detail, a Gaussian wake model is used in combination with observation points that carry wind condition information. This allows the estimation of the rotor effective wind speeds at downstream turbines, based on which we deduce their power output. Through different approximation methods, the associated finite horizon nonlinear optimization problem is reformulated in a mixed-integer quadratically-constrained quadratic program fashion. By solving the reformulated problem online, optimal yaw angles and axial induction factors are found. Closed-loop simulations indicate good power tracking capabilities over a wide range of power setpoints while distributing wind turbine infeed evenly among all units. Additionally, the simulation results underline real time capabilities of our approach.

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