Modeling Large-Scale Wind Farms for Reliability Analysis Considering Wake Effect

Large scale wind power penetration has a significant impact on the reliability of the electric generation systems. A wind farm consists of a large number of wind turbine generators (WTGs). A major difficulty in modeling wind farms is that the WTG not have an independent capacity distribution due to the dependence of the individual turbine output on the same energy source, the wind. In this paper, a model of the wind farm output power considering multi-wake effects is established according to the probability distribution of the wind speed and the characteristic of the wind generator output power: based on the simple Jenson wake effect model, the wake effect with wind speed sheer model and the detail wake effect model with the detail shade areas of the upstream wind turbines are discussed respectively. Compared to the individual wake effect model, this model takes the wind farm as a whole and considers the multi-wakes effect on the same unit. As a result the loss of the velocity inside the wind farm is considered more exactly. Furthermore, considering the features of sequentially and self-correlation of wind speed, an auto-regressive and moving average (ARMA) model for wind speed is built up. Also the reliability model of wind farm is built when the output characteristics of wind power generation units, correlation of wind speeds among different wind farms, outage model of wind power generation units, wake effect of wind farm and air temperature are considered. Simulation results validate the effectiveness of the proposed models. These models can be used to research the reliability of power grid containing wind farms, wind farm capacity credit as well as the interconnection among wind farms

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Analysis of Wind Turbine Aging through Operation Data Calibrated by LiDAR Measurement

Energies ◽

10.3390/en14082319 ◽

2021 ◽

Vol 14 (8) ◽

pp. 2319

Author(s):

Hyun-Goo Kim ◽

Jin-Young Kim

Keyword(s):

Power Generation ◽

Wind Speed ◽

Wind Turbine ◽

Wind Farm ◽

Free Stream ◽

Turbine Blades ◽

Wind Farms ◽

Lidar Measurement ◽

Wind Turbine Blades ◽

Wake Effect

This study analyzed the performance decline of wind turbine with age using the SCADA (Supervisory Control And Data Acquisition) data and the short-term in situ LiDAR (Light Detection and Ranging) measurements taken at the Shinan wind farm located on the coast of Bigeumdo Island in the southwestern sea of South Korea. Existing methods have generally attempted to estimate performance aging through long-term trend analysis of a normalized capacity factor in which wind speed variability is calibrated. However, this study proposes a new method using SCADA data for wind farms whose total operation period is short (less than a decade). That is, the trend of power output deficit between predicted and actual power generation was analyzed in order to estimate performance aging, wherein a theoretically predicted level of power generation was calculated by substituting a free stream wind speed projecting to a wind turbine into its power curve. To calibrate a distorted wind speed measurement in a nacelle anemometer caused by the wake effect resulting from the rotation of wind-turbine blades and the shape of the nacelle, the free stream wind speed was measured using LiDAR remote sensing as the reference data; and the nacelle transfer function, which converts nacelle wind speed into free stream wind speed, was derived. A four-year analysis of the Shinan wind farm showed that the rate of performance aging of the wind turbines was estimated to be −0.52%p/year.

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Performance Enhancement of Proposed Namaacha Wind Farm by Minimising Losses Due to the Wake Effect: A Mozambican Case Study

Energies ◽

10.3390/en14144291 ◽

2021 ◽

Vol 14 (14) ◽

pp. 4291

Author(s):

Paxis Marques João Roque ◽

Shyama Pada Chowdhury ◽

Zhongjie Huan

Keyword(s):

Wind Speed ◽

Wind Power ◽

Wind Turbines ◽

Power Output ◽

Wind Farm ◽

Wind Farms ◽

Operating Conditions ◽

Wake Effect ◽

Wind Generators ◽

Wind Potential

District of Namaacha in Maputo Province of Mozambique presents a high wind potential, with an average wind speed of around 7.5 m/s and huge open fields that are favourable to the installation of wind farms. However, in order to make better use of the wind potential, it is necessary to evaluate the operating conditions of the turbines and guide the independent power producers (IPPs) on how to efficiently use wind power. The investigation of the wind farm operating conditions is justified by the fact that the implementation of wind power systems is quite expensive, and therefore, it is imperative to find alternatives to reduce power losses and improve energy production. Taking into account the power needs in Mozambique, this project applied hybrid optimisation of multiple energy resources (HOMER) to size the capacity of the wind farm and the number of turbines that guarantee an adequate supply of power. Moreover, considering the topographic conditions of the site and the operational parameters of the turbines, the system advisor model (SAM) was applied to evaluate the performance of the Vestas V82-1.65 horizontal axis turbines and the system’s power output as a result of the wake effect. For any wind farm, it is evident that wind turbines’ wake effects significantly reduce the performance of wind farms. The paper seeks to design and examine the proper layout for practical placements of wind generators. Firstly, a survey on the Namaacha’s electricity demand was carried out in order to obtain the district’s daily load profile required to size the wind farm’s capacity. Secondly, with the previous knowledge that the operation of wind farms is affected by wake losses, different wake effect models applied by SAM were examined and the Eddy–Viscosity model was selected to perform the analysis. Three distinct layouts result from SAM optimisation, and the best one is recommended for wind turbines installation for maximising wind to energy generation. Although it is understood that the wake effect occurs on any wind farm, it is observed that wake losses can be minimised through the proper design of the wind generators’ placement layout. Therefore, any wind farm project should, from its layout, examine the optimal wind farm arrangement, which will depend on the wind speed, wind direction, turbine hub height, and other topographical characteristics of the area. In that context, considering the topographic and climate features of Mozambique, the study brings novelty in the way wind farms should be placed in the district and wake losses minimised. The study is based on a real assumption that the project can be implemented in the district, and thus, considering the wind farm’s capacity, the district’s energy needs could be met. The optimal transversal and longitudinal distances between turbines recommended are 8Do and 10Do, respectively, arranged according to layout 1, with wake losses of about 1.7%, land utilisation of about 6.46 Km2, and power output estimated at 71.844 GWh per year.

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Two methods for estimating limits to large-scale wind power generation

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1408251112 ◽

2015 ◽

Vol 112 (36) ◽

pp. 11169-11174 ◽

Cited By ~ 32

Author(s):

Lee M. Miller ◽

Nathaniel A. Brunsell ◽

David B. Mechem ◽

Fabian Gans ◽

Andrew J. Monaghan ◽

...

Keyword(s):

Power Generation ◽

Kinetic Energy ◽

Wind Power ◽

Energy Flux ◽

Wind Turbines ◽

Large Scale ◽

Wind Farms ◽

Wind Power Generation ◽

Flux Method ◽

Free Atmosphere

Wind turbines remove kinetic energy from the atmospheric flow, which reduces wind speeds and limits generation rates of large wind farms. These interactions can be approximated using a vertical kinetic energy (VKE) flux method, which predicts that the maximum power generation potential is 26% of the instantaneous downward transport of kinetic energy using the preturbine climatology. We compare the energy flux method to the Weather Research and Forecasting (WRF) regional atmospheric model equipped with a wind turbine parameterization over a 105 km2 region in the central United States. The WRF simulations yield a maximum generation of 1.1 We⋅m−2, whereas the VKE method predicts the time series while underestimating the maximum generation rate by about 50%. Because VKE derives the generation limit from the preturbine climatology, potential changes in the vertical kinetic energy flux from the free atmosphere are not considered. Such changes are important at night when WRF estimates are about twice the VKE value because wind turbines interact with the decoupled nocturnal low-level jet in this region. Daytime estimates agree better to 20% because the wind turbines induce comparatively small changes to the downward kinetic energy flux. This combination of downward transport limits and wind speed reductions explains why large-scale wind power generation in windy regions is limited to about 1 We⋅m−2, with VKE capturing this combination in a comparatively simple way.

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Impacts of Integration of Wind Farms on Power System Transient Stability

Applied Sciences ◽

10.3390/app8081289 ◽

2018 ◽

Vol 8 (8) ◽

pp. 1289 ◽

Cited By ~ 11

Author(s):

Shiwei Xia ◽

Qian Zhang ◽

S.T. Hussain ◽

Baodi Hong ◽

Weiwei Zou

Keyword(s):

Power Generation ◽

Wind Speed ◽

Power System ◽

Wind Power ◽

Transient Stability ◽

Wind Farms ◽

Wind Power Generation ◽

Wind Generation ◽

System Operation ◽

Power System Operation

To compensate for the ever-growing energy gap, renewable resources have undergone fast expansions worldwide in recent years, but they also result in some challenges for power system operation such as the static security and transient stability issues. In particular, as wind power generation accounts for a large share of these renewable energy and reduces the inertia of a power network, the transient stability of power systems with high-level wind generation is decreased and has attracted wide attention recently. Effectively analyzing and evaluating the impact of wind generation on power transient stability is indispensable to improve power system operation security level. In this paper, a Doubly Fed Induction Generator with a two-lumped mass wind turbine model is presented firstly to analyze impacts of wind power generation on power system transient stability. Although the influence of wind power generation on transient stability has been comprehensively studied, many other key factors such as the locations of wind farms and the wind speed driving the wind turbine are also investigated in detail. Furthermore, how to improve the transient stability by installing capacitors is also demonstrated in the paper. The IEEE 14-bus system is used to conduct these investigations by using the Power System Analysis Tool, and the time domain simulation results show that: (1) By increasing the capacity of wind farms, the system instability increases; (2) The wind farm location and wind speed can affect power system transient stability; (3) Installing capacitors will effectively improve system transient stability.

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The Application of Wind Speed Numerical Simulation in Wind Power Generation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.380-384.3370 ◽

2013 ◽

Vol 380-384 ◽

pp. 3370-3373 ◽

Cited By ~ 1

Author(s):

Li Yang Liu ◽

Jun Ji Wu ◽

Shao Liang Meng

Keyword(s):

Wind Speed ◽

Wind Energy ◽

Wind Power ◽

Wind Farm ◽

Power Grid ◽

Wind Farms ◽

Stable Operation ◽

Wind Gust ◽

Gradual Change ◽

Wake Effect

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.

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Estimating Wind Farm Transformers Rating through Lifetime Characterization Based on Stochastic Modeling of Wind Power

Energies ◽

10.3390/en14051498 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1498

Author(s):

Maurizio Fantauzzi ◽

Davide Lauria ◽

Fabio Mottola ◽

Daniela Proto

Keyword(s):

Power Generation ◽

Wind Power ◽

Wind Farm ◽

Wind Farms ◽

Mineral Oil ◽

Wind Power Generation ◽

Numerical Application ◽

Stochastic Characterization ◽

Large Wind

This paper deals with the problem of the optimal rating of mineral-oil-immersed transformers in large wind farms. The optimal rating is derived based on the probabilistic analyses of wind power generation through the Ornstein–Uhlenbeck stochastic process and on thermal model of the transformer through the integration of stochastic differential equations. These analyses allow the stochastic characterization of lifetime reduction of the transformer and then its optimal rating through a simple closed form. The numerical application highlights the effectiveness and easy applicability of the proposed methodology. The proposed methodology allows deriving the rating of transformers which better fits the specific peculiarities of wind power generation. Compared to the conventional approaches, the proposed method can better adapt the transformer size to the intermittence and variability of the power generated by wind farms, thus overcoming the often-recognized reduced lifetime.

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Evaluation Method of Wind Speed Time-Shifting Characteristics at Multiple Scales and Its Application in Wind Power System

Mathematical Problems in Engineering ◽

10.1155/2020/4057630 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Han Wang ◽

Shuang Han ◽

Yongqian Liu ◽

Aimei Lin

Keyword(s):

Wind Speed ◽

Power System ◽

Wind Power ◽

Large Scale ◽

Wind Farm ◽

Evaluation Method ◽

Multiple Scales ◽

Wind Farms ◽

Physical Method ◽

Wind Power System

The wind speed sequences at different spatial positions have a certain spatiotemporal coupling relationship. It is of great significance to analyze the clustering effect of the wind farm(s) and reduce the adverse impact of large-scale wind power integration if we can grasp this relationship at multiple scales. At present, the physical method cannot optimize the time-shifting characteristics in real time, and the research scope is concentrated on the wind farm. The statistical method cannot quantitatively describe the temporal relationship and the speed variation among wind speed sequences at different spatial positions. To solve the above problems, a quantification method of wind speed time-shifting characteristics based on wind process is proposed in this paper. Two evaluation indexes, the delay time and the decay speed, are presented to quantify the time-shifting characteristics. The effectiveness of the proposed method is verified from the perspective of the correlation between wind speed sequences. The time-shifting characteristics of wind speed sequences under the wind farms scale and the wind turbines scale are studied, respectively. The results show that the proposed evaluation method can effectively achieve the quantitative analysis of time-shifting and could improve the results continuously according to the actual wind conditions. Besides, it is suitable for any spatial scale. The calculation results can be directly applied to the wind power system to help obtain the more accurate output of the wind farm.

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Estimation of the Daily Variability of Aggregate Wind Power Generation in Alberta, Canada

Energies ◽

10.3390/en12101998 ◽

2019 ◽

Vol 12 (10) ◽

pp. 1998 ◽

Cited By ~ 1

Author(s):

Yilan Luo ◽

Deniz Sezer ◽

David Wood ◽

Mingkuan Wu ◽

Hamid Zareipour

Keyword(s):

Power Generation ◽

Wind Power ◽

Wind Farm ◽

Wind Farms ◽

Gaussian Random Fields ◽

Wind Power Generation ◽

Temporal Correlations ◽

Prevailing Wind Direction ◽

Full Symmetry ◽

Spatio Temporal

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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On the wake effect in wind farm power forecasting: a new data-driven approach

E3S Web of Conferences ◽

10.1051/e3sconf/202019708016 ◽

2020 ◽

Vol 197 ◽

pp. 08016

Author(s):

Fabio Famoso ◽

Sebastian Brusca ◽

Antonio Galvagno ◽

Michele Messina ◽

Rosario Lanzafame

Keyword(s):

Power Generation ◽

Wind Power ◽

Wind Farm ◽

Wind Farms ◽

Wind Power Generation ◽

Physical Models ◽

Production Forecast ◽

Innovative Method ◽

Power Forecasting

Wind power generation differs from other energy sources, such as thermal, solar or hydro, due to the inherent stochastic nature of wind. For this reason wind power forecasting, especially for wind farms, is a complex task that cannot be accurately solved with traditional statistical methods or needs large computational systems if physical models are used. Recently, the so-called learning approaches are considered a good compromise among the previous methods since they are able to integrate physical phenomena such as wake effects without presenting heavy computational loads. The present work deals with an innovative method to forecast wind power generation in a wind farm with a combination of GISbased methods, neural network approach and a wake physical model. This innovative method was tested with a wind farm located in Sicily (Italy), used as a case study. It consists of 30 identical wind turbines (850 kW each one), located at different heights, for an overall Power peak of 25 MW. The time series dataset consists of one year with a sampling time of 10 minutes considering wind speeds and wind directions. The output of this innovative model leaded to good results, especially for medium-term overall energy production forecast for the case study.

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Effects of Large Scale Wind Power on Total System Variability and Operation: Case Study of Northern Ireland

Wind Engineering ◽

10.1260/030952403321833725 ◽

2003 ◽

Vol 27 (1) ◽

pp. 3-20 ◽

Cited By ~ 22

Author(s):

Shashi Persaud ◽

Brendan Fox ◽

Damian Flynn

Keyword(s):

Power Generation ◽

Northern Ireland ◽

Wind Power ◽

Large Scale ◽

Wind Farms ◽

Wind Power Generation ◽

Wind Generation ◽

Total System ◽

Power Capacity ◽

Spinning Reserve

The paper simulates the potential impact of significant wind power capacity on key operational aspects of a medium-sized grid-power system, viz. generator loading levels, system reserve availability and generator ramping requirements. The measured data, from Northern Ireland, consist of three years of 1/2 hourly metered records of (i) total energy generation and (ii) five wind farms, each of 5 MW capacity. These wind power data were scaled-up to represent a 10% annual energy contribution, taking account of diversity on the specific variability of total wind power output. The wind power generation reduced the system non-wind peak-generation. This reduction equalled 20% of the installed wind power capacity. There was also a reduction in the minimum non-wind generation, which equalled 43% of the wind power capacity. The analysis also showed that the spinning-reserve requirement depended on the accuracy of forecasting wind power ahead of scheduling, i.e. on the operational mode. When wind power was predicted accurately, (i) it was possible to reduce non-wind generation without over-commitment, but, (ii) the spinning-reserve non-wind conventional generation would usually have to be increased by 25% of the wind power capacity, unless quick-start gas generation was available. However, with unpredicted wind power generation, (i) despite reductions in non-wind generation, there was frequent over-commitment of conventional generation, but (ii) usually the spinning-reserve margin could be reduced by 10% of the wind power capacity with the same degree of risk. Finally, it was shown that wind power generation did not significantly increase the ramping duty on the system. For accurately predicted and unpredicted wind power the increases were only 4% and 5% respectively.

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