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

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1498
Author(s):  
Maurizio Fantauzzi ◽  
Davide Lauria ◽  
Fabio Mottola ◽  
Daniela Proto

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.

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1998 ◽  
Author(s):  
Yilan Luo ◽  
Deniz Sezer ◽  
David Wood ◽  
Mingkuan Wu ◽  
Hamid Zareipour

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.


2020 ◽  
Vol 197 ◽  
pp. 08016
Author(s):  
Fabio Famoso ◽  
Sebastian Brusca ◽  
Antonio Galvagno ◽  
Michele Messina ◽  
Rosario Lanzafame

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.


2014 ◽  
Vol 543-547 ◽  
pp. 647-652
Author(s):  
Ye Zhou Hu ◽  
Lin Zhang ◽  
Pai Liu ◽  
Xin Yuan Liu ◽  
Ming Zhou

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


Land ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 120 ◽  
Author(s):  
Salvatore Giuffrida ◽  
Filippo Gagliano ◽  
Francesco Nocera ◽  
Maria Trovato

In recent years, the scientific interest for the economic and landscape impact of wind farms has increased. This paper presents a useful GIS tool that allows for helping policymakers and investors to identify promising areas for wind power generation as well as landscape impact and financial and economic sustainability of wind farms. The results of the research carried out for exploring the potential for wind energy in two territorial contexts of Sicily region are presented with the particular look at the possibilities of economic developing, stakeholders’ opportunities and obstacles in the policy, legal, and regulatory framework.


2019 ◽  
Vol 21 (2) ◽  
pp. 745-754
Author(s):  
Otávio Augusto de Oliveira Lima Barra ◽  
Fábio Perdigão Vasconcelos ◽  
Danilo Vieira dos Santos ◽  
Adely Pereira Silveira

O Brasil é um país com uma extensa linha de costa, são cerca de 7.367 km de extensão do seu litoral, com um potencial natural para a geração de energia eólica. O estado do Ceará é um dos maiores produtores de energia eólica para o país, obtendo notoriedade e a necessidade de manutenção dos seus parques eólicos, especialmente se instalados em zonas de costa, onde há uma grande dinâmica natural. O presente trabalho, busca o acompanhamento das dinâmicas morfológicas na praia de Volta do Rio, localizada em Acaraú/CE, que fica a cerca de 238 km de Fortaleza/CE. Os dados coletados em idas à campo, constataram que há um forte processo erosivo atuante na praia de Volta do Rio, o que alerta para a contenção do avanço marinho sob o parque eólico presente no local. A erosão é um fenômeno natural que trabalha na modelação de demasiadas formas terrestres. No litoral, isso não é diferente, por ser um ambiente altamente dinâmico onde há a interação entre continente, atmosfera e oceano, sendo possível encontrar diversos atuantes que podem intensificar os processos erosivos, sejam eles o vento, maré, ou por intervenções humanas, como construções e ocupações indevidas ao longo da linha de costa.Palavras Chave: Volta do Rio; Energia Eólica; Erosão. ABSTRACTBrazil is a country with an extensive coastline, about 7,367 km of coastline, with a natural potential for wind power generation. The state of Ceará is one of the largest producers of wind energy for the country, obtaining notoriety and required maintenance of its wind farms, especially if located in coastal areas, where there is a great natural dynamic. The present work seeks the movement of morphological dynamics in the beach of Volta do Rio, located in Acaraú/CE, which is about 238 km from Fortaleza/CE. The data collected in the field found that there is a strong erosive process on the Beach of Volta do Rio, which warns about the expansion of advanced marine on the wind farm present on site. Erosion is a natural phenomenon that works in the modeling of many hearth forms. On the coast, this is not different, considering a highly dynamic environment in which there is an interaction between continent, atmosphere and ocean, being possible to find many factors that can intensify the erosive processes, such as wind, tide, or human intervention, as constructions and improper occupations along the coast line.Key words: Volta do Rio; Wind Energy; Erosion. RESUMENBrasil es un país con una extensa costa, cerca de 7.367 km de costa, con un potencial natural para la generación de energía eólica. El estado del Ceará es uno de los mayores productores de energía eólica del país, ganando notoriedad y la necesidad de mantener sus parques eólicos, especialmente si está instalado en zonas costeras, donde existe una gran dinámica natural. La presente investigación tiene como objetivo monitorear la dinámica morfológica en la playa de Vuelta del Rio, ubicada en Acaraú / CE, que está a unos 238 km de Fortaleza / CE. Los datos recopilados en los viajes de campo, encontraron que hay un fuerte proceso erosivo en la playa de Vuelta del Rio, que advierte sobre la contención del avance marino bajo el parque eólico presente en el sitio. La erosión es un fenómeno natural que funciona en el modelado de muchas formas terrestres. En la costa, esto no es diferente, ya que es un entorno altamente dinámico donde existe la interacción entre el continente, la atmósfera y el océano, permitiendo encontrar varios actores que pueden intensificar los procesos erosivos, ya sea viento, marea o intervenciones humanas, como edificios y ocupaciones inadecuadas a lo largo de la costa.Palabras clave: Vuelta del Río; Energía Eólica; Erosión.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Amila T. Peiris ◽  
Jeevani Jayasinghe ◽  
Upaka Rathnayake

Wind power, as a renewable energy resource, has taken much attention of the energy authorities in many countries, as it is used as one of the major energy sources to satisfy the ever-increasing energy demand. However, careful attention is needed in identifying the wind power potential in a particular area due to climate changes. In this sense, forecasting both wind power generation and wind power potential is essential. This paper develops artificial neural network (ANN) models to forecast wind power generation in “Pawan Danawi”, a functioning wind farm in Sri Lanka. Wind speed, wind direction, and ambient temperature of the area were used as the independent variable matrices of the developed ANN models, while the generated wind power was used as the dependent variable. The models were tested with three training algorithms, namely, Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) training algorithms. In addition, the model was calibrated for five validation percentages (5% to 25% in 5% intervals) under each algorithm to identify the best training algorithm with the most suitable training and validation percentages. Mean squared error (MSE), coefficient of correlation (R), root mean squared error ratio (RSR), Nash number, and BIAS were used to evaluate the performance of the developed ANN models. Results revealed that all three training algorithms produce acceptable predictions for the power generation in the Pawan Danawi wind farm with R > 0.91, MSE < 0.22, and BIAS < 1. Among them, the LM training algorithm at 70% of training and 5% of validation percentages produces the best forecasting results. The developed models can be effectively used in the prediction of wind power at the Pawan Danawi wind farm. In addition, the models can be used with the projected climatic scenarios in predicting the future wind power harvest. Furthermore, the models can acceptably be used in similar environmental and climatic conditions to identify the wind power potential of the area.


2018 ◽  
Vol 42 (6) ◽  
pp. 547-560 ◽  
Author(s):  
Fa Wang ◽  
Mario Garcia-Sanz

The power generation of a wind farm depends on the efficiency of the individual wind turbines of the farm. In large wind farms, wind turbines usually affect each other aerodynamically at some specific wind directions. Previous studies suggest that a way to maximize the power generation of these wind farms is to reduce the generation of the first rows wind turbines to allow the next rows to generate more power (coordinated case). Yet, other studies indicate that the maximum generation of the wind farm is reached when every wind turbine works at its individual maximum power coefficient CPmax (individual case). This article studies this paradigm and proposes a practical method to evaluate when the wind farm needs to be controlled according to the individual or the coordinated case. The discussion is based on basic principles, numerical computations, and wind tunnel experiments.


2020 ◽  
pp. 0309524X2097211
Author(s):  
Cem Özen ◽  
Umur Dinç ◽  
Ali Deniz ◽  
Haldun Karan

Forecasting of the wind speed and power generation for a wind farm has always been quite challenging and has importance in terms of balancing the electricity grid and preventing energy imbalance penalties. This study focuses on creating a hybrid model that uses both numerical weather prediction model and gradient boosting machines (GBM) for wind power generation forecast. Weather Research and Forecasting (WRF) model with a low spatial resolution is used to increase temporal resolutions of the computed new or existing variables whereas GBM is used for downscaling purposes. The results of the hybrid model have been compared with the outputs of a stand-alone WRF which is well configured in terms of physical schemes and has a high spatial resolution for Yahyalı wind farm over a complex terrain located in Turkey. Consequently, the superiority of the hybrid model in terms of both performance indicators and computational expense in detail is shown.


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