scholarly journals Design and analysis of a wake model for spatially heterogeneous flow

2021 ◽  
Vol 6 (3) ◽  
pp. 737-758
Author(s):  
Alayna Farrell ◽  
Jennifer King ◽  
Caroline Draxl ◽  
Rafael Mudafort ◽  
Nicholas Hamilton ◽  
...  
Keyword(s):  
Flow Field ◽  
Power Output ◽  
Wind Farm ◽  
Atmospheric Effects ◽  
Power Prediction ◽  
Atmospheric Measurements ◽  
Heterogeneous Model ◽  
Site Specific ◽  
Heterogeneous Flow ◽  
Wake Modeling

Abstract. Methods of turbine wake modeling are being developed to more accurately account for spatially variant atmospheric conditions within wind farms. Most current wake modeling utilities are designed to apply a uniform flow field to the entire domain of a wind farm. When this method is used, the accuracy of power prediction and wind farm controls can be compromised depending on the flow-field characteristics of a particular area. In an effort to improve strategies of wind farm wake modeling and power prediction, FLOw Redirection and Induction in Steady State (FLORIS) was developed to implement sophisticated methods of atmospheric characterization and power output calculation. In this paper, we describe an adapted FLORIS model that features spatial heterogeneity in flow-field characterization. This model approximates an observed flow field by interpolating from a set of atmospheric measurements that represent local weather conditions. The objective of this method is to capture heterogeneous atmospheric effects caused by site-specific terrain features, without explicitly modeling the geometry of the wind farm terrain. The implemented adaptations were validated by comparing the simulated power predictions generated from FLORIS to the actual recorded wind farm output from the supervisory control and data acquisition (SCADA) recordings and large eddy simulations (LESs). When comparing the performance of the proposed heterogeneous model to homogeneous FLORIS simulations, the results show a 14.6 % decrease for mean absolute error (MAE) in wind farm power output predictions for cases using wind farm SCADA data and a 18.9 % decrease in LES case studies. The results of these studies also indicate that the efficacy of the proposed modeling techniques may vary with differing site-specific operational conditions. This work quantifies the accuracy of wind plant power predictions under heterogeneous flow conditions and establishes best practices for atmospheric surveying for wake modeling.

scholarly journals Design and analysis of a spatially heterogeneous wake

2020 ◽  
Author(s):  
Alayna Farrell ◽  
Jennifer King ◽  
Caroline Draxl ◽  
Rafael Mudafort ◽  
Nicholas Hamilton ◽  
...  
Keyword(s):  
Flow Field ◽  
Wind Farm ◽  
Wind Farms ◽  
Weather Conditions ◽  
Atmospheric Conditions ◽  
Power Prediction ◽  
Atmospheric Measurements ◽  
Flow Field Characteristics ◽  
Spatially Variant ◽  
Wake Modeling

Abstract. Methods of turbine wake modeling are being developed to more accurately account for spatially variant atmospheric conditions within wind farms. Most current wake modeling utilities are designed to apply a uniform flow field to the entire domain of a wind farm. When this method is used, the accuracy of power prediction and wind farm controls can be compromised depending on the flow-field characteristics of a particular area. In an effort to improve strategies of wind farm wake modeling and power prediction, FLOw Redirection and Induction in Steady State (FLORIS) was developed to implement sophisticated methods of atmospheric characterization and power output calculation. In this paper, we describe an adapted FLORIS model that features spatial heterogeneity in flow-field characterization. This model approximates an observed flow field by interpolating from a set of atmospheric measurements that represent local weather conditions. The adaptations were validated by comparing the simulated power predictions generated from FLORIS to the actual recorded wind farm output from the Supervisory Control And Data Acquisition (SCADA) recordings. This work quantifies the accuracy of wind plant power predictions under heterogeneous flow conditions and establishes best practices for atmospheric surveying for wake modeling.

Nacelle Anemometry for the Electric Power Prediction System

2007 ◽  
Author(s):  
J. Kuroda ◽  
M. Iida ◽  
C. Arakawa
Keyword(s):  
Numerical Simulation ◽  
Flow Field ◽  
Electric Power ◽  
Wind Velocity ◽  
Wind Turbines ◽  
Power Output ◽  
Measurement Data ◽  
Power Curve ◽  
Prediction System ◽  
Power Prediction

The purpose of this study is to establish the nacelle anemometry for the wind forecast. This paper describes the problems of the meteorological anemometry and the nacelle anemometry based on measurement data in Japan. In the results, it is shown that wind velocity measured at the mast is less related with power output of wind turbines than measured at the nacelle. However it seems power curve referred to the nacelle anemometer to shift to lower wind velocity. Then the numerical simulation is carried out for the flow field around the nacelle and the blade as the first step.

Control-Oriented Wind Farm Wake Modeling with Near Wake Performance and LES Validation

2020 ◽  
Author(s):  
Zhen Xie ◽  
Zhongwei Lin ◽  
Zhenyu Chen ◽  
Chuanxi Wang ◽  
Qingru Cui ◽  
...  
Keyword(s):  
Wind Farm ◽  
Near Wake ◽  
Wake Modeling

scholarly journals GMM-HMM-based Medium- and Long-term Multi-Wind Farm Correlated Power Output Time Series Generation Method

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Yufei Li ◽  
Bo Hu ◽  
Tao Niu ◽  
Shengpu Gao ◽  
Jiahao Yan ◽  
...  
Keyword(s):  
Time Series ◽  
Power Output ◽  
Wind Farm ◽  
Output Time

scholarly journals Predicting the Performance of Solar Power Generation Using Deep Learning Methods

2021 ◽  
Vol 11 (15) ◽  
pp. 6887
Author(s):  
Chung-Hong Lee ◽  
Hsin-Chang Yang ◽  
Guan-Bo Ye
Keyword(s):  
Power Generation ◽  
Solar Energy ◽  
Power Output ◽  
Power Plants ◽  
Solar Power ◽  
Photovoltaic Cell ◽  
Power Prediction ◽  
Seasonal Characteristics ◽  
Variable Nature ◽  
Solar Power Plants

In recent years, many countries have provided promotion policies related to renewable energy in order to take advantage of the environmental factors of sufficient sunlight. However, the application of solar energy in the power grid also has disadvantages. The most obvious is the variability of power output, which will put pressure on the system. As more grid reserves are needed to compensate for fluctuations in power output, the variable nature of solar power may hinder further deployment. Besides, one of the main issues surrounding solar energy is the variability and unpredictability of sunlight. If it is cloudy or covered by clouds during the day, the photovoltaic cell cannot produce satisfactory electricity. How to collect relevant factors (variables) and data to make predictions so that the solar system can increase the power generation of solar power plants is an important topic that every solar supplier is constantly thinking about. The view is taken, therefore, in this work, we utilized the historical monitoring data collected by the ground-connected solar power plants to predict the power generation, using daily characteristics (24 h) to replace the usual seasonal characteristics (365 days) as the experimental basis. Further, we implemented daily numerical prediction of the whole-point power generation. The preliminary experimental evaluations demonstrate that our developed method is sensible, allowing for exploring the performance of solar power prediction.

scholarly journals Spatiotemporal Correlations in the Power Output of Wind Farms: On the Impact of Atmospheric Stability

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1486 ◽  
Author(s):  
Nicolas Tobin ◽  
Adam Lavely ◽  
Sven Schmitz ◽  
Leonardo P. Chamorro
Keyword(s):  
Power Output ◽  
Wind Farm ◽  
Statistical Significance ◽  
Atmospheric Stability ◽  
Wind Farms ◽  
Temporal Correlations ◽  
Theoretical Predictions ◽  
Power Variance ◽  
Ambient Turbulence ◽  
The Impact

The dependence of temporal correlations in the power output of wind-turbine pairs on atmospheric stability is explored using theoretical arguments and wind-farm large-eddy simulations. For this purpose, a range of five distinct stability regimes, ranging from weakly stable to moderately convective, were investigated with the same aligned wind-farm layout used among simulations. The coherence spectrum between turbine pairs in each simulation was compared to theoretical predictions. We found with high statistical significance (p < 0.01) that higher levels of atmospheric instability lead to higher coherence between turbines, with wake motions reducing correlations up to 40%. This is attributed to higher dominance of atmospheric motions over wakes in strongly unstable flows. Good agreement resulted with the use of an empirical model for wake-added turbulence to predict the variation of turbine power coherence with ambient turbulence intensity (R 2 = 0.82), though other empirical relations may be applicable. It was shown that improperly accounting for turbine–turbine correlations can substantially impact power variance estimates on the order of a factor of 4.

Reliability Modeling of Wind Farm Considering the Outages of Connection Cables

2013 ◽  
Vol 291-294 ◽  
pp. 461-466
Author(s):  
Guo Bing Qiu ◽  
Wen Xia Liu ◽  
Jian Hua Zhang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Power Output ◽  
Complex Terrain ◽  
Wind Farm ◽  
Sequential Monte Carlo ◽  
Reliability Model ◽  
Wake Effect ◽  
Reliability Modeling ◽  
Wake Model

Considering the randomness of wind speed and wind direction, the partial wake effect between wind turbines (WTs) in complex terrain was analyzed and a multiple wake model in complex terrain was established. Taking the power output characteristic of WT into consideration, a wind farm reliability model which considered the outages of connection cables was presented. The model is implemented in MATLAB using sequential Monte Carlo simulation and the results show that this model corrects the power output of wind farm, while improving the accuracy of wind farm reliability model.

Probabilistic Assessment of Wind Farm Active Power Based on Monte-Carlo Simulation

2013 ◽  
Vol 291-294 ◽  
pp. 536-540 ◽  
Author(s):  
Xin Wei Wang ◽  
Jian Hua Zhang ◽  
Cheng Jiang ◽  
Lei Yu
Keyword(s):  
Monte Carlo ◽  
Power Output ◽  
Wind Farm ◽  
Active Power ◽  
Assessment Methods ◽  
Probabilistic Methods ◽  
Model Parameters ◽  
Probability Assessment ◽  
Reliability Models ◽  
Reliability Parameters

The conventional deterministic methods have been unable to accurately assess the active power output of the wind farm being the random and intermittent of wind power, and the probabilistic methods commonly used to solve this problem. In this paper the multi-state fault model is built considering run, outage and derating state of wind turbine, and then the reliability model of the wind farm is established considering the randomness of the wind speed, the wind farm wake effects and turbine failure. The active wind farm output probability assessment methods and processes based on the Monte Carlo method. The related programs are written in MATLAB, and the probability assessment for active power output of a wind farm in carried out, the effectiveness and adaptability of built reliability models and assessment methods are illustrated by analysis of the effects of reliability parameters and model parameters on assessment results.

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