An Operational Application of NWP Models in a Wind Power Forecasting Demonstration Experiment

Yu, Wei; Plante, André; Dyck, S.; Chardon, Laurent; Forcione, Alain; Choisnard, Julien; Benoit, Robert; Glazer, Anna; Roberge, G.; Petrucci, Franco; Bourret, Jacques; Antic, S.

doi:10.1260/0309-524x.38.1.1

Cited by 10 publications

(8 citation statements)

References 20 publications

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“…Another reason for which wind plants in Gaspé were chosen for this study is that Environment Canada and Hydro-Québec have been collaborating on wind energy forecasting projects since 2007 (Yu et al 2014). The province of Québec ranks as the second largest producer of wind energy in Canada with 2398 MW, mostly from wind plants on the Gaspé Peninsula, a region of favorable wind conditions.…”

Section: Icing Events Occurred In Wind Plants Of the Gaspé Regionmentioning

confidence: 99%

“…These were selected based on an observed power loss of over 20% of the theoretically generated power and differences in wind speed as measured by heated and nonheated anemometers at a wind plant in Gaspé. More details about the empirical power curve are given in Yu et al (2014) (the power curve is based on the power production and average wind speed from all turbine sites at the plant). ( Table 1 lists the average of the meteorological variables and the power loss percentage during these icing events.)…”

Section: Icing Events Occurred In Wind Plants Of the Gaspé Regionmentioning

confidence: 99%

“…4 for comparison. Figure 6a displays the real power production and theoretically generated power, the latter being calculated from an empirical turbine power curve with average nacelle wind speeds (Yu et al 2014). 4).…”

Section: ) Meteorological Fieldsmentioning

confidence: 99%

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Coupled Atmospheric–Ice Load Model for Evaluation of Wind Plant Power Loss

Yang

Choisnard

et al. 2015

Journal of Applied Meteorology and Climatology

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Icing is a weather phenomenon that is typical of cold climates. It impacts human activities through ice accretion on tower structures, transmission lines, and the blades of wind turbines. Icing on turbine blades, in particular, results in wind turbine performance degradation and/or safety shutdowns. The objective of this study is to explore the feasibility of using a coupled atmospheric and ice load model to simulate icing start-up, duration, and amount while also quantitatively evaluating power loss in wind plants related to icing events and mechanisms. Eight of 27 icing episodes identified for a wind plant in the Gaspé region of Québec (Canada) during the period 2008-10 were simulated using a mesoscale model (the Global Environmental Multiscale Limited-Area Model, or GEM-LAM). The simulations were verified using near-surface temperature, relative humidity, and wind speed, all of which compared well to in situ observations. Simulated wind speed, precipitation, cloud liquid water content, and median volume diameter of the droplets were used to drive ice load models to simulate the total ice load on a cylindrical structure. The three ice load models accounted for freezing rain, wet snow, and in-cloud icing, respectively, and in all three cases a sink term was added to account for melting due to radiation. The start-up and duration of ice were well captured by the coupled model, and a positive correlation was found between icing episodes and wind power reduction. This study demonstrates the improvements of the icing forecasts by using three ice load models, and provides a framework for both qualitative and quantitative evaluation of icing impact on wind turbine operations.

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Section: Icing Events Occurred In Wind Plants Of the Gaspé Regionmentioning

confidence: 99%

Section: Icing Events Occurred In Wind Plants Of the Gaspé Regionmentioning

confidence: 99%

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Coupled Atmospheric–Ice Load Model for Evaluation of Wind Plant Power Loss

Yang

Choisnard

et al. 2015

Journal of Applied Meteorology and Climatology

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“…References [7,8] forecasted the wind speed of wind turbines based on physical method with the data of wind speed and its direction, taking into account topographic change and wake effect. And the method proposed is capable of forecasting the wind power output by considering wind power curve.…”

Section: Introductionmentioning

confidence: 99%

“Section to Point” Correction Method for Wind Power Forecasting Based on Cloud Theory

Liu

et al. 2015

Mathematical Problems in Engineering

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As an intermittent energy, wind power has the characteristics of randomness and uncontrollability. It is of great significance to improve the accuracy of wind power forecasting. Currently, most models for wind power forecasting are based on wind speed forecasting. However, it is stuck in a dilemma called "garbage in, garbage out, " which means it is difficult to improve the forecasting accuracy without improving the accuracy of input data such as the wind speed. In this paper, a new model based on cloud theory is proposed. It establishes a more accurate relational model between the wind power and wind speed, which has lots of catastrophe points. Then, combined with the trend during adjacent time and the laws of historical data, the forecasting value will be corrected by the theory of "section to point" correction. It significantly improves the stability of forecasting accuracy and reduces significant forecasting errors at some particular points. At last, by analyzing the data of generation power and historical wind speed in Inner Mongolia, China, it is proved that the proposed method can effectively improve the accuracy of wind speed forecasting.

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“…Wake effects on the wind farm power curve: red dots are data measured in all wind directions and black dots are data measured in the wind directions that don't have wake effects.Environment Canada, in collaboration with Hydro-Quebec, has been carrying an experimental wind energy forecasting demonstration project[7] using the Canadian atmospheric mesoscale model, GEM-LAM [8]. The mesoscale model domain covers the regions of the wind farms used in this study.…”

mentioning

confidence: 99%

Development of a Wind to Power Model for Wind Farm Power Production Forecasting

Touani

Gagnon

et al. 2013

Wind Engineering

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The objective of this study is to develop a Wind to Power forecasting methodology where wind farm power curves are used to convert the predicted wind speeds to predicted power productions of wind farms. A methodology is proposed to develop a wind farm power curve by performing several experiments using historical wind farm power productions and wind measurements. A simple method, the Bins Method, is compared to an advanced method based on Artificial Neural Networks (ANN). It is shown that the advanced method does not have significant advantages in terms of accuracy. However, due to its flexibility on the choice of input parameters, the ANN method is best suited for studies such as the optimal selection of input parameters. For this reason, these two methods are used alternatively in this study according to the applications. The influence of different meteorological parameters on the wind farm power curve has also been investigated with the ANN method. The wind farm power curves developed in this work have been tested for the prediction of the wind power productions using forecasted wind speed data obtained from a Numerical Weather Prediction model (GEM-LAM) as input variables to the Wind to Power model. The predicted power productions compare well with those recorded at the wind farm. The proposed method is applied to a second operating wind farm to demonstrate its validity.

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An Operational Application of NWP Models in a Wind Power Forecasting Demonstration Experiment

Cited by 10 publications

References 20 publications

Coupled Atmospheric–Ice Load Model for Evaluation of Wind Plant Power Loss

Coupled Atmospheric–Ice Load Model for Evaluation of Wind Plant Power Loss

“Section to Point” Correction Method for Wind Power Forecasting Based on Cloud Theory

Development of a Wind to Power Model for Wind Farm Power Production Forecasting

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