Modelling atmospheric icing: A comparison between icing calculated with measured meteorological data and NWP data

Thorsson, Petra; Söderberg, Stefan; Bergström, Hans

doi:10.1016/j.coldregions.2015.07.003

Cited by 13 publications

(7 citation statements)

References 15 publications

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“…For wind turbine icing forecasts, earlier studies have mainly focused on the uncertainties in the initial/ boundary conditions and model formulations of the NWP model (e.g., Molinder et al 2018;Davis et al 2014;Thorsson et al 2015). In Molinder et al (2018), an NWP ensemble and a neighborhood method accounting for uncertainties in the representation of the wind turbine locations was used confirming the benefits of an ensemble for the examined period.…”

Section: Introductionmentioning

confidence: 99%

The Use of Uncertainty Quantification for the Empirical Modeling of Wind Turbine Icing

Molinder

Körnich

Olsson

et al. 2019

Journal of Applied Meteorology and Climatology

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A novel uncertainty quantification method is used to evaluate the impact of uncertainties of parameters within the icing model in the modeling chain for icing-related wind power production loss forecasts. As a first step, uncertain parameters in the icing model were identified from the literature and personal communications. These parameters are the median volume diameter of the hydrometeors, the sticking efficiency for snow and graupel, the Nusselt number, the shedding factor, and the wind erosion factor. The sensitivity of these parameters on icing-related wind power production losses is examined. An icing model ensemble representing the estimated parameter uncertainties is designed using so-called deterministic sampling and is run for two periods over a total of 29 weeks. Deterministic sampling allows an exact representation of the uncertainty and, in future applications, further calibration of these parameters. Also, the number of required ensemble members is reduced drastically relative to the commonly used random-sampling method, thus enabling faster delivery and a more flexible system. The results from random and deterministic sampling are compared and agree very well, confirming the usefulness of deterministic sampling. The ensemble mean of the nine-member icing model ensemble generated with deterministic sampling is shown to improve the forecast skill relative to one single forecast for the winter periods. In addition, the ensemble spread provides valuable information as compared with a single forecast in terms of forecasting uncertainty. However, addressing uncertainties in the icing model alone underestimates the forecast uncertainty, thus stressing the need for a fully probabilistic approach in the modeling chain for wind power forecasts in a cold climate.

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Section: Introductionmentioning

confidence: 99%

The Use of Uncertainty Quantification for the Empirical Modeling of Wind Turbine Icing

Molinder

Körnich

Olsson

et al. 2019

Journal of Applied Meteorology and Climatology

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“…Furthermore, an EPS for short-range forecasting has been investigated for wind energy purposes (e.g. Pinson and Kariniotakis, 2010;Traiteur et al, 2013). Traiteur et al (2013) studied the use of a short-range EPS for 1 h wind speed forecasting and showed that a statistically calibrated EPS outperforms other forecast methods.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic forecasting of wind power production losses in cold climates: a case study

et al. 2018

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Abstract. The problem of icing on wind turbines in cold climates is addressed using probabilistic forecasting to improve next-day forecasts of icing and related production losses. A case study of probabilistic forecasts was generated for a 2-week period. Uncertainties in initial and boundary conditions are represented with an ensemble forecasting system, while uncertainties in the spatial representation are included with a neighbourhood method. Using probabilistic forecasting instead of one single forecast was shown to improve the forecast skill of the ice-related production loss forecasts and hence the icing forecasts. The spread of the multiple forecasts can be used as an estimate of the forecast uncertainty and of the likelihood for icing and severe production losses. Best results, both in terms of forecast skill and forecasted uncertainty, were achieved using both the ensemble forecast and the neighbourhood method combined. This demonstrates that the application of probabilistic forecasting for wind power in cold climates can be valuable when planning next-day energy production, in the usage of de-icing systems and for site safety.

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“…In a similar manner, experimental data represents the behaviour of the system under controlled conditions, for example the conditions inside a cold chamber. Data include environmental information (pressure, temperature, humidity and wind speed and direction) at the specified location and information on the specific studied phenomenon such atmospheric icing [1]. Wind profiles are closely related with the terrain type and complexity of the topology [1,2].…”

Section: Numerical Simulation Mediamentioning

confidence: 99%

“…Data include environmental information (pressure, temperature, humidity and wind speed and direction) at the specified location and information on the specific studied phenomenon such atmospheric icing [1]. Wind profiles are closely related with the terrain type and complexity of the topology [1,2]. In the case of ice accretion on wire, relating to instrument performance, the data include ice load, ice type, register of real time appearance of the ice, tension and torsion of the wire elements, electrical variables like electrical current, resistance (when applied) and power loss.…”

Section: Numerical Simulation Mediamentioning

confidence: 99%

Smart Solution to Icing Accretion Problems

Polanco¹

2017

IRATJ

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Modelling atmospheric icing: A comparison between icing calculated with measured meteorological data and NWP data

Cited by 13 publications

References 15 publications

The Use of Uncertainty Quantification for the Empirical Modeling of Wind Turbine Icing

The Use of Uncertainty Quantification for the Empirical Modeling of Wind Turbine Icing

Probabilistic forecasting of wind power production losses in cold climates: a case study

Smart Solution to Icing Accretion Problems

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