The impact of humidity on performance of wind turbine

Danook, Suad Hassan; Jassim, Khamis Joir; Hussein, Adnan M.

doi:10.1016/j.csite.2019.100456

Cited by 20 publications

(3 citation statements)

References 12 publications

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“…A method to interpolate power curves that are valid for site-specific air densities is also presented, but their objective is not to focus on temporal variations and they use annual averages to find the corresponding power curve. Other very recent publications also ignores these seasonal variations and consider the effect of geographical altitude or humidity in air density and the consequent wind energy production [40,41].…”

Section: Introductionmentioning

confidence: 99%

The Consequences of Air Density Variations over Northeastern Scotland for Offshore Wind Energy Potential

et al. 2019

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Hywind-Scotland is a wind farm in Scotland that for many reasons is at the leading edge of technology and is located at a paradigmatic study area for offshore wind energy assessment. The objective of this paper is to compute the Capacity Factor ( C F ) changes and instantaneous power generation changes due to seasonal and hourly fluctuations in air density. For that reason, the novel ERA5 reanalysis is used as a source of temperature, pressure, and wind speed data. Seasonal results for winter show that C F values increase by 3% due to low temperatures and denser air, with economical profit consequences of tens of thousands (US$). Hourly results show variations of 7% in air density and of 26% in power generation via FAST simulations, emphasizing the need to include air density in short-term wind energy studying.

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Section: Introductionmentioning

confidence: 99%

The Consequences of Air Density Variations over Northeastern Scotland for Offshore Wind Energy Potential

et al. 2019

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“…Although the method is applied around the Iberian Peninsula, it can be globally generalized for interesting offshore locations. Air density is a very recent topic of study in wind energy, although its variations are usually considered spatially and in terms of geographical elevation or position [10,11].…”

Section: Introductionmentioning

confidence: 99%

Seasonal Correction of Offshore Wind Energy Potential due to Air Density: Case of the Iberian Peninsula

et al. 2019

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A constant value of air density based on its annual average value at a given location is commonly used for the computation of the annual energy production in wind industry. Thus, the correction required in the estimation of daily, monthly or seasonal wind energy production, due to the use of air density, is ordinarily omitted in existing literature. The general method, based on the implementation of the wind speed’s Weibull distribution over the power curve of the turbine, omits it if the power curve is not corrected according to the air density of the site. In this study, the seasonal variation of air density was shown to be highly relevant for the computation of offshore wind energy potential around the Iberian Peninsula. If the temperature, pressure, and moisture are taken into account, the wind power density and turbine capacity factor corrections derived from these variations are also significant. In order to demonstrate this, the advanced Weather Research and Forecasting mesoscale Model (WRF) using data assimilation was executed in the study area to obtain a spatial representation of these corrections. According to the results, the wind power density, estimated by taking into account the air density correction, exhibits a difference of 8% between summer and winter, compared with that estimated without the density correction. This implies that seasonal capacity factor estimation corrections of up to 1% in percentage points are necessary for wind turbines mainly for summer and winter, due to air density changes.

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“…Recent studies have been trying to create alternative curves to increase accuracy in prediction and comprehension of production. In the literature there are studies creating curves adding more inputs, such as air density [9], humidity [10], wind direction [11], turbulence [12], and periods of the day [13]. Also machine learning has been largely used to predict wind power output, as shown in [14]- [20] and also to create a model of day-ahead prediction [21].…”

Section: Introductionmentioning

confidence: 99%

The Use of Machine Learning and Performance Concept to Monitor and Predict Wind Power Output

Sathler

Kolios

2022

2022 International Conference on Electrical, Computer and Energy Technologies (ICECET)

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Monitoring and predicting wind power output more precisely can be very beneficial for an increasingly competitive Wind Power industry. Although many advances have been made throughout the last decades, the production forecast is still based mainly on the manufacturing power curve and wind speed. Even though this approach is very useful, especially during the design phase, it does not consider other factors that affect production, such as topography, weather conditions, and wind features. A more precise prediction model that is able to recognize production fluctuation and is tailored using current operational data is proposed in this paper. The model analyzes the performance through Meteorological Mast Data (Met Mast Data) and then uses it as an input to monitor and predict power output. As a result, the model proposed achieves high accuracy and can be key to understanding the wind turbine asset's behavior throughout its lifespan, assisting operators in decision making to increase overall power production.

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The impact of humidity on performance of wind turbine

Cited by 20 publications

References 12 publications

The Consequences of Air Density Variations over Northeastern Scotland for Offshore Wind Energy Potential

The Consequences of Air Density Variations over Northeastern Scotland for Offshore Wind Energy Potential

Seasonal Correction of Offshore Wind Energy Potential due to Air Density: Case of the Iberian Peninsula

The Use of Machine Learning and Performance Concept to Monitor and Predict Wind Power Output

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