Influence of the variation of meteorological and operational parameters on estimation of the power output of a wind farm with active power control

Díaz, Santiago; Carta, José A.; Castañeda, Alberto

doi:10.1016/j.renene.2020.05.187

Cited by 14 publications

(9 citation statements)

References 19 publications

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“…As a result, before using these data for further analysis, the first task is to filter them. Pre-processing of the raw data was done similarly to that outlined in [43]. The first step is to filter out samples with missing values or negative power values.…”

Section: Exploratory Scada Data Analysis and Filterationmentioning

confidence: 99%

“…Overall, criteria including timestamp missing, negative power values, out-of-range values, and turbine curtailment are utilised to filter out misleading data like that reported in [43,39]. Table 2 summarises a SCADA dataset that has a starting time-stamp of ''01/3/2012 00:00 PM" and ending at the timestamp of ''30/05/2012 00:00 PM".…”

Section: Exploratory Scada Data Analysis and Filterationmentioning

confidence: 99%

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Accounting for Environmental Conditions in Data-Driven Wind Turbine Power Models

Pandit

Infield

Peñas

2023

IEEE Trans. Sustain. Energy

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Continuous assessment of wind turbine performance is a key to maximising power generation at a very low cost. A wind turbine power curve is a non-linear function between power output and wind speed and is widely used to approach numerous problems linked to turbine operation. According to the current IEC standard, power curves are determined by a data reduction method, called binning, where hub height, wind speed and air density are considered as appropriate input parameters. However, as turbine rotors have grown in size over recent years, the impact of variations in wind speed, and thus of power output, can no longer be overlooked. Two environmental variables, namely wind shear and turbulence intensity, have the greatest impact on power output. Therefore, taking account of these factors may improve the accuracy as well as reduce the uncertainty of data-driven power curve models, which could be helpful in performance monitoring applications. This paper aims to quantify and analyse the impact of these two environmental factors on wind turbine power curves.Gaussian process (GP) is a data-driven, nonparametric based approach to power curve modelling that can incorporate these two additional environmental factors. The proposed technique's effectiveness is trained and validated using historical 10-minute average supervisory control and data acquisition (SCADA) datasets from variable speed, pitch control, and wind turbines rated at 2.5 MW. The results suggest that (i) the inclusion of the additional environmental parameters increases GP model accuracy and reduces uncertainty in estimating the power curve; (ii) a comparative study reveals that turbulence intensity has a relatively greater impact on GP model accuracy, together with uncertainty as compared to blade pitch angle. These conclusions are confirmed using performance error metrics and uncertainty calculations. The results have practical beneficial consequences for O&M related activities such as early failure detection.

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Section: Exploratory Scada Data Analysis and Filterationmentioning

confidence: 99%

Section: Exploratory Scada Data Analysis and Filterationmentioning

confidence: 99%

Accounting for Environmental Conditions in Data-Driven Wind Turbine Power Models

Pandit

Infield

Peñas

2023

IEEE Trans. Sustain. Energy

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“…For further reading of wind farm performance and effect of meteorological parameters on energy output, readers are encouraged to consult. 42,43 Saudi Arabia is going for onshore wind power capacity buildup…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the laterally staggered arrangements of wind turbines in a wind farm results in higher energy yield while vertically staggered placement causes noticeable velocity deficit reduction and ultimately the power output. For further reading of wind farm performance and effect of meteorological parameters on energy output, readers are encouraged to consult 42,43 …”

Section: Introductionmentioning

confidence: 99%

Wind power resources assessment at 10 different locations using wind measurements at five heights

Rehman

2022

Env Prog and Sustain Energy

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Globally, people from all walks of life are working towards a sustainable globe and the availability of energy to all human beings using clean, self-renewed, freely available, and distributive sources. Present study utilizes wind speed data measured at 40, 60, 80, 98, and 100 m above ground level and other meteorological parameters near ground level to analyze the wind characteristics, energy yield, cost of energy, and greenhouse gasses reduction at chosen locations. The data analysis showed an increasing trend of wind shear exponent with longitude and a decreasing trend of ambient temperature with latitude. The analysis recommends Yanbu North, Hafar Al-Batin, Sharourah, and Turaif sites with annual energy delivered to the grid of 213.5, 179.4, 167.1, and 166.7 GWh from a wind farm of 49.5 MW installed capacity as the first, second, third, and fourth choice for wind power deployment in the Kingdom of Saudi Arabia. On an average, 28.45 thousand tons CO 2 equivalent greenhouse gasses can be avoided from entering into the local atmosphere at each site with a maximum of 41.1 thousand tons at Yanbu North site alone.

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“…From the perspective of cumulative installed capacity, the European region is still the world's largest offshore wind power market [3]. However, the investment of the offshore wind power farms is still complex due to uncertain factors and high risks [4]. It is difficult to make investment plans for new offshore wind power farms.…”

Section: Introductionmentioning

confidence: 99%

Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net

Niu

et al. 2021

Sustainability

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Recently, countries around the world have begun to develop low-carbon energy sources to alleviate energy shortage and cope with climate change. The offshore wind power has become a new direction for clean energy exploration. However, the accuracy of offshore wind power investment is still an urgent problem due to its complexity. Therefore, this paper investigates offshore wind power investment to improve the investment forecasting accuracy. In this study, the random forest (RF) algorithm was used to screen out the key factors influencing multi-dimensional global offshore wind power investment, and the elastic net (EN) was optimized using the ADMM algorithm and used in the global offshore wind power investment forecast model. The results show that the adoption of the random forest algorithm can effectively screen out the key influencing factors of global offshore wind power investment. Water depth, offshore distance and sweeping area have the most influence on the investment. Moreover, compared with other models, the elastic net optimized by ADMM can better reflect the changing trend of global offshore wind power investment, with smaller errors and a higher regression accuracy. The application of the RF–EN combined model can screen out effective factors from complex multi-dimensional influencing factors, and perform high-precision regression analysis, which is conducive to improving the global offshore wind power investment forecast. The conclusion obtained can set a more reasonable plan for the future construction and investment of global offshore wind power projects.

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Influence of the variation of meteorological and operational parameters on estimation of the power output of a wind farm with active power control

Cited by 14 publications

References 19 publications

Accounting for Environmental Conditions in Data-Driven Wind Turbine Power Models

Accounting for Environmental Conditions in Data-Driven Wind Turbine Power Models

Wind power resources assessment at 10 different locations using wind measurements at five heights

Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net

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