Power Performance Verification of a Wind Farm Using the Friedman’s Test

Hernández, Wilmar; López-Presa, José Luis; Maldonado, Jorge Luis

doi:10.3390/s16060816

Cited by 15 publications

(10 citation statements)

References 34 publications

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“…This turbine is placed in the Villonaco Wind Farm (VWF), Loja, Ecuador [7,8,9,27]. The Universal Transverse Mercator (UTM) coordinate system of this wind turbine is: 693.035 East and 9558.399 North.…”

Section: Data and Methods Used For The Estimation Of The Power Curvementioning

confidence: 99%

“…As was previously mentioned, the time measurement interval was from 1 October 2014 to 31 December 2014, the information about the wind speed and power output was taken from the Goldwind SCADA system, the hub height was equal to 65 m, and the sampling interval was equal to 10 min [7,8,9,31]. Figure 3 shows the scatter plot of the power output against the wind speed (i.e., the power curve) identifying some of the points (

p_{i}

v_{i}

) that are affected by any anomaly.…”

Section: Data and Methods Used For The Estimation Of The Power Curvementioning

confidence: 99%

“…In previous research works, the frequency response of a wind turbine under assessment was obtained [7], and novel methods of verifying the power performance of both a wind turbine and a wind farm were presented in [8,9]. …”

Section: Introductionmentioning

confidence: 99%

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Modeling of a Robust Confidence Band for the Power Curve of a Wind Turbine

Hernández

Méndez

Maldonado

et al. 2016

Sensors

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Having an accurate model of the power curve of a wind turbine allows us to better monitor its operation and planning of storage capacity. Since wind speed and direction is of a highly stochastic nature, the forecasting of the power generated by the wind turbine is of the same nature as well. In this paper, a method for obtaining a robust confidence band containing the power curve of a wind turbine under test conditions is presented. Here, the confidence band is bound by two curves which are estimated using parametric statistical inference techniques. However, the observations that are used for carrying out the statistical analysis are obtained by using the binning method, and in each bin, the outliers are eliminated by using a censorship process based on robust statistical techniques. Then, the observations that are not outliers are divided into observation sets. Finally, both the power curve of the wind turbine and the two curves that define the robust confidence band are estimated using each of the previously mentioned observation sets.

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Section: Data and Methods Used For The Estimation Of The Power Curvementioning

confidence: 99%

p_{i}

v_{i}

) that are affected by any anomaly.…”

Section: Data and Methods Used For The Estimation Of The Power Curvementioning

confidence: 99%

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Modeling of a Robust Confidence Band for the Power Curve of a Wind Turbine

Hernández

Méndez

Maldonado

et al. 2016

Sensors

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“…This information was stored in 12 groups, six groups consisting of samples of the random variables under study and six groups consisting of the six categories of contamination levels. Next, Friedman's test [38], [40], [49] was used to detect significant differences, at the α = 0.01 significance level, between the samples and the intervals established by QIAQ for the classifications. Figs.…”

Section: Classification According To Air Pollution By Pm 25 Concementioning

confidence: 99%

Analysis of the Information Obtained From PM_2.5 Concentration Measurements in an Urban Park

Hernández

Méndez

Žalakevičiūtė

et al. 2020

IEEE Trans. Instrum. Meas.

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In this article, parametric and nonparametric statistical inference analysis of a set the measurements of air pollution because of PM 2.5 concentrations was performed. The research work was carried out in an urban park in Quito, Ecuador. Specifically, the park that was chosen to perform the analysis was La Carolina Park. The analysis carried out here was aimed at obtaining the statistical models for parts of this urban park under study and some of its border streets. Furthermore, the park and its border streets were modeled as random variables that were finally classified according to the amount of PM 2.5 concentration levels they carry. This classification was performed by using a method based on both Friedman's test and the categories of the index of air quality of Quito. The results of this article showed that air pollution levels because of PM 2.5 concentrations in La Carolina Park are not in alert level. The worst case, considering the analysis tools used in this article, is that one of the streets that border the park is in caution level. The other streets and parts of the park that were analyzed are either in a desirable level or in an acceptable level. Furthermore, in this article, it has been shown that as pedestrian and temporary residents move further away from the trees and vegetation of the park, the level of exposure to PM 2.5 concentrations that they experience is higher.

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“…The Villonaco Wind Power Plant (VWPP) is located in a mountainous region of Loja province in southern Ecuador, between Universal Transverse Mercator (UTM) coordinates 693030 E and 9556476 N (see Figure 1). (Hernandez et al, 2016). Moreover, it has an installed power of 16.5 MW and is located in a mountainous terrain complex at approximately 2720 masl, in an area with average annual wind speeds above 10.5 m/s and high orographic complexity (Hernandez et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Wind power forecasting: A systematic literature review

2019

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Accurate and reliable prediction of wind energy in the short term is of great importance for the efficient operation of wind farms. One of the procedures to search for, summarize, organize and synthesize existing information is a systematic literature review. In this article, we present a systematic literature review on the predictive models of wind energy, aiming to establish the baseline for the development of a short-term wind energy prediction model that employs artificial intelligence tools to be applied in the Villonaco Wind Power Plant. Following a systematic method of literature review, we have established 4 research questions and 37 scientific articles that answer the said questions. Consequently, we found that artificial neural networks are used more frequently for the prediction of wind energy, which highlights its use in the studies consulted for the results achieved compared with that of other methods.

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Power Performance Verification of a Wind Farm Using the Friedman’s Test

Cited by 15 publications

References 34 publications

Modeling of a Robust Confidence Band for the Power Curve of a Wind Turbine

Modeling of a Robust Confidence Band for the Power Curve of a Wind Turbine

Analysis of the Information Obtained From PM_2.5 Concentration Measurements in an Urban Park

Wind power forecasting: A systematic literature review

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Power Performance Verification of a Wind Farm Using the Friedman’s Test

Cited by 15 publications

References 34 publications

Modeling of a Robust Confidence Band for the Power Curve of a Wind Turbine

Modeling of a Robust Confidence Band for the Power Curve of a Wind Turbine

Analysis of the Information Obtained From PM2.5 Concentration Measurements in an Urban Park

Wind power forecasting: A systematic literature review

Contact Info

Product

Resources

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Analysis of the Information Obtained From PM_2.5 Concentration Measurements in an Urban Park