Wind Turbine Power Curves Based on the Weibull Cumulative Distribution Function

Bokde, Neeraj Dhanraj; Feijóo, Andrés; Villanueva, Daniel

doi:10.3390/app8101757

Cited by 33 publications

(16 citation statements)

References 19 publications

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“…Weibull distributions are widely used [4,5]. Therefore, in this study, we assume the lifetime of the on-orbit component follows the Weibull distribution, denoted by W(λ, β), with the CDF [6] F(t; λ, β) = 1 − e −λt β , t ≥ 0, λ > 0, β > 0.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Estimation of Residual Life for Weibull-Distributed Components of On-Orbit Satellites Based on Multi-Source Information Fusion

2019

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Residual life estimation is an important problem in reliability engineering. Traditional methods, which are based on time-to-failure distribution, have limitations for components of on-orbit satellites characterized as high reliability with small sample size. Various types of reliability information can be collected during test and operation, including historical lifetime data, degradation data, similar data, expert information, etc. Therefore, making full use of multi-source information is meaningful for improving estimation precision. However, research on residual life estimation by fusing multi-source information is rare. No study has examined the overall process of fusing all of the different kinds of information. In this paper, a Bayesian method is presented to estimate the residual life of Weibull-distributed components of on-orbit satellites by fusing all the collected information. Prior distributions are determined using different kinds of information. After fusing the field data, posterior distributions can be obtained corresponding to each prior distribution. Then, the joint posterior distribution is the weighted sum of these posterior distributions with weights calculated using the second Maximum Likelihood Estimation (ML-II) method. Consistency is tested to guarantee the safety of the information fusion. Furthermore, residual life is estimated by the proposed sample-based method including both the Bayesian estimate and credible interval (CI). A Monte Carlo simulation study is conducted to demonstrate the proposed methods and shows that the Bayesian method is satisfactory and robust. Finally, a published dataset of the momentum wheel in a satellite is analyzed to illustrate the application of the method.

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Section: Introductionmentioning

confidence: 99%

Bayesian Estimation of Residual Life for Weibull-Distributed Components of On-Orbit Satellites Based on Multi-Source Information Fusion

2019

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“…In the paper "Numerical and Experimental Methods for the Assessment of Wind Turbine Control Upgrades" [7], the impacts of wind turbine control system upgrade have been studied numerically with operational data; it may be used as a tool to evaluate the cost effectiveness of wind turbine control system improvement. The paper "Wind Turbine Power Curves Based on the Weibull Cumulative Distribution Function" has discussed the representation of a wind turbine power curve by means of the cumulative distribution function (CDF) of a Weibull distribution [8]. The Weibull functions are used to represent the relationship between wind speed and wind power, and therefore for modelling the wind turbine power characteristic.…”

Section: Wind Turbine Systemsmentioning

confidence: 99%

Special Issue on “Wind Energy Conversion Systems”

Chen

2019

Applied Sciences

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A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: describe briefly the main methods or treatments applied; (3) Results: summarize the article’s main findings; and (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article; it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.

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“…It is dependent on various climate variables such as wind direction, wind speed, air temperature, turbines and its physical characteristics [41]. The forecast of wind power indirectly by forecasting wind speed data and then transforming it to wind power with the help of the power curve, which indicates the cubic relation between wind speed and power [42][43][44][45]. Actually, wind power is a function of the cube of the wind speed [44].…”

Section: Introductionmentioning

confidence: 99%

The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling

et al. 2020

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In this research, two hybrid intelligent models are proposed for prediction accuracy enhancement for wind speed and power modeling. The established models are based on the hybridisation of Ensemble Empirical Mode Decomposition (EEMD) with a Pattern Sequence-based Forecasting (PSF) model and the integration of EEMD-PSF with Autoregressive Integrated Moving Average (ARIMA) model. In both models (i.e., EEMD-PSF and EEMD-PSF-ARIMA), the EEMD method is used to decompose the time-series into a set of sub-series and the forecasting of each sub-series is initiated by respective prediction models. In the EEMD-PSF model, all sub-series are predicted using the PSF model, whereas in the EEMD-PSF-ARIMA model, the sub-series with high and low frequencies are predicted using PSF and ARIMA, respectively. The selection of the PSF or ARIMA models for the prediction process is dependent on the time-series characteristics of the decomposed series obtained with the EEMD method. The proposed models are examined for predicting wind speed and wind power time-series at Maharashtra state, India. In case of short-term wind power time-series prediction, both proposed methods have shown at least 18.03 and 14.78 percentage improvement in forecast accuracy in terms of root mean square error (RMSE) as compared to contemporary methods considered in this study for direct and iterated strategies, respectively. Similarly, for wind speed data, those improvement observed to be 20.00 and 23.80 percentages, respectively. These attained prediction results evidenced the potential of the proposed models for the wind speed and wind power forecasting. The current proposed methodology is transformed into R package ‘decomposedPSF’ which is discussed in the Appendix.

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Wind Turbine Power Curves Based on the Weibull Cumulative Distribution Function

Cited by 33 publications

References 19 publications

Bayesian Estimation of Residual Life for Weibull-Distributed Components of On-Orbit Satellites Based on Multi-Source Information Fusion

Bayesian Estimation of Residual Life for Weibull-Distributed Components of On-Orbit Satellites Based on Multi-Source Information Fusion

Special Issue on “Wind Energy Conversion Systems”

The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling

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