Application of the Cuckoo Search in the Adjustment of Weibull Curves for Wind Energy Using Wind Data of Petrolina City

Andrade, Carla Freitas de; Veiga, Fernanda Rocha; Macedo, Marcus Vinicius Silveira; Santos, Lindenberg Ferreira dos; Rocha, Paulo Alexandre Costa; Guedes, Kevin Santos

doi:10.1109/tla.2018.8795130

Cited by 2 publications

(5 citation statements)

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“…The power extracted by a turbine is proportional to the wind speed cubed, which demands an assertive analysis of the wind speed data to avoid errors even higher relative to power generation. Usually, the 2-parameter Weibull distribution function is applied to estimate the wind speed distribution over a period (Freitas de Andrade et al, 2018; Karthikeya et al, 2016; Shu et al, 2016). The Weibull probability density function and the cumulative distribution function are provided by…”

Section: Methodsmentioning

confidence: 99%

“…RMSE is used to calculate the difference between calculated and observed data. According to Freitas de Andrade et al (2018), the RMSE value for wind speed data analysis can be performed between the values of the calculated distribution functions ( f ( u ) cal ) and the measured values ( f ( u ) med ) , given by (A.10): where n is the sample number. The lower the RMSE value, the better the agreement between the measured and calculated data, small RMSE values indicate less dispersion between the data.…”

Section: Maximum Likelihood Estimation (Mle)mentioning

confidence: 99%

“…The Empirical Method was used by Freitas de Andrade et al (2018), which requires the mean and standard deviation of wind speed to be available. Thus, the shape parameter is obtained by (A.6):…”

Section: Maximum Likelihood Estimation (Mle)mentioning

confidence: 99%

Section: Maximum Likelihood Estimation (Mle)mentioning

confidence: 99%

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Assessment of wind resource considering local turbulence based on data acquisition with SODAR

et al. 2023

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This work presents a new methodology to evaluate the influence of wind speed data corrections in the fit of the Weibull distribution. Corrections are made for data measured by Sonic Detection and Ranging (SODAR) and MERRA-2 base data. SODAR data are corrected through Turbulence Intensity (TI). The MERRA-2 data correction uses National Institute of Meteorology (INMET) weather station data to find a local scale factor. The results showed that the corrected data present a better fit in the Weibull distribution and evidence that corrections are necessary when wind speed averages are used to evaluate the wind resource. Wind speed data were also applied to simulate the energy production by a commercial turbine to demonstrate the contrast in the total energy generated. The new methodology shows that IT must be considered in the evaluation of wind resources.

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Section: Methodsmentioning

confidence: 99%

Section: Maximum Likelihood Estimation (Mle)mentioning

confidence: 99%

Section: Maximum Likelihood Estimation (Mle)mentioning

confidence: 99%

Section: Maximum Likelihood Estimation (Mle)mentioning

confidence: 99%

See 2 more Smart Citations

Assessment of wind resource considering local turbulence based on data acquisition with SODAR

et al. 2023

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“…Another option for parametric estimation is the so-called population-based metaheuristic algorithms, such as the research presented in [ 24 , 25 , 26 ]. The authors use the Cuckoo Search Algorithm to estimate different types of physical systems.…”

Section: Introductionmentioning

confidence: 99%

Sensorless Estimation Based on Neural Networks Trained with the Dynamic Response Points

Rodríguez-Abreo

Velásquez

Paz

et al. 2021

Sensors

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In the present work, a neuronal dynamic response prediction system is shown to estimate the response of multiple systems remotely without sensors. For this, a set of Neural Networks and the response to the step of a stable system is used. Six basic characteristics of the dynamic response were extracted and used to calculate a Transfer Function equivalent to the dynamic model. A database with 1,500,000 data points was created to train the network system with the basic characteristics of the dynamic response and the Transfer Function that causes it. The contribution of this work lies in the use of Neural Network systems to estimate the behavior of any stable system, which has multiple advantages compared to typical linear regression techniques since, although the training process is offline, the estimation can perform in real time. The results show an average 2% MSE error for the set of networks. In addition, the system was tested with physical systems to observe the performance with practical examples, achieving a precise estimation of the output with an error of less than 1% for simulated systems and high performance in real signals with the typical noise associated due to the acquisition system.

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Application of the Cuckoo Search in the Adjustment of Weibull Curves for Wind Energy Using Wind Data of Petrolina City

Cited by 2 publications

References 0 publications

Assessment of wind resource considering local turbulence based on data acquisition with SODAR

Assessment of wind resource considering local turbulence based on data acquisition with SODAR

Sensorless Estimation Based on Neural Networks Trained with the Dynamic Response Points

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