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Wind power ramp event detection with a hybrid neuro-evolutionary approach

  • IWANN2017: Learning algorithms with real world applications
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A Correction to this article was published on 11 March 2019

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Abstract

In this paper, a hybrid system for wind power ramp events (WPREs) detection is proposed. The system is based on modeling the detection problem as a binary classification problem from atmospheric reanalysis data inputs. Specifically, a hybrid neuro-evolutionary algorithm is proposed, which combines artificial neural networks such as extreme learning machine (ELM), with evolutionary algorithms to optimize the trained models and carry out a feature selection on the input variables. The phenomenon under study occurs with a low probability, and for this reason the classification problem is quite unbalanced. Therefore, is necessary to resort to techniques focused on providing a balance in the classes, such as the synthetic minority over-sampling technique approach, the model applied in this work. The final model obtained is evaluated by a test set using both ELM and support vector machine algorithms, and its accuracy performance is analyzed. The proposed approach has been tested in a real problem of WPREs detection in three wind farms located in different areas of Spain, in order to see the spatial generalization of the method.

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Change history

  • 11 March 2019

    The article L. Cornejo-Bueno, C. Camacho-Gómez, A. Aybar-Ruiz, L. Prieto, A. Barea-Ropero, S. Salcedo-Sanz, “Wind power ramp event detection with a hybrid neuro-evolutionary approach,” Cornejo-Bueno, L., Camacho-Gómez, C., Aybar-Ruiz, A. et al. Neural Comput & Applic (2018).

Abbreviations

AUC:

Area under the curve

ARMA:

Autoregressive moving average

ANN:

Artificial neural networks

ELM:

Extreme learning machine

ECMWF:

European Centre for Medium-Range Weather Forecasts

KNN:

Nearest K-neighbors

ML:

Machine learning

MLPs:

Multi-layer perceptrons

NWM:

Numerical Weather Models

ROC:

Receiver operating characteristic

SDA:

Swinging door algorithm

SMOTE:

Synthetic minority over-sampling technique

SVM:

Support vector machine

SVR:

Support vector regression

WPREs:

Wind power ramp events

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Acknowledgements

This work has been partially supported by Comunidad de Madrid, under Project Number S2013/ICE-2933, by projects TIN2014-54583-C2-2-R and TIN2017-85887-C2-2-P of the Spanish Ministerial Commission of Science and Technology (MICYT). The authors acknowledge support by DAMA network TIM2015-70308-REDT.

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Authors and Affiliations

  1. Department of Signal Processing and Communications, Universidad de Alcalá, 28805, Alcalá de Henares, Madrid, Spain

    L. Cornejo-Bueno, C. Camacho-Gómez, A. Aybar-Ruiz, A. Barea-Ropero & S. Salcedo-Sanz

  2. Department of Forecast Systems, Iberdrola, Madrid, Spain

    L. Prieto

Authors
  1. L. Cornejo-Bueno
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  2. C. Camacho-Gómez
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  3. A. Aybar-Ruiz
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  4. L. Prieto
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  5. A. Barea-Ropero
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  6. S. Salcedo-Sanz
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Corresponding author

Correspondence to S. Salcedo-Sanz.

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Cornejo-Bueno, L., Camacho-Gómez, C., Aybar-Ruiz, A. et al. Wind power ramp event detection with a hybrid neuro-evolutionary approach. Neural Comput & Applic 32, 391–402 (2020). https://doi.org/10.1007/s00521-018-3707-7

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