1. Wind power ramp event detection with a hybrid neuro-evolutionary approach.
- Author
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Cornejo-Bueno, L., Camacho-Gómez, C., Aybar-Ruiz, A., Prieto, L., Barea-Ropero, A., and Salcedo-Sanz, S.
- Subjects
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WIND power , *SUPPORT vector machines , *HYBRID power systems , *ARTIFICIAL neural networks , *WIND power plants , *FEATURE selection , *EVOLUTIONARY algorithms - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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