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

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.