Dynamic Equivalent Modeling of Winds and Generators in Wind Farms based on Neural Regression and Clustering

As the size of wind farms and therefor the wind speed variety and number of generators is increased, it is of interest to work with equivalent models for wind and generators to avoid complexity in calculation and time consuming simulations. In this paper, an interval of wind inputs will be considered and with the suggestion of the neural regression and with the creation of its structure, it will be shown that how much the input winds affect the output power and its importance for feature space in the clustering, too. Normally, due to the complexity of dynamic relationship between output power and wind speed traditional regression methods become more complex. After finishing regression, with suggestion of a formula to calculate the entries of the feature space matrix, fuzzy clustering algorithm will be proposed and applied on the feature space. In each cluster the equivalent model for the wind is determined as well as the aggregated parameters are calculated based on specific formulas. The fuzzy clustering is not fallen easily in to local optimums. Strong regression as well as very closeness between equivalent and detailed models are shown as the benefits of using the proposed approach in this paper.