Identification of Gaussian mixture model using Mean Variance Mapping Optimization: Venezuelan case

The characterization of random load behavior has been largely attempted through statistics-based model fitting. Remarkably, the use of Gaussian mixture model (GMM) has proven to be adequate to tackle the heterogeneity and variability of the statistical distribution of loads. In this paper, an application of the Mean-Variance Mapping Optimization (MVMO) algorithm to the identification of the parameters of GMMs, is presented. The feasibility of the proposed identification approach is demonstrated using historical data records from the Venezuelan transmission system portion that covers the Paraguaná Peninsula.