Mean Variance Mapping Optimization for the identification of Gaussian Mixture Model: Test case

This paper presents an application of the Mean-Variance Mapping Optimization (MVMO) algorithm to the identification of the parameters of Gaussian Mixture Model (GMM) representing variability of power system loads. The advantage of this approach is that different types of load distributions can be fairly represented as a convex combination of several normal distributions with respective means and standard deviation. The problem of obtaining various mixture components (weight, mean, and standard deviation) is formulated as a problem of identification and MVMO is used to provide an efficient solution in this paper. The performance of the proposed approach is demonstrated using two tests. Results indicate the MVMO approach is efficient to represented load models.