Statistical Representation of Distribution System Loads Using Gaussian Mixture Model

This paper presents a probabilistic approach for statistical modeling of the loads in distribution networks. In a distribution network, the probability density functions (pdfs) of loads at different buses show a number of variations and cannot be represented by any specific distribution. The approach presented in this paper represents all the load pdfs through Gaussian mixture model (GMM). The expectation maximization (EM) algorithm is used to obtain the parameters of the mixture components. The performance of the method is demonstrated on a 95-bus generic distribution network model.