Machine Learning for the Uncertainty Quantification of Power Networks

Abstract

This letter addresses the uncertainty quantification of a power network and is based on surrogate models built via Machine Learning techniques. Specifically, the least-square support vector machine regression is combined with the principal component analysis to generate a compressed surrogate model capable of predicting all the nodal voltages of the network as a function of the uncertain electrical parameters of the transmission lines. The surrogate model is built from a limited number of system responses provided by the computational model. The power flow analysis of the benchmark IEEE-118 bus system with 250 parameters is considered as a test case. The performance of the proposed modeling strategy in terms of accuracy, efficiency and convergence, are assessed and compared with those of an alternative surrogate model based on a sparse implementation of the polynomial chaos expansion. The results of a Monte Carlo simulation are used as reference in the above comparison