Low-Voltage PLC Noise Modelling

Abstract

This paper models the PLC impulsive noise using a linear superposition of univariate Gaussian distributions where the Bayes’ theorem is used to find the posterior probabilities. The Gaussian mixture is formulated using discrete latent variables and modelled using two, three and four components in order to evaluate the effect of the number of components (Q). The parameters of the Gaussian mixture are then estimated using the maximum likelihood technique and the expectation-maximization algorithm. Regression analysis is proposed in order to solve the issue of singularity which is often present when the maximum likelihood approach is employed. The model is then validated through measurements where the impulsive noise is categorized into low, medium and highly impulsive depending on the amplitude of the indoor PLC noise. It is observed that as the number of components increases the performance of the Gaussian mixture model also increases as depicted by the correlation coefficient and RMSE. The χ2 test indicates that the proposed model provides a better fit as the PLC noise amplitude increases. In addition, the shape of the impulsive noise PDF becomes more defined with higher Q values. A singularity case is also examined where the Gaussian mixture model also provides a good approximation of the measured data.