volume | PIER Journals

Abstract

This paper examines the use of extreme value theory (EVT) in modelling and forecasting extreme noise events in Power Line Communication (PLC) networks. PLC noise is characterised by random, high-amplitude noise spikes that significantly degrade PLC performance. As such, EVT, which is a branch of statistics that is concerned with modelling and analysing extreme deviations of random processes, is particularly useful for modelling PLC noise impulsive noise events, which are random since it focuses on the tail behaviour of the noise distribution. In this proposed EVT analysis, the probability of extreme noise events is estimated from the high-amplitude spikes. The heavy-tailed characteristics of PLC noise are estimated by the shape parameter (ξ) to model impulsive noise distributions, and the Block Maxima (BM) approach is employed to handle the worst-case PLC noise events lasting over long periods, consequently estimating the maximum expected noise over time. Lastly, the peaks over threshold (POT) method is proposed to handle the threshold exceedance probability, which can be used for threshold selection for PLC noise suppression.

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Dr. Steven O. Awino

Dr. Bakhe Nleya