Modeling and Characterization of Low Voltage Access Network for Narrowband Powerline Communications

Abstract

-Nowadays, Power Line Communication (PLC) is gaining high attention from industry and electric supply companies for the services like demand response, demand side management and Advanced Metering Infrastructure (AMI). The reliable services to consumers using PLC can be provided by utilizing an efficient PLC channel for which sophisticated channel modeling is very important. This paper presents characterization of a Low Voltage (LV) access network for Narrowband Power Line Communications (NB-PLC) using transmission line (TL) theory and a Simulink model. The TL theory analysis not only includes the constant parameters but frequency selectivity is also introduced in these parameters such as resistance, conductance and impedances. However, the proposed Simulink channel model offers an analysis and characterization of capacitive coupler, network impedance and channel transfer function for NB-PLC. Analysis of analytical and simulated results shows a close agreement of the channel transfer function. In the absence of a standardized NB-PLC channel model, this research work can prove significant in improving the efficiency and accuracy of NB-PLC communication transceivers for Smart Grid communications.

Sensitivity-Informed Bayesian Inference for Home PLC Network Models with Unknown Parameters

“…The wires between the service panel and junction boxes are randomly generated with radii in the range [1.03, 2.06] mm (corresponding to wire gauge size between 12 and 6) and have length in the range [5,20] m. The wires in the rooms are randomly generated with radii in the range [0.81, 1.29] mm (corresponding to wire gauge size between 10 and 14) and have length in the range [2,5] m.…”

“…The pushed-forward posterior does not account for the discrepancy between the data d and the model predictions, subsumed into the definition of the likelihood function in (15). The Bayesian posterior-predictive distribution, defined in (20), is computed by marginalization of the likelihood over the posterior distribution of model parameters θ:…”

“…On the other hand, the bottom-up approach is computationally complex, and it cannot account directly for unknown network components [14]. There have been many bottom-up network models for different applications and of varying complexity [9,10,[16][17][18][19][20]. A recent review of contributions to PLC modeling is provided by Masood et al [7].…”