Power Line Communications (PLC) are the communication technology at the base of Smart Grid operation. They rely on the preexisting infrastructure that enables power delivery. To evaluate performance of this technology, usually the main metric that is looked at is the SNR at the receiver in a link. In order to predict this performance, different approaches can be used: bottom-up approaches implement physical models to employ characteristics of the medium to understand its channel response, while top-down ones focus on data from measurements to identify patterns and create stochastic models.Due to a hard-to-model noise and channel, these approaches come up short. In this work, we consider measurement data from Low Voltage distribution networks, we show how the classic SNR value relates to the network topology; additionally, we discuss how coverage in terms of distance from the central element of the network can be used as a performance indicator and how it relates to a novel, easy-to-compute density factor.
Data Analytics in G3-PLC Deployments for Coverage Prediction
Tonello A. M.;
2021-01-01
Abstract
Power Line Communications (PLC) are the communication technology at the base of Smart Grid operation. They rely on the preexisting infrastructure that enables power delivery. To evaluate performance of this technology, usually the main metric that is looked at is the SNR at the receiver in a link. In order to predict this performance, different approaches can be used: bottom-up approaches implement physical models to employ characteristics of the medium to understand its channel response, while top-down ones focus on data from measurements to identify patterns and create stochastic models.Due to a hard-to-model noise and channel, these approaches come up short. In this work, we consider measurement data from Low Voltage distribution networks, we show how the classic SNR value relates to the network topology; additionally, we discuss how coverage in terms of distance from the central element of the network can be used as a performance indicator and how it relates to a novel, easy-to-compute density factor.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
