Abstract:Abstract-The knowledge of the topology of a wired network is often of fundamental importance. For instance, in the context of Power Line Communications (PLC) networks it is helpful to implement data routing strategies, while in power distribution networks and Smart Micro Grids (SMG) it is required for grid monitoring and for power flow management. In this paper, we use the transmission line theory to shed new light and to show how the topological properties of a wired network can be found exploiting admittance… Show more
“…Differently from [3], [4], both the distances between the PLC nodes and the topological information are learned in a single-step algorithm, which saves operational time and limits the error propagation. In particular, we rely on the theorem demonstrated in [5], which states:…”
Section: Approach and Resultsmentioning
confidence: 99%
“…For example, in the Cenelec A band the injected power is in the order of -15 dBm, while the PLC background noise ranges from -70 dBm to -90 dBm [6], so that the SNR is higher than 55 dB. Moreover, the admittance measurement noise is directly related to the classical voltage noise, so that the admittance-to-noise ratio (ANR) can be greater than the SNR, depending on the transmitter output admittance [5]. Hence, with a proper tuning of the transmitter admittance, the minimum ANR can reach values up to 85-90 dB.…”
“…Differently from [3], [4], both the distances between the PLC nodes and the topological information are learned in a single-step algorithm, which saves operational time and limits the error propagation. In particular, we rely on the theorem demonstrated in [5], which states:…”
Section: Approach and Resultsmentioning
confidence: 99%
“…For example, in the Cenelec A band the injected power is in the order of -15 dBm, while the PLC background noise ranges from -70 dBm to -90 dBm [6], so that the SNR is higher than 55 dB. Moreover, the admittance measurement noise is directly related to the classical voltage noise, so that the admittance-to-noise ratio (ANR) can be greater than the SNR, depending on the transmitter output admittance [5]. Hence, with a proper tuning of the transmitter admittance, the minimum ANR can reach values up to 85-90 dB.…”
“…Topology can be estimated by a node by exploiting measurements of load admittance [34], since the relation between distance and measured load admittance in another point of the network can be expressed in closed form. The measurable distance between nodes pertains to a range related to the frequency bands used for communication -e.g.…”
Smart Grids (SG) envision the exchange of both power and data, enabling system and customers to generate and transfer energy in a more efficient and balanced way. Among the relevant communication technologies, we find Power-Line Communications (PLC), which allow for data transmission on the electrical cables used for power delivery. Despite the hostile medium, PLC offer reliability and data rates to support exchange of control traffic, smart metering and polling applications. The distribution portion of the power delivery network, which we focus on in this work, is the most topologically complex, which makes channel prediction complicated. We show how random realistic topologies can be generated and then used to train a Machine Learning (ML) algorithm to infer PLC link quality (based on channel response) based solely on topology descriptors. We eventually show how precisely the communication quality can be inferred from the SG topology through ML. In doing so, we also discuss how the ML approach offers the common ground between top-down and bottom-up approaches for network characterization and how it enables smart decision making in the SG.
“…In other words, PLC transceivers can act as probes for grid diagnostics by analyzing the electromagnetic field and the data traffic in the PLC frequency bands [11]. Possible applications of PLC for grid sensing are topology reconstruction [138], [139] and anomalies detection, i.e., fault detection, cable aging, load identification [140]- [142]. The former application enables grid operators to better the knowledge about the grid configuration, the status of switches and feeders which is not always complete especially in the LV part of the access network.…”
A great deal of attention has been recently given to Machine Learning (ML) techniques in many different application fields. This paper provides a vision of what ML can do in Power Line Communications (PLC). We firstly and briefly describe classical formulations of ML, and distinguish deterministic from statistical learning models with relevance to communications. We then discuss ML applications in PLC for each layer, namely, for characterization and modeling, for the development of physical layer algorithms, for media access control and networking. Finally, other applications of PLC that can benefit from the usage of ML, as grid diagnostics, are analyzed. Illustrative numerical examples are reported to serve the purpose of validating the ideas and motivate future research endeavors in this stimulating signal/data processing field.
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