Inferring Power Grid Information with Power Line Communications: Review and Insights

High-frequency signals were widely studied in the last decade to identify grid and channel conditions in PLNs. PLMs operating on the grid's physical layer are capable of transmitting such signals to infer information about the grid. Hence, PLC is a suitable communication technology for SG applications, especially suited for grid monitoring and surveillance. In this paper, we provide several contributions: 1) a classification of PLC-based applications; 2) a taxonomy of the related methodologies; 3) a review of the literature in the area of PLC Grid Information Inference (GII); and, insights that can be leveraged to further advance the field. We found research contributions addressing PLMs for three main PLC-GII applications: topology inference, anomaly detection, and physical layer key generation. In addition, various PLC-GII measurement, processing, and analysis approaches were found to provide distinctive features in measurement resolution, computation complexity, and analysis accuracy. We utilize the outcome of our review to shed light on the current limitations of the research contributions and suggest future research directions in this field.Comment: IEEE Communication Surveys and Tutorials Journa

On the Exploitation of Admittance Measurements for Wired Network Topology Derivation

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 measurements at the nodes. An analytic proof is reported to show that the derivation of the topology can be done in complex networks under certain assumptions. We also analyze the effect of the network background noise on admittance measurements. In this respect, we propose a topology derivation algorithm that works in the presence of noise. We finally analyze the performance of the algorithm using values that are typical of power line distribution networks.Comment: A version of this manuscript has been submitted to the IEEE Transactions on Instrumentation and Measurement for possible publication. The paper consists of 8 pages, 11 figures, 1 tabl

Dynamic Topology Estimation and Resource Allocation for Power Line Communication

Power line communication (PLC), which uses existing infrastructure of power delivery for data transfer, is regarded as an economical, pervasive and extensive communication solution for smart grid and home broadband applications. One of the challenges of applying communication technologies to power line network lies in acquirement of channel state information (CSI), which is dependent on network topology. Moreover, the knowledge of topology provides a basis for the design of routing protocols and power flow optimization. Therefore, efficient approaches for dynamic topology estimation are highly demanded. While dynamic routing and resource allocation enable high-speed and multi-tasking communication services over power lines. In this thesis, a dynamic topology estimation scheme for PLC is investigated, and a cross-layer routing and resource allocation scheme assisted by dynamic topology estimation is developed to improve the system performance. In the first contribution, a high-resolution and low-complexity dynamic topology estimation scheme for time-varying indoor PLC networks is proposed. The scheme consists of three parts: a) a time-frequency domain reflectometry (TFDR) based path length estimation method, which requires measurement at a single PLC modem and achieves a much higher resolution than the frequency domain reflectometry (FDR) based method; b) a node-by-node greedy algorithm for topology reconstruction, which is much more computationally efficient than the existing peak-by-peak searching algorithm; c) an impulsive noise assisted dynamic topology re-estimation method, which results in a significant complexity reduction over fixed-frequency re-estimation. In the second contribution, a cross-layer routing and resource allocation (RA) scheme assisted by dynamic topology estimation is proposed to optimize the system throughput of indoor PLC network with heterogeneous delay requirements. The proposed scheme provides a multi-layer solution, which conducts the network layer routing based on the result of PHY layer resource allocation which is constrained by the MAC layer queuing delay. With the dynamic topology estimation proposed in the first contribution, the routing can be solved centrally at the source, which is more robust against topology changes compared to distributed solutions. The proposed cross-layer RA scheme consists of subcarrier allocation (SA) to multiple users and power allocation (PA) to subcarriers satisfying heterogeneous delay requirements. It is demonstrated that the proposed centralized routing strategy achieves a much lower packet loss rate (PLR) than a distributed routing scheme; while with optimal RA, the system throughput is significantly improved compared to the routing schemes without considering RA

Detection and Classification of Fault Types in Distribution Lines by Applying Contrastive Learning to GAN Encoded Time-Series of Pulse Reflectometry Signals

T This study proposes a new method for detecting and classifying faults in distribution lines. The physical principle of classification is based on time-domain pulse reflectometry (TDR). These high-frequency pulses are injected into the line, propagate through all of its bifurcations, and are reflected back to the injection point. According to the impedances encountered along the way, these signals carry information regarding the state of the line. In the present work, an initial signal database was obtained using the TDR technique, simulating a real distribution line using (PSCADTM). By transforming these signals into images and reducing their dimensionality, these signals are processed using convolutional neural networks (CNN). In particular, in this study, contrastive learning in Siamese networks was used for the classification of different types of faults (ToF). In addition, to avoid the problem of overfitting owing to the scarcity of examples, generative adversarial neural networks (GAN) have been used to synthesise new examples, enlarging the initial database. The combination of Siamese neural networks and GAN allows the classification of this type of signal using only synthesised examples to train and validate and only the original examples to test the network. This solves the problem of the lack of original examples in this type of signal of natural phenomena which are difficult to obtain and simulate