Characterization and Emulation of Low-Voltage Power Line Channels for Narrowband and Broadband Communication

The demand for smart grid and smart home applications has raised the recent interest in power line communication (PLC) technologies, and has driven a broad set of deep surveys in low-voltage (LV) power line channels. This book proposes a set of novel approaches, to characterize and to emulate LV power line channels in the frequency range from0.15to 10 MHz, which closes gaps between the traditional narrowband (up to 500 kHz) and broadband (above1.8 MHz) ranges

Characterization and Emulation of Low-Voltage Power Line Channels for Narrowband and Broadband Communication

The demand for smart grid and smart home applications has raised the recent interest in power line communication (PLC) technologies, and has driven a broad set of deep surveys in low-voltage (LV) power line channels. This book proposes a set of novel approaches, to characterize and to emulate LV power line channels in the frequency range from0.15to 10 MHz, which closes gaps between the traditional narrowband (up to 500 kHz) and broadband (above1.8 MHz) ranges

Transmission haut-débit sur les réseaux d'énergie: principes physiques et compatibilité électromagnétique

Power Line Communications consist of transmitting data by reusing the existing powerline as a propagation medium. Powerline networks represent a challenging environment for broadband communications, since they have not been designed for the transmission of high frequency signals. This Habilitation degree thesis presents our research on transmission physics and electromagnetic compatibility for in-home powerline networks. This research has been conducted since 2007 in the framework of a collaboration between Orange Labs and Telecom Bretagne, involving my supervision of three Ph.D. theses defended in 2012, 2013 and 2015, as the principal advisor.La technologie Courant Porteur en Ligne consiste à transmettre des données en réutilisant le réseau électrique classique en tant que support de propagation. Les réseaux d'énergie sont des environnements difficiles pour les communications à haut débit, car ils n'ont pas été conçus pour la transmission d'un signal à haute fréquence. Ce mémoire d'Habilitation à Diriger des Recherches présente mes travaux concernant la physique de la transmission et les aspects de Compatibilité Electro-Magnétique (CEM) pour le réseau électrique domestique. Ils ont été réalisés à partir de 2007 dans le cadre d'une collaboration entre Orange Labs et Telecom Bretagne, notamment à travers trois thèses soutenues en 2012, 2013 et 2015. Après une introduction générale à la technologie CPL, le manuscrit décrit l'environnement de propagation dans les réseaux d'énergie en termes de canal et de bruit électromagnétique. Les principes de la modélisation du canal CPL sont illustrés à partir de la problématique d'identification des trajets de propagation. L'une des principales évolutions du domaine concerne l'application de la technologie Multiple Input Multiple Output (MIMO) aux communications sur réseaux d'énergie. Nos études expérimentales ont démontré que l'adaptation de cette technique issue du domaine de la radio permet un doublement de la capacité de transmission. Nous présentons les campagnes de mesure réalisées au sein d'Orange Labs et du groupe Specialist Task Force 410 de l'ETSI. A partir de ces données, des modèles statistiques de canal de propagation MIMO et de bruit multi-capteurs ont été élaborés. En termes d'émission électromagnétique, la bande utilisée par les systèmes CPL est déjà occupée par d'autres services (radio amateur, radiodiffusion en ondes courtes). Nous décrivons les contraintes CEM des systèmes CPL et abordons les techniques de CEM cognitive, consistant à optimiser les ressources spectrales en tenant compte de la connaissance de l'environnement du système. En particulier, la technique de retournement temporel est étudiée pour la mitigation du rayonnement involontaire et sa performance est étudiée de manière expérimentale. Enfin, le manuscrit présente la problématique de l'efficacité énergétique des systèmes CPL. Nous présentons les mesures expérimentales réalisées afin de modéliser la consommation de modems classiques et MIMO. D'autre part, la configuration de communication en relais a été étudiée, afin d'évaluer le gain de ce mode de transmission en termes de consommation énergétique. A l'avenir, ces travaux pourront être étendus aux réseaux de distribution en basse et moyenne tension, pour le développement et l'optimisation des réseaux d'énergie intelligents, ou Smart Grids

Characterization and Emulation of Low-Voltage Power Line Channels for Narrowband and Broadband Communication

This thesis proposes a set of novel approaches to characterize and to emulate LV power line channels in the frequency range from 0.15 to 10MHz, which close gaps between the traditional narrowband (up to 500 kHz) and broadband (above 1.8MHz) ranges

Information Technology, Artificial Intelligence and Machine Learning in Smart Grid – Performance Comparison between Topology Identification Methodology and Neural Network Identification Methodology for the Branch Number Approximation of Overhead Low-Voltage Broadband over Power Lines Network Topologies

Broadband over Power Lines (BPL) networks that are deployed across the smart grid can benefit from the usage of machine learning, as smarter grid diagnostics are collected and analyzed. In this paper, the neural network identification methodology of Overhead Low-Voltage (OV LV) BPL networks that aims at identifying the number of branches for a given OV LV BPL topology channel attenuation behavior is proposed, which is simply denoted as NNIM-BNI. In order to identify the branch number of an OV LV BPL topology through its channel attenuation behavior, NNIM-BNI exploits the Deterministic Hybrid Model (DHM), which has been extensively tested in OV LV BPL networks for their channel attenuation determination, and the OV LV BPL topology database of Topology Identification Methodology (TIM). The results of NNIM-BNI towards the branch number identification of OV LV BPL topologies are compared against the ones of a newly proposed TIM-based methodology, denoted as TIM-BNI.Citation: Lazaropoulos, A. G. (2021). Information Technology, Artificial Intelligence and Machine Learning in Smart Grid-Performance Comparison between Topology Identification Methodology and Neural Network Identification Methodology for the Branch Number Approximation of Overhead Low-Voltage Broadband over Power Lines Network Topologies. Trends in Renewable Energy, 7, 87-113. DOI: 10.17737/tre.2021.7.1.0013

Impulsive noise cancellation and channel estimation in power line communication systems

Power line communication (PLC) is considered as the most viable enabler of the smart grid. PLC exploits the power line infrastructure for data transmission and provides an economical communication backbone to support the requirements of smart grid applications. Though PLC brings a lot of benefits to the smart grid implementation, impairments such as frequency selective attenuation of the high-frequency communication signal, the presence of impulsive noise (IN) and the narrowband interference (NBI) from closely operating wireless communication systems, make the power line a hostile environament for reliable data transmission. Hence, the main objective of this dissertation is to design signal processing algorithms that are specifically tailored to overcome the inevitable impairments in the power line environment. First, we propose a novel IN mitigation scheme for PLC systems. The proposed scheme actively estimates the locations of IN samples and eliminates the effect of IN only from the contaminated samples of the received signal. By doing so, the typical problem encountered while mitigating the IN is avoided by using passive IN power suppression algorithms, where samples besides the ones containing the IN are also affected creating additional distortion in the received signal. Apart from the IN, the PLC transmission is also impaired by NBI. Exploiting the duality of the problem where the IN is impulsive in the time domain and the NBI is impulsive in the frequency domain, an extended IN mitigation algorithm is proposed in order to accurately estimate and effectively cancel both impairments from the received signal. The numerical validation of the proposed schemes shows improved BER performance of PLC systems in the presence of IN and NBI. Secondly, we pay attention to the problem of channel estimation in the power line environment. The presence of IN makes channel estimation challenging for PLC systems. To accurately estimate the channel, two maximumlikelihood (ML) channel estimators for PLC systems are proposed in this thesis. Both ML estimators exploit the estimated IN samples to determine the channel coefficients. Among the proposed channel estimators, one treats the estimated IN as a deterministic quantity, and the other assumes that the estimated IN is a random quantity. The performance of both estimators is analyzed and numerically evaluated to show the superiority of the proposed estimators in comparison to conventional channel estimation strategies in the presence of IN. Furthermore, between the two proposed estimators, the one that is based on the random approach outperforms the deterministic one in all typical PLC scenarios. However, the deterministic approach based estimator can perform consistent channel estimation regardless of the IN behavior with less computational effort and becomes an efficient channel estimation strategy in situations where high computational complexity cannot be afforded. Finally, we propose two ML algorithms to perform a precise IN support detection. The proposed algorithms perform a greedy search of the samples in the received signal that are contaminated by IN. To design such algorithms, statistics defined for deterministic and random ML channel estimators are exploited and two multiple hypothesis tests are built according to Bonferroni and Benjamini and Hochberg design criteria. Among the proposed estimators, the random ML-based approach outperforms the deterministic ML-based approach while detecting the IN support in typical power line environment. Hence, this thesis studies the power line environment for reliable data transmission to support smart grid. The proposed signal processing schemes are robust and allow PLC systems to effectively overcome the major impairments in an active electrical network.The efficient mitigation of IN and NBI and accurate estimation of channel enhances the applicability of PLC to support critical applications that are envisioned for the future electrical power grid.La comunicación a través de líneas de transmisión eléctricas (PLC) se considera uno de los habilitadores principales de la red eléctrica inteligente (smart grid). PLC explota la infraestructura de la red eléctrica para la transmisión de datos y proporciona una red troncal de comunicación económica para poder cumplir con los requisitos de las aplicaciones para smart grids. Si bien la tecnología PLC aporta muchos beneficios a la implementación de la smart grid, los impedimentos, como la atenuación selectiva en frecuencia de la señal de comunicación, la presencia de ruido impulsivo (IN) y las interferencias de banda estrecha (NBI) de los sistemas de comunicación inalámbrica de operación cercana, hacen que la red eléctrica sea un entorno hostil para la transmisión fiable de datos. En este contexto, el objetivo principal de esta tesis es diseñar algoritmos de procesado de señal que estén específicamente diseñados para superar los impedimentos inevitables en el entorno de la red eléctrica como son IN y NBI. Primeramente, proponemos un nuevo esquema de mitigación de IN en sistemas PLC. El esquema propuesto estima activamente las ubicaciones de las muestras de IN y elimina el efecto de IN solo en las muestras contaminadas de la señal recibida. Al hacerlo, el problema típico que se encuentra al mitigar el IN con técnicas tradicionales (donde también se ven afectadas otras muestras que contienen la IN, creando una distorsión adicional en la señal recibida) se puede evitar con la consiguiente mejora del rendimiento. Aparte de IN, los sistemas PLC también se ven afectados por el NBI. Aprovechando la dualidad del problema (el IN es impulsivo en el dominio del tiempo y el NBI es impulsivo en el dominio de la frecuencia), se propone un algoritmo de mitigación de IN ampliado para estimar con precisión y cancelar efectivamente ambas degradaciones de la señal recibida. La validación numérica de los esquemas propuestos muestra un mejor rendimiento en términos de tasa de error de bit (BER) en sistemas PLC con presencia de IN y NBI. En segundo lugar, prestamos atención al problema de la estimación de canal en entornos PLC. La presencia de IN hace que la estimación de canal sea un desafío para los sistemas PLC futuros. En esta tesis, se proponen dos estimadores de canal para sistemas PLC de máxima verosimilitud (ML) para sistemas PLC. Ambos estimadores ML explotan las muestras IN estimadas para determinar los coeficientes del canal. Entre los estimadores de canal propuestos, uno trata la IN estimada como una cantidad determinista, y la otra asume que la IN estimada es una cantidad aleatoria. El rendimiento de ambos estimadores se analiza y se evalúa numéricamente para mostrar la superioridad de los estimadores propuestos en comparación con las estrategias de estimación de canales convencionales en presencia de IN. Además, entre los dos estimadores propuestos, el que se basa en el enfoque aleatorio supera el determinista en escenarios PLC típicos. Sin embargo, el estimador basado en el enfoque determinista puede llevar a cabo una estimación de canal consistente independientemente del comportamiento de la IN con menos esfuerzo computacional y se convierte en una estrategia de estimación de canal eficiente en situaciones donde no es posible disponer de una alta complejidad computacionalPostprint (published version