Classification and modeling of power line noise using machine learning techniques

A thesis submitted in ful lment of the requirements for the degree of Doctor of Philosophy in the School of Electrical and Information Engineering Faculty of Engineering and Built Environment June 2017The realization of robust, reliable and e cient data transmission have been the theme of recent research, most importantly in real channel such as the noisy, fading prone power line communication (PLC) channel. The focus is to exploit old techniques or create new techniques capable of improving the transmission reliability and also increasing the transmission capacity of the real communication channels. Multi-carrier modulation scheme such as Orthogonal Frequency Division Multiplexing (OFDM) utilizing conventional single-carrier modulation is developed to facilitate a robust data transmission, increasing transmission capacity (e cient bandwidth usage) and further reducing design complexity in PLC systems. On the contrary, the reliability of data transmission is subjected to several inhibiting factors as a result of the varying nature of the PLC channel. These inhibiting factors include noise, perturbation and disturbances. Contrary to the Additive White Gaussian noise (AWGN) model often assumed in several communication systems, this noise model fails to capture the attributes of noise encountered on the PLC channel. This is because periodic noise or random noise pulses injected by power electronic appliances on the network is a deviation from the AWGN. The nature of the noise is categorized as non-white non-Gaussian and unstable due to its impulsive attributes, thus, it is labeled as Non-additive White Gaussian Noise (NAWGN). These noise and disturbances results into long burst errors that corrupts signals being transmitted, thus, the PLC is labeled as a horrible or burst error channel. The e cient and optimal performance of a conventional linear receiver in the white Gaussian noise environment can therefore be made to drastically degrade in this NAWGN environment. Therefore, transmission reliability in such environment can be greatly enhanced if we know and exploit the knowledge of the channel's statistical attributes, thus, the need for developing statistical channel model based on empirical data. In this thesis, attention is focused on developing a recon gurable software de ned un-coded single-carrier and multicarrier PLC transceiver as a tool for realizing an optimized channel model for the narrowband PLC (NB-PLC) channel. First, a novel recon gurable software de ned un-coded single-carrier and multi-carrier PLC transceiver is developed for real-time NB-PLC transmission. The transceivers can be adapted to implement di erent waveforms for several real-time scenarios and performance evaluation. Due to the varying noise parameters obtained from country to country as a result of the dependence of noise impairment on mains voltages, topology of power line, place and time, the developed transceivers is capable of facilitating constant measurement campaigns to capture these varying noise parameters before statistical and mathematically inclined channel models are derived. Furthermore, the single-carrier (Binary Phase Shift Keying (BPSK), Di erential BPSK (DBPSK), Quadrature Phase Shift Keying (QPSK) and Di erential QPSK (DQPSK)) PLC transceiver system developed is used to facilitate a First-Order semi-hidden Fritchman Markov modeling (SHFMM) of the NB-PLC channel utilizing the e cient iterative Baum- Welch algorithm (BWA) for parameter estimation. The performance of each modulation scheme is evaluated in a mildly and heavily disturbed scenarios for both residential and laboratory site considered. The First-Order estimated error statistics of the realized First- Order SHFMM have been analytically validated in terms of performance metrics such as: log-likelihood ratio (LLR), error-free run distribution (EFRD), error probabilities, mean square error (MSE) and Chi-square ( 2) test. The reliability of the model results is also con rmed by an excellent match between the empirically obtained error sequence and the SHFMM regenerated error sequence as shown by the error-free run distribution plot. This thesis also reports a novel development of a low cost, low complexity Frequency-shift keying (FSK) - On-o keying (OOK) in-house hybrid PLC and VLC system. The functionality of this hybrid PLC-VLC transceiver system was ascertained at both residential and laboratory site at three di erent times of the day: morning, afternoon and evening. A First and Second-Order SHFMM of the hybrid system is realized. The error statistics of the realized First and Second-Order SHFMMs have been analytically validated in terms of LLR, EFRD, error probabilities, MSE and Chi-square ( 2). The Second-Order SHFMMs have also been analytically validated to be superior to the First-Order SHFMMs although at the expense of added computational complexity. The reliability of both First and Second-Order SHFMM results is con rmed by an excellent match between the empirical error sequences and SHFMM re-generated error sequences as shown by the EFRD plot. In addition, the multi-carrier (QPSK-OFDM, Di erential QPSK (DQPSK)-OFDM) and Di erential 8-PSK (D8PSK)-OFDM) PLC transceiver system developed is used to facilitate a First and Second-Order modeling of the NB-PLC system using the SHFMM and BWA for parameter estimation. The performance of each OFDM modulation scheme in evaluated and compared taking into consideration the mildly and heavily disturbed noise scenarios for the two measurement sites considered. The estimated error statistics of the realized SHFMMs have been analytically validated in terms of LLR, EFRD, error probabilities, MSE and Chi-square ( 2) test. The estimated Second-Order SHFMMs have been analytically validated to be outperform the First-Order SHFMMs although with added computational complexity. The reliability of the models is con rmed by an excellent match between the empirical data and SHFMM generated data as shown by the EFRD plot. The statistical models obtained using Baum-Welch to adjust the parameters of the adopted SHFMM are often locally maximized. To solve this problem, a novel Metropolis-Hastings algorithm, a Bayesian inference approach based on Markov Chain Monte Carlo (MCMC) is developed to optimize the parameters of the adopted SHFMM. The algorithm is used to optimize the model results obtained from the single-carrier and multi-carrier PLC systems as well as that of the hybrid PLC-VLC system. Consequently, as deduced from the results, the models obtained utilizing the novel Metropolis-Hastings algorithm are more precise, near optimal model with parameter sets that are closer to the global maxima. Generally, the model results obtained in this thesis are relevant in enhancing transmission reliability on the PLC channel through the use of the models to improve the adopted modulation schemes, create adaptive modulation techniques, develop and evaluate forward error correction (FEC) codes such as a concatenation of Reed-Solomon and Permutation codes and other robust codes suitable for exploiting and mitigating noise impairments encountered on the low voltage NB-PLC channel. Furthermore, the recon gurable software de ned NB-PLC transceiver test-bed developed can be utilized for future measurement campaign as well as adapted for multiple-input and multiple-output (MIMO) PLC applications.MT201

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

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

Characterization and modelling of the channel and noise for broadband indoor powerline communication (plc.) networks.

Masters degree. University of KwaZulu-Natal, Durban.Power Line Communication (PLC) is an interesting approach in establishing last mile broad band access especially in rural areas. PLC provides an already existing medium for broad band internet connectivity as well as monitoring and control functions for both industrial and indoor usage. PLC network is the most ubiquitous network in the world reaching every home. However, it presents a channel that is inherently hostile in nature when used for communication purposes. This hostility is due to the many problematic characteristics of the PLC from a data communications’ perspective. They include multipath propagation due to multiple reflections resulting from impedance mismatches and cable joints, as well as the various types of noise inherent in the channel. Apart from wireless technologies, current high data rate services such as high speed internet are provided through optical fibre links, Ethernet, and VDSL (very-high-bit-rate digital subscriber line) technology. The deployment of a wired network is costly and demands physical effort. The transmission of high frequency signals over power lines, known as power line communications (PLC), plays an important role in contributing towards global goals for broadband services inside the home and office. In this thesis we aim to contribute to this ideal by presenting a powerline channel modeling approach which describes a powerline network as a lattice structure. In a lattice structure, a signal propagates from one end into a network of boundaries (branches) through numerous paths characterized by different reflection/transmission properties. Due to theoretically infi nite number of reflections likely to be experienced by a propagating wave, we determine the optimum number of paths required for meaningful contribution towards the overall signal level at the receiver. The propagation parameters are obtained through measurements and other model parameters are derived from deterministic power system. It is observed that the notch positions in the transfer characteristics are associated with the branch lengths in the network. Short branches will result in fewer notches in a fixed bandwidth as compared to longer branches. Generally, the channel attenuation increase with network size in terms of number of branches. The proposed model compares well with experimental data. This work presents another alternative approach to model the transfer characteristics of power lines for broadband power line communication. The model is developed by considering the power line to be a two-wire transmission line and the theory of transverse electromagnetic (TEM) wave propagation. The characteristic impedance and attenuation constant of the power line v are determined through measurements. These parameters are used in model simplification and determination of other model parameters for typical indoor multi-tapped transmission line system. The transfer function of the PLC channel is determined by considering the branching sections as parallel resonant circuits (PRC) attached to the main line. The model is evaluated through comparison with measured transfer characteristics of known topologies and it is in good agreement with measurements. Apart from the harsh topology of power line networks, the presence of electrical appliances further aggravates the channel conditions by injecting various types of noises into the system. This thesis also discusses the process of estimating powerline communication (PLC) asynchronous impulsive noise volatility by studying the conditional variance of the noise time series residuals. In our approach, we use the Generalized Autoregressive Conditional Heteroskedastic (GARCH) models on the basis that in our observations, the noise time series residuals indicate heteroskedasticity. By per forming an ordinary least squares (OLS) regression of the noise data, the empirical results show that the conditional variance process is highly persistent in the residuals. The variance of the error terms are not uniform, in fact, the error terms are larger at some portions of the data than at other time instances. Thus, PLC impulsive noise often exhibit volatility clustering where the noise time series is comprised of periods of high volatility followed by periods of high volatility and periods of low volatility followed by periods of low volatility. The burstiness of PLC impulsive noise is therefore not spread randomly across the time period, but instead has a degree of autocorrelation. This provides evidence of time-varying conditional second order moment of the noise time series. Based on these properties, the noise time series data is said to suffer from heteroskedasticity. GARCH models addresses the deficiencies of common regression models such as Autoregressive Moving Average (ARMA) which models the conditional expectation of a process given the past, but regards the past conditional variances to be constant. In our approach, we predict the time-varying volatility by using past time-varying variances in the error terms of the noise data series. Subsequent variances are predicted as a weighted average of past squared residuals with declining weights that never completely diminish. The parameter estimates of the model indicates a high de gree of persistence in conditional volatility of impulsive noise which is a strong evidence of explosive volatility. Parameter estimation of linear regression models usually employs least squares (LS) and maximum likelihood (ML) estimators. While maximum likelihood remains one of the best estimators within the classical statistics paradigm to date, it is highly reliant vi on the assumption about the joint probability distribution of the data for optimal results. In our work, we use the Generalized Method of Moments (GMM) to address the deficien cies of LS/ML in order to estimate the underlying data generating process (DGP). We use GMM as a statistical technique that incorporate observed noise data with the information in population moment conditions to determine estimates of unknown parameters of the under lying model. Periodic impulsive noise (short-term) has been measured, deseasonalized and modeled using GMM. The numerical results show that the model captures the noise process accurately. Usually, the impulsive signals originates from connected loads in an electrical power network can often be characterized as cyclostationary processes. A cyclostationary process is described as a non-stationary process whose statistics exhibit periodic time varia tion, and therefore can be described by virtue of its periodic order. The focus of this chapter centres on the utilization of cyclic spectral analysis technique for identification and analysis of the second-order periodicity (SOP) of time sequences like those which are generated by electrical loads connected in the vicinity of a power line communications receiver. Analysis of cyclic spectrum generally incorporates determining the random features besides the pe riodicity of impulsive noise, through the determination of the spectral correlation density (SCD). Its effectiveness on identifying and analysing cyclostationary noise is substantiated in this work by processing data collected at indoor low voltage sites

Improvement of indoor environment signal reception using PLC-RF diversity techniques

D.Ing. (Electrical and Electronic Engineering)Abstract: Please refer to full text to view abstract