Robust wireless sensor network for smart grid communication : modeling and performance evaluation

Our planet is gradually heading towards an energy famine due to growing population and industrialization. Hence, increasing electricity consumption and prices, diminishing fossil fuels and lack significantly in environment-friendliness due to their emission of greenhouse gasses, and inefficient usage of existing energy supplies have caused serious network congestion problems in many countries in recent years. In addition to this overstressed situation, nowadays, the electric power system is facing many challenges, such as high maintenance cost, aging equipment, lack of effective fault diagnostics, power supply reliability, etc., which further increase the possibility of system breakdown. Furthermore, the adaptation of the new renewable energy sources with the existing power plants to provide an alternative way for electricity production transformed it in a very large and complex scale, which increases new issues. To address these challenges, a new concept of next generation electric power system, called the "smart grid", has emerged in which Information and Communication Technologies (ICTs) are playing the key role. For a reliable smart grid, monitoring and control of power system parameters in the transmission and distribution segments are crucial. This necessitates the deployment of a robust communication network within the power grid. Traditionally, power grid communications are realized through wired communications, including power line communication (PLC). However, the cost of its installation might be expensive especially for remote control and monitoring applications. More recently, plenty of research interests have been drawn to the wireless communications for smart grid applications. In this regard, the most promising methods of smart grid monitoring explored in the literature is based on wireless sensor network (WSN). Indeed, the collaborative nature of WSN brings significant advantages over the traditional wireless networks, including low-cost, wider coverage, self-organization, and rapid deployment. Unfortunately, harsh and hostile electric power system environments pose great challenges in the reliability of sensor node communications because of strong RF interference and noise called impulsive noise. On account of the fundamental of WSN-based smart grid communications and the possible impacts of impulsive noise on the reliability of sensor node communications, this dissertation is supposed to further fill the lacking of the existing research outcomes. To be specific, the contributions of this dissertation can be summarized as three fold: (i) investigation and performance analysis of impulsive noise mitigation techniques for point-to-point single-carrier communication systems impaired by bursty impulsive noise; (ii) design and performance analysis of collaborative WSN for smart grid communication by considering the RF noise model in the designing process, a particular intension is given to how the time-correlation among the noise samples can be taken into account; (iii) optimal minimum mean square error (MMSE)estimation of physical phenomenon like temperature, current, voltage, etc., typically modeled by a Gaussian source in the presence of impulsive noise. In the first part, we compare and analyze the widely used non-linear methods such as clipping, blanking, and combined clipping-blanking to mitigate the noxious effects of bursty impulsive noise for point-to-point communication systems with low-density parity-check (LDPC) coded single-carrier transmission. While, the performance of these mitigation techniques are widely investigated for multi-carrier communication systems using orthogonal frequency division multiplexing (OFDM) transmission under the effect of memoryless impulsive noise, we note that OFDM is outperformed by its single-carrier counterpart when the impulses are very strong and/or they occur frequently, which likely exists in contemporary communication systems including smart grid communications. Likewise, the assumption of memoryless noise model is not valid for many communication scenarios. Moreover, we propose log-likelihood ratio (LLR)-based impulsive noise mitigation for the considered scenario. We show that the memory property of the noise can be exploited in the LLR calculation through maximum a posteriori (MAP) detection. In this context, provided simulation results highlight the superiority of the LLR-based mitigation scheme over the simple clipping/blanking schemes. The second contribution can be divided into two aspects: (i) we consider the performance analysis of a single-relay decode-and-forward (DF) cooperative relaying scheme over channels impaired by bursty impulsive noise. For this channel, the bit error rate (BER) performances of direct transmission and a DF relaying scheme using M-PSK modulation in the presence of Rayleigh fading with a MAP receiver are derived; (ii) as a continuation of single-relay collaborative WSN scheme, we propose a novel relay selection protocol for a multi-relay DF collaborative WSN taking into account the bursty impulsive noise. The proposed protocol chooses the N’th best relay considering both the channel gains and the states of the impulsive noise of the source-relay and relay-destination links. To analyze the performance of the proposed protocol, we first derive closed-form expressions for the probability density function (PDF) of the received SNR. Then, these PDFs are used to derive closed-form expressions for the BER and the outage probability. Finally, we also derive the asymptotic BER and outage expressions to quantify the diversity benefits. From the obtained results, it is seen that the proposed receivers based on the MAP detection criterion is the most suitable one for bursty impulsive noise environments as it has been designed according to the statistical behavior of the noise. Different from the aforementioned contributions, talked about the reliable detection of finite alphabets in the presence of bursty impulsive noise, in the thrid part, we investigate the optimal MMSE estimation for a scalar Gaussian source impaired by impulsive noise. In Chapter 5, the MMSE optimal Bayesian estimation for a scalar Gaussian source, in the presence of bursty impulsive noise is considered. On the other hand, in Chapter 6, we investigate the distributed estimation of a scalar Gaussian source in WSNs in the presence of Middleton class-A noise. From the obtained results we conclude that the proposed optimal MMSE estimator outperforms the linear MMSE estimator developed for Gaussian channel

Robust adaptive filtering algorithms for system identification and array signal processing in non-Gaussian environment

This dissertation proposes four new algorithms based on fractionally lower order statistics for adaptive filtering in a non-Gaussian interference environment. One is the affine projection sign algorithm (APSA) based on L₁ norm minimization, which combines the ability of decorrelating colored input and suppressing divergence when an outlier occurs. The second one is the variable-step-size normalized sign algorithm (VSS-NSA), which adjusts its step size automatically by matching the L₁ norm of the a posteriori error to that of noise. The third one adopts the same variable-step-size scheme but extends L₁ minimization to Lp minimization and the variable step-size normalized fractionally lower-order moment (VSS-NFLOM) algorithms are generalized. Instead of variable step size, the variable order is another trial to facilitate adaptive algorithms where no a priori statistics are available, which leads to the variable-order least mean pth norm (VO-LMP) algorithm, as the fourth one. These algorithms are applied to system identification for impulsive interference suppression, echo cancelation, and noise reduction. They are also applied to a phased array radar system with space-time adaptive processing (beamforming) to combat heavy-tailed non-Gaussian clutters. The proposed algorithms are tested by extensive computer simulations. The results demonstrate significant performance improvements in terms of convergence rate, steady-state error, computational simplicity, and robustness against impulsive noise and interference --Abstract, page iv

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 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

A flexible statistical framework for the characterization and modelling of noise in powerline communication channels.

Doctor of Philosophy in Electronic Engineering.One communication medium that has received a lot of interest in recent years is the power line channel, especially for the delivery of broadband content. This channel has been traditionally used to carry electrical power only. But with the recent advancements in digital signal processing, it is now possible to realize communications through the power grid, both in narrowband and broadband. The use of the power line network for telecommunication purposes constitutes what is referred to as powerline carrier communications or simply powerline communications (PLC). The biggest incentive for PLC technology use is the fact that the power line network is already in place, which greatly reduces the communication network set up cost, since no new cabling layout is required. PLC technology is widely applied in home networking, broadband internet provision and smart grid solutions. However, the PLC channel presents a very hostile communication environment. And as such, no consideration has been made in the design of traditional power line network to accommodate communication services. Of all the PLC channel impairments which include frequency-dependent attenuation, frequency selectivity, multipath and noise, noise is the biggest threat to communication signals. This noise manifests itself in form of coloured background noise, narrowband interference and impulsive noise. A thorough understanding of this noise distribution is therefore crucial for the design of a reliable and high performing PLC system. A proper understanding of the noise characteristics in the PLC channel can only be realized through noise measurements in live power networks, and then analyzing and modeling the noise appropriately. Moreover, the noise scenario in power line networks is very complex and therefore cannot be modeled through mere analytical methods. Additionally, most of the models that have been proposed for the PLC noise previously are mere adaptations of the measured noise to some existing impulsive noise models. These earlier modeling approaches are also rigid and model the noise via a fixed set of parameters. In the introductory work in this thesis, a study of orthogonal frequency division multiplexing (OFDM) as the modulation of choice for PLC systems is presented. A thorough survey of the salient features of this modulation scheme that make it the perfect candidate for PLC modulation needs is presented. In the end, a performance analysis study on the impact of impulsive noise on an OFDM based binary phase shift keying (BPSK) system is done. This study differs from earlier ones in that its focus is on how the elementary parameters that define the impulsive noise affect the system, a departure from the usual norm of considering the overall noise distribution. This study focuses on the impact of interarrival times (IAT), pulse amplitudes as well as pulse widths, among other parameters. In the first part of the main work in this thesis, results of an intensive noise measurement campaign for indoor low voltage power line noise carried out in various power line networks, in the Department of Electrical, Electronic and Computer Engineering buildings at the University of KwaZulu-Natal, Howard campus are presented. The noise measurements are carried out in both time and frequency domains. Next, the noise measurements are then analyzed and modeled using two very flexible data modeling tools; nonparametric kernel density estimators and parametric alpha stable (α-stable) distributions. The kernel method’s ability to overcome all the shortcomings of the primitive histogram method makes it very attractive. In this method, the noise data structure is derived straight from the data itself, with no prior assumptions or restrictions on the data structure, thus effectively overcoming the rigidity associated with previous noise models for power line channels. As such, it results in density estimates that “hug” the measured density as much as possible. The models obtained using the kernel methods are therefore better than any parametric equivalent; something that can always be proven through goodness of fit tests. These models therefore form an excellent reference for parametric modeling of the power line noise. This work forms the author’s first main contribution to PLC research. As a demonstration of the kernel models suitability to act as a reference, parametric models of the noise distribution using the alpha stable (α-stable) distribution are also developed. This distribution is chosen due to its flexibility and ability to capture impulsiveness (long-tailed behaviour), such as the one found in power line noise. Stable distributions are characterized by long/fat tails than those of the Gaussian distribution, and that is the main reason why they are preferable here since the noise characteritics obtained in the kernel technique show visible long/heavy tailed behavior. A parameter estimation technique that is based on quantiles and another on the empirical characteristic function are employed in the extraction of the four parameters that define the characteristic function of the α-stable distribution. The application of the α-stable distribution in other signal processing problems has often been over-simplied by considering the symmetric alpha stable distribution, but in this thesis, the general α-stable distribution is used to model the power line noise. This is necessary so as to ensure that no features of the noise distribution are missed. All the models obtained are validated through error analysis and Chi-square fitness tests. This work forms the author’s second main contribution to PLC research. The author’s last contribution in this thesis is the development of an algorithm for the synthesis of the power line as a Levy stable stochastic process. The algorithm developed is then used to generate the PLC noise process for a random number of alpha stable noise samples using the alpha stable noise parameters obtained in the parametric modeling using stable distributions. This algorithm is generalized for all admissible values of alpha stable noise parameters and therefore results for a Levy stable Gaussian process are also presented for the same number of random noise samples for comparison purposes