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

Power line communication (PLC) channel measurements and characterization.

M. Sc. Eng. University of KwaZulu-Natal, Durban 2014.The potential of the power line to transport both power and communication signals simultaneously has been realized and practiced for over a century, dating back to the 1900’s. Since the key aspect of power line communications being its expansivity, its implementations were largely as a retrofit technology. This motivation of power line communication is typical for low-, medium-, and high voltage distribution networks. Beyond the “last mile” part, there’s an uprising appeal for intra-building networks currently targeted for home automation (smart homes/buildings) and in-building networking. The optimum use of the existing power line channels has been a focus area for researchers and designers, with the inherent channel hostility proving a serious drawback for high speed data communications. The low-voltage electrical network has unpredictable noise sources, moreover it has two other main disadvantages as a communication channel. The first short coming has to do with the unknown characteristics of the power cable and topology of the network, the second arises from the time-dependent fluctuation of the impedance level of the power line as the loads are switched into and out of the power line network in an unpredictable manner. These factors determine the behaviour of the power line channel when a high frequency signal is impressed on it. This study has shown that the behaviour of indoor power line channels can be captured using a multipath based model even with limited qualitative and/or quantitative knowledge of the network topology. This model is suitable for typical indoor power line channels where knowledge of the topology is near impossible. Some of the feed parameters are obtained through measurements. With sufficient adjustment of control parameters, this model was successfully validated using sample measured channels from the numerous measurements. Through noise measurements, this study has established that impulsive noise is the rifest in the frequency band of interest. The impulsive energy rises well above background noise, which translates to possible data “black outs”. The statistics of the components of this noise are presented. A model of sufficient simplicity is used to facilitate the qualitative description of the background noise through its power spectral density. Two descriptions are provided in terms of the worst and best case scenarios of the background noise occurrences. The model has a good macroscopic capture of the noise power spectral density, with narrow-band interference visible for the worst case noise. Due to the multipath nature of the power line channel, this study also presents the dispersive characteristics of the power line as a communication channel. The power delay profile is used to determine parameters such as first arrival delay, mean excess delay, root mean square delay spread and maximum delay spread. The statistics of these parameters are presented. Also, the coherence bandwidth of power line channels is studied and its relationship with the rms delay spread is developed. It is in view of this work that further research in power line communication and related topics shall be inspired

Characterization and modeling of the channel and noise for broadband indoor Power Line Communication (PLC) networks.

Doctor of Philosophy in Electronic Engineering. University of KwaZulu-Natal, Durban 2016Power Line Communication (PLC) is an interesting approach in establishing last mile broadband access especially in rural areas. PLC provides an already existing medium for broadband 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 infinite 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 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 performing 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 degree 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 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 deficiencies 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 underlying 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 variation, 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 periodicity 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

Experimental characterisation and life data analysis of superior mixed liquid dielectrics for power transformer application

Transformer liquid dielectrics play a vital role in safety and reliability of power utility grids under normal and faulty conditions. Liquid dielectrics provide electrical insulation and act as coolant for the power transformer. However, most used dielectrics are made from Mineral Oil (MO) which has low breakdown voltage, flash, and fire point, also posing a risk of contaminating the environment in case of leakage or explosion. Therefore, research explores on potential natural renewable resources to replace the vast use of MO*⁎ Mineral Oil based dielectric. This paper focuses on using Ximenia Oil (XO) and Kalahari Melon Seed Oil (KMS), both being natural esters, for dielectric application. Each of them is mixed at different ratios separately with reclaimed mineral oil to analyse their compatibility with power transformers. Electrical and chemical properties of these esters are tested to find optimal blending ratio for each, and lifetime analysis is applied to further establish their usability. In this study, both XO†† Ximenia Oil and KMS have been identified to be applicable at 75:25 MO: XO ratio and 70:30 for MO: KMS respectively, especially after moisture content reduction. Lifetime analysis has also rendered the two dielectrics applicable as they have a long-estimated lifetime greater than that of MO alone. MO: XO dielectric has been more dominant over KMS, and MO based dielectrics in most property while KMS has shown dominance in the lifetime reliability analysis after 50 kV BDV‡‡ Breakdown Voltage value. Therefore, application of these blends significantly improves the lifetime of the transformer dielectrics hence improving that of the transformer itself

A Graph-Theoretic Approach for Optimal Phasor Measurement Units Placement Using Binary Firefly Algorithm

The pursuit of achieving total power network observability in smart grids using Phasor Measurement Units (PMUs) carries a significant promise of real-time Wide-Area Monitoring, Protection, and Control (WAMPAC). PMU applications eliminate periodical measurements, thereby increasing accuracy through a high sampling rate of the measured power systems quantities. The high costs of installation of PMUs for total power system observability presents a challenge in the implementation of PMUs. This is due to the expensive costs of PMU devices. This has led to a prominent optimal PMU placement (OPP) problem that researchers tirelessly aim to solve by ensuring a complete power network observability while using the least installed PMU devices possible. In this paper, a novel Binary Firefly Algorithm (BFA) based on the node degree centrality scores of each bus is proposed to minimize PMU installations. The BFA solves the OPP problem in consideration of the effect of Zero Injection Buses (ZIBs) under normal operation and single PMU outage (SPO). The robustness and efficiency of the proposed algorithm is tested on IEEE-approved test systems and visualized with a force-directed technique on an undirected power network graph. The proposed BFA yields the same but better optimal PMU numbers, obtained by existing meta-heuristic optimization techniques found in the literature for each of the IEEE test cases, as well as highlighting the cost–benefit of having a robust system against single PMU loss while considering the ZIB effect for an improved system measurement availability

Design Procedure of a Frequency Reconfigurable Metasurface Antenna at mmWave Band

The use of the millimeter wave (mmWave) spectrum and further exploration of sub-mmWave has led to a new era in wireless communication, as the need for higher data rates grows. High frequencies, on the other hand, incur a higher path loss, requiring an increase in antenna gain requirements. Metasurfaces, which emerge as a promising technology for mitigating path loss effects by utilizing two dimensional (2D) arrays of engineered meta-atoms resembling metamaterials that control the surface’s electromagnetic response have been introduced. Currently, metasurfaces are primarily considered as passive reflecting devices in wireless communications, assisting conventional transceivers in shaping propagation environments. This paper presents an alternative application of metasurfaces for wireless communications as active reconfigurable antennas for next generation transceivers. A framework that demonstrates the design process of a metasurface antenna structure was introduced and further used to design a 4 × 4 array and its reconfigurable counterpart. In contrast to conventional phased array antennas, a reconfigurable metasurface (RMS) antenna does not require phase-shifters and amplifiers, which leads to reduced cost. Instead, each individual element achieves reconfigurability by shifting the resonating frequency using semiconductor devices such as PIN diodes. The proposed metasurface antenna is designed to operate at a frequency of 28 GHz and 40 GHz. In addition, an increase in gain and directivity was observed when diodes were added to the metasurface antenna array. However, due to PIN diodes being connected to metallic strips in the metasurface antenna array, loss can occur due to power dissipation, which results in a decrease in radiation efficiency

Investigation of Survival/Hazard Rate of Natural Ester Treated with Al2O3 Nanoparticle for Power Transformer Liquid Dielectric

Increasing usage of petroleum-based insulating oils in electrical apparatus has led to increase in pollution and, at the same time, the oils adversely affect the life of electrical apparatus. This increases the demand of Mineral Oil (MO), which is on the verge of extinction and leads to conducting tests on natural esters. This work discusses dielectric endurance of Marula Oil (MRO), a natural ester modified using Conductive Nano Particle (CNP) to replace petroleum-based dielectric oils for power transformer applications. The Al2O3 is a CNP that has a melting point of 2072 °C and a low charge relaxation time that allows time to quench free electrons during electrical discharge. Al2O3 is blended with the MRO and Mineral Oil (MO) in different concentrations. The measured dielectric properties are transformed into mathematical equations using the Lagrange interpolation polynomial functions and compared with the predicted values either using Gaussian or Fourier distribution functions. Addition of Al2O3 indicates that 0.75 g/L in MRO has an 80% survival rate and 20% hazard rate compared to MO which has 50% survival rate and 50% hazard rate. Considering the measured or interpolated values and the predicted values, they are used to identify the MRO and MO’s optimum concentration produces better results. The test result confirms the enhancement of the breakdown voltage up to 64%, kinematic viscosity is lowered by up to 40% at 110 °C, and flash/fire points of MRO after Al2O3 treatment enhanced to 14% and 23%. Hence the endurance of Al2O3 in MRO proves to be effective against electrical, physical and thermal stress

Propagation channel characterization for mobile communication based on measurement campaign and simulation

Predicting the coverage area of mobile networks across various frequency bands is a critical concern in wireless communication. Each frequency band exhibits distinct propagation characteristics that directly influence the network coverage. Therefore, the use of the appropriate propagation models, whether through simulation or measurement-based approaches, is essential for accurate signal strength prediction in different environments and scenarios. In this study, we focus on the analysis and comparison of propagation channel characterization for mobile communication within the sub-6GHz frequency range. Our research involved outdoor measurement campaigns and ray-tracing simulations conducted at the Botswana International University of Science and Technology, covering distances up to 213 m. We then conducted an in-depth investigation and comparative analysis of the measured and simulated data for the propagation channel. Additionally, we derived a characterization model for the closed-in free space reference distance (CI) model based on measured and simulated data. Finally, we employed the root-mean-square error (RMSE) as a quantitative assessment of the performance of the characterized models. Notably, our findings revealed that the derived Close-in model based on measured data outperformed that of the simulations, exhibiting the lowest RMSE values of 3.56 dB, 4.20 dB, and 7.87 dB for location-1 line-of-sight, location-2 line-of-sight, and non-line-of-sight scenarios, respectively. These results hold significant potential for the development of precise path loss models that can effectively predict and optimize the coverage area of mobile communication systems across various real-world scenarios and environments

A comparative analysis of alpha-beta-gamma and close-in path loss models based on measured data for 5G mobile networks

Mobile coverage is crucial for the fifth-generation (5G) network since it affects the network's accessibility and dependability in various locations. With a wider coverage, more people and devices will access the 5G network, allowing them to experience the benefits and capabilities of this important technology. However, the development and construction of the networks need a lot of time and effort to maximize network coverage and service delivery while utilizing the fewest possible infrastructure components. Path loss models are commonly employed to predict network coverage. Therefore, it is expedient to adopt a path loss model that is suitable for the specified geographical area. In this paper, we investigated and analysed two empirical models: the Close-in free space reference distance (CI) model and the alpha-beta-gamma (ABG) model at the low- and mid-spectrum bands. Secondly, the path loss parameters were derived to characterize the two empirical models based on data from the measurement campaign. Finally, Root Mean Square Error (RMSE) was employed for the comparison of the models. It was found that at frequencies 0.9 and 1.8 GHz, the CI model's least RMSE values were 3.6 and 4.1 at location 1 and 4.7 and 4.2 dB at location 2, respectively, outperforming the ABG. However, at 3.4 GHz, the ABG with 3.25 and 1.75 dB outperformed the CI at both locations. For future work, the integration of machine learning techniques to dynamically predict and adapt path loss models in real time based on continuous data collection will be appropriate