School of Electrical & Information Engineering (ETDs)
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Item Performance Modeling of Cognitive NOMA-aided IoT Networks with Energy Harvesting(University of the Witwatersrand, Johannesburg, 2024) Selematsela, Neo Edwin; Takawira, Fambirai; Chabalala, ChabalalaIn an attempt to address rocketed connectivity and bandwidth demands in 5G wireless networks, Non-Orthogonal Multiple Access (NOMA) and Cognitive Radio (CR) concepts have been proposed. The former addresses increased connectivity requirements by allowing multiple users in the same NOMA group to utilise the same channel resources. The latter enhances spectrum efficiency by intelligently al- lowing spectrum sharing between primary and secondary networks, if secondary to primary network interference is properly managed. To prolong connectivity/ser- vice life-time of battery capacity constrained Internet of Things (IoT) devices, Energy Harvesting (EH) technique has been identified as the technology that can enable such devices to harvest energy from ambient sources present in the envi- ronment. This research work is motivated by the observed surge in adoption of IoT devices around the globe. The resulting adoption has brought about the need to investigate performance of different IoT system models and hence, understand potential applicability and optimization options for different services. The focus of this dissertation is to model and analyse the performance of an EH Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA) IoT network. To accomplish this, a simplified energy harvesting CR-NOMA IoT network is considered. The considered network consists of primary and secondary network components. The primary network contains Macro Base-Station (MBS) and Pri- mary Network users (PUs), while the secondary network is made up of Secondary Base-Station (SBS) and multiple CR-NOMA groups containing two Secondary Users (SUs) each. To analytically capture the stochastic nature of energy harvest- ing process and cater for residual energy from one transmission frame to the next, each SU’s energy level in the battery is discretized to represent the state of each SU during each transmission frame; with this, we derive a complete Markovian model for the considered system model using queueing theory and Markovian analysis. Two Markovian models are developed for the considered system model, with one assuming that the SUs are harvesting energy from the SBS (one energy source) and the other, adopting an assumption that energy is harvested from both the SBS and MBS (two energy sources). The considered system performance is analysed in terms of up-link system outage probability and mean capacity. To provide detailed insights, closed-form analytical expressions for up-link outage probability and mean capacity for each user in the CR-NOMA group are derived using the Markovian models as the ba- sis. Produced analytical results are confirmed through simulations using Matlab. Simulation results matched the analytical results, this confirmed the validity of the derived analytical expressions for SUs outage probability and mean capacity. ii Both performance metrics are studied and the impact of varying different network parameters on outage probability and mean capacity is investigated. For out- age probability, results are generated which demonstrate SUs outage performance as we vary Signal-to-Interference-plus-Noise Ratio (SINR), interference threshold, and battery power level. Similarly, mean capacity results are generated to illus- trate each SU mean capacity performance while varying their battery levels, this is done for different values of primary transmit power and interference threshold. Performance results observed as different parameters are varied for outage prob- ability and mean capacity align with the theoretical performance expected when those parameters are changed. The significance of this work lies in providing ana- lytical tools to assess the performance of the CR-NOMA IoT system with energy harvesting (EH). These tools enable easy computation of system performance in- dicators such as outage performance and mean capacity. Attempting the same assessment through simulation would be a cumbersome process.Item Estimating Resistance and Performance of Earthing Systems Electrode in Variably Saturated Soil Conditions(University of the Witwatersrand, Johannesburg, 2024) Nnamdi, Onyedikachi Samuel; Gomes, ChandimaThe design and determination of post-installed resistance of earthing systems are significantly influenced by subsoil resistivity profiles, which are prone to seasonal variations due to environmental and climatic changes. These fluctuations can compromise operational safety and reliability of transmission systems, necessitating periodic monitoring of earthing installations as recommended by national and international standards. However, compliance with these recommendations is often impractical due to the vast number of earthing installations and associated costs. To address this challenge, this thesis proposes a novel multiphysics earthing model that integrates hydraulic and electrical properties of subsoil and earthing enhancement materials (EEMs) with climatic parameters to predict earthing resistance under varying conditions. The model, developed by coupling partial differential equations governing electric current dispersion and fluid retention in porous media, is validated through COMSOL Multiphysics® simulations of vertical earth rods in single and double subsoil layers. The results demonstrate that earthing resistance variation is dependent on subsoil texture, water content, and distribution of soil water potential, which determines subsoil resistivity. The proposed method achieves a relative error range of 2.72% to 6.53% and 1.47% compared to analytical and finite element method solutions, ensuring accuracy and validity. This innovative approach enables site-specific and climate-adaptive assessments of EEM effectiveness, facilitating informed decisions for earthing improvements in diverse conditions, and ultimately optimising material selection and recommendation for various soils and climates.Item Comparative Study on the Accuracy of the Conventional DGA Techniques and Artificial Neural Network in Classifying Faults Inside Oil Filled Power Transformers(University of the Witwatersrand, Johannesburg, 2024) Mokgosi, Gomotsegang Millicent; Nyamupangedengu , Cuthbert; Nixon , KenPower transformers are expensive yet crucial for power system reliability. As the installed base ages and failure rates rise, there is growing interest in advanced methods for monitoring and diagnosing faults to mitigate risks. Power transformer failures are often due to insulation breakdown from harsh conditions like overloading, that leads to prolonged outages, economic losses and safety hazards. Dissolved Gases Analysis (DGA) is a common diagnostic tool for detecting faults in oil-filled power transformers. However, it heavily relies on expert interpretation and can yield conflicting results, complicating decision-making. Researchers have explored Artificial Intelligence (AI) to address these challenges and improve diagnostic accuracy. This study investigates using Machine Learning (ML) techniques to enhance DGA for diagnosing power transformers. It employs an Artificial Neural Network (ANN) with Feed Forward Back Propagation, a Bayesian Regularizer for predictions, Principal Component Analysis (PCA) for feature selection and Adaptive Synthesizer (ADASYN) for data balancing. While traditional DGA methods are known for their accuracy and non- intrusiveness, they have limitations, particularly with undefined diagnostic areas. This research focuses on these limitations, to demonstrate that ANN provides more accurate predictions compared to conventional methods, with an average accuracy of 76.8% versus lower accuracies of 55% for Dornenburg, 40% for Duval, 38.4% for Roger and 31.8% for IEC (International Electrotechnical Commission) Methods. The study findings prove that ANN can effectively operate independently to improve diagnostic performance.Item Breakdown Strength Influences of Titanium Dioxide Nanoparticles on Midel Canola-Based Natural Ester oil: A Comparison Between the Anatase and Rutile Phases of Titanium Dioxide(University of the Witwatersrand, Johannesburg, 2024) Miya, Mabontsi Koba; Nixon, KenNatural ester oils are an alternative solution for sustainable transformer insulation. They offer good dielectric properties and in addition improve safety of equipment and sustainable environment. They have higher fire resistance than the widely used mineral oil and are less prone to explosions. They are also highly biodegradable and renewable. However, some challenges such as inconsistent breakdown voltage at higher temperatures and higher streamer speeds hinder the wide use of natural esters. Nanotechnology has been found to improve the properties of the oil, including the breakdown voltage. Different nanoparticles have been previously studied, giving varying results. This dissertation presents a study of the use of two phases of TiO2 nanoparticles, namely rutile and anatase, to improve the breakdown voltage of natural ester oil at higher temperatures. The study seeks to find the effects of the nanoparticle phases on the oil under uniform and non-uniform electric fields. Nanofluids of different loading concentrations (0.01 vol%, 0.03 vol%, 0.05, vol%) were created in each nanoparticle phase for the purpose of the study. The findings are that both phases of the nanoparticles improve the breakdown voltage under uniform fields. The anatase portrayed an impressive improvement of 85% at ambient temperature, while the rutile phase enhanced by 61%. At higher temperatures however, the rutile phase had better improvement. Rutile TiO2 nanoparticles consistently outperformed the anatase phase in improving the breakdown voltage at higher temperatures. Under non-uniform electric fields, the rutile TiO2-based nanofluid was found to be superior to the anatase-based fluid. Rutile TiO2 resulted in a significant 10% improvement in the average breakdown voltage and streamer acceleration voltage. An overall decrease in the streamer speeds was observed with the addition of the rutile TiO2 nanoparticles. In contrast, anatase TiO2 resulted in decreased breakdown voltage and increased streamer speeds when compared to both the rutile nanofluid as well as the pure natural ester oil. The rutile phase of TiO2 can be regarded as a feasible solution for breakdown voltage improvement of natural ester oil in both cases of uniform and non-uniform electric field. The effects are attributed to the electron capture phenomenon and the good thermal stability of rutile TiO2. A stable composite is formed between the rutile nanoparticles and the host natural ester. The resultant morphological structure enables stable interfacial regions even at higher temperatures. In conclusion therefore, rutile TiO2 nanocomposite natural fluid is a possible solution to the current limitations in ester oils as power transformer insulation oil alternative.Item Characterization of high-frequency time-domain e↵ects arising from the transmission line substitutions of reactive components in a buck converter(University of the Witwatersrand, Johannesburg, 2024) Maree, JohnThe work presented in this dissertation is a continuation of a line of research that suggests that the energy storage components within a DC-DC converter may be a source of high frequency e↵ects in power converter circuits. It is shown that for physically large energy storage compo- nents, conventional models are insucient for modelling the e↵ects of these components and that a transmission line approach is required. Very little work has been done within switching circuits using transmission line theory for the primary components themselves, specifically re- garding the time-domian e↵ects of these components. A significant finding of this work is that it is shown that both in simulation and experimental results these components do indeed have a measurable e↵ect on the output of the converter. Furthermore, this dissertation explores time-domain quantification methods for these distributed e↵ects, and shows that the delay ratio between the transmission lines is a key parameter in determining the magnitude of the e↵ects. This work provides strong experimental evidence for the existence of distributed e↵ects occurring from energy storage components within a DC-DC converter, and indicates that this area of research is worth further investigation. Advancements into our understanding of the high-frequency operation of DC-DC converters have become increasingly rare, necessitating a new perspective. This work focusses on using transmission line theory to model energy storage components within a DC-DC converter, and investigating the e↵ects of doing so. The research firstly introduces the design, simulation and experimental evidence for inductors and capacitors using transmission line theory. In fact, it is shown that in order to accurately model a physically large reactive component, transmission line modelling is required. Thereafter, these components in a physically large form are then applied to a DC-DC buck converter circuit where it is shown that the converter manifests high frequency e↵ects that are not predicted by conventional models, but is adequately shown using transmission line models. The e↵ects of these components are then investigated, and it is shown that the delay ratio between the transmission lines is a key parameter in determining the magnitude of the e↵ects. This work provides strong experimental evidence for the existence of distributed e↵ects occurring from energy storage components within a DC-DC converter, and indicates that this area of research is worth further investigation.Item Assessment of DC-DC Converter Selection Metrics(University of the Witwatersrand, Johannesburg, 2024) Letsoalo, Future Malekutu; Hofsajer, IvanThe exponential growth of Internet of Things (IoT) devices, powered by diverse energy sources, poses significant challenges in power electronics. Despite advances in DC-DC converter topologies, a gap remains in the literature regarding standardized performance metrics for selecting suitable converters, making the selection process complex. This study critically assesses metrics from seminal works of the 1960s to contemporary state-of-the-art, proposing a systematic approach to converter assessment. Two major categories of metrics are identified: averaging metrics and waveform-preserving metrics. Averaging metrics, grounded in Wolaver's foundational work, are effective for high-level comparisons among many converter options, establishing a performance baseline. The study introduces an average modeling tool to reveal core converter characteristics for objective comparison. Waveform-preserving metrics, on the other hand, provide detailed performance insights and are suitable for a narrower set of converter options. The study further categorizes these metrics to assess converter switches and reactive components. A new RMS metric is proposed, refining the existing processed power metric for better accuracy. By integrating both averaging and waveform-preserving metrics at relevant design stages, this study offers a systematic framework for converter assessment. This approach bridges the gap between high-level comparison and detailed performance evaluation, facilitating informed decision-making in converter selection.Item A Study of the Effect of Temperature on Cavity Partial Discharges in Polyethylene (PE) Insulation(University of the Witwatersrand, Johannesburg, 2024) Khangale, Mulovhela Kennedy; Nyamupangedengu, CuthbertSynthetic Polymers such as polyethylene are prevalent for high-voltage insulation applications as they offer remarkable insulating and dielectric properties. Notwithstanding precautionary measures made during manufacturing and installation processes, insulation systems are always susceptible to defects for various reasons, which constitute a significant source of Partial Discharge (PD) activity. It is a precursor to insulation degradation leading to premature failure of high-voltage equipment. PD activity is complex due to its non-stationary behaviour and multi- variance dependence. Studies in partial discharge mechanisms have received significant attention over the years to improve phenomena understanding and, in some cases, to allow conclusions to be drawn on the parameters affecting PD mechanisms. These studies have shown that different mechanisms and parameters influence partial discharge activity. In this study , experimental and analytical modelling techniques are used to explore the behaviour of partial discharge mechanisms at varying temperatures. Experimental PD measurements were carried out in accordance with the IEC 60270 standard. A test voltage of 11 kV ac was used. The test temperatures studied were 15C, 40C, 50C, 60C, 70C, 80C and 90C. Test specimens with a cavity diameter of 2.5 mm were assembled using three 1.5 mm thick polyethylene sheets sandwiched between two flat brass electrodes. Partial discharge parameters such as the charge magnitude, inception voltage and PD phase resolved pattern (PDPRP) were measured and analysed at varying temperatures. For analytical modelling, the streamer-like discharge concept is adopted to model PDIV while the apparent charge magnitude is modelled based on the induced charge concept introduced by Pedersen in the 1980s. The curve fitting approach was adopted to replicate and explain the measured experimental data. Results showed that Partial Discharge Inception Voltage (PDIV) increased linearly with temperature for the entire test temperature range. PD charge magnitude initially decreased with temperature from 15°C to 60°C and then increased from 60°C to 90°C. The evolution of PD phase resolved pattern (PDPRP) with temperature was characterised by a turtle-like pattern at ambient temperature, which transitioned into a rabbit ear PDPRP as the temperature increased to 90°C. The findings are interpreted using the mean free path effect on ionisation probability as well as the residual charge dynamics in the cavity as a function of temperature. The overall conclusion is that in polyethylene, cavity discharge characteristics respond to temperature changes. The variations in PD characteristics iv are monotonous for PDIV and non-monotonous for apparent charge magnitude as well as PDPRP. The implications of the findings are that in PD diagnosis,temperature of the equipment under test must be taken into account in interpretation of PD measurements results.Item Exploring the Performance of a PV-Supplied Cooking System with Hybrid Electric-Thermal Energy Storage(University of the Witwatersrand, Johannesburg, 2024) Chiloane, Learn LeagoThis research explores the extent to which the addition of a thermal storage tank can reduce the electrical cooking energy in solar Photo-Voltaic (PV) supplied cooking systems. The aim is to consider a smaller battery size to store the decreased electrical cooking energy and the rest of the solar energy is stored in a low-cost thermal storage tank. This is done to reduce the cost of the storage unit to address the challenges of clean-cooking access in rural African households without grid electricity. The energy performance of the system is evaluated considering a small-scale solar system that powers a storage water heater as the thermal storage tank and a slow cooker as the cooking appliance. A thermal-electrical analogy and experimental tests are employed to model and validate the effectiveness of the system under different cooking conditions. Rice is chosen as the cooking medium because it is the fastest growing staple in Africa, and its cooking process benefits from starting it with pre-heated water. It is shown that starting with pre-heated water accelerates the readiness of rice, and reduces the additional electrical energy to complete the cooking process which translates to a similar battery size reduction. The results from three cooking scenarios indicate that a hybrid cooking system incorporating a battery and thermal storage tank can reduce the battery capacity requirements by up to 24.3% for one meal, 14.7% for two meals, and 10.6% for three meals in comparison to a pure electrical cooking system with a battery only.