Theses and Dissertations (Electrical, Electronic and Computer Engineering)

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Innovations in advanced regulatory control methods for modern distributed control systems
(University of Pretoria, 2024-09) Craig, Ian Keith; Le Roux, Johan Derik; gustaf.gous@gmail.com; Gous, Gustaf Zacharias
Many modern Distributed Control Systems (DCS) in industry are new replacements of previous versions of the same DCS vendor’s product line. During such upgrades the process is often automated using software to translate existing controller configurations as well as custom software to comply with the new system’s requirements and syntax. Doing this makes the upgrade process much faster and reduces the risk of introducing errors. It does, however, rob the control practitioner from making use of new features and capabilities of the new system. Therefore, there are many DCS in industry where only a small fraction of their newer capabilities are used. Many improvements in advanced regulatory control (ARC) that would improve control performance are available, but are never used. In order to show how modern DCS can enable more complex control solutions, four ARC level controllers and two stiction compensation algorithms, all more complex than current solutions typically found in industry, are introduced as examples of how increased complexity may provide increased control performance.
Current steering with sequential stimulation in cochlear implants
(University of Pretoria, 2017-12) Hanekom, J.J. (Johannes Jurgens); Roux, Johanie
Current steering has been proposed to increase place pitch resolution in cochlear implant (CI) users. Many studies have shown that a current steering effect can be achieved when simultaneous stimulation is used (Koch, Downing, Osberger, Litvak and Greco, 2007, Saoji and Litvak, 2010, Wu and Luo, 2013). Some literature has shown that a current steering effect can also be achieved when sequential stimulation is used (McDermott and McKay, 1994, Kwon and van den Honert, 2006, Swanson, 2008). Literature proposes different features that could underlie place pitch and consequently possibly also current steering effects (McDermott and McKay, 1994, Kwon and van den Honert, 2006, Swanson, 2008, Frijns, Kalkman, Vanpoucke, Bongers and Briaire, 2009, Macherey and Carlyon, 2012, Venter, 2015). The present study confirmed that a current steering effect can be achieved when sequential stimulation is used by using multi-dimensional scaling and statistical analysis in addition to the convention of using cumulative d' values to analyse pitch ranking results of current steering experiments. It was however observed that a current steering effect could only be achieved in listeners who were at least able to pitch rank the pitch of the two individual stimulating electrodes correctly according to expectation. The effect of different stimulation parameters on the pitch ranking ability of CI users during current steering experiments was investigated. Results showed that some parameters only had an effect on the pitch ranking performance of some listeners, while other stimulation parameters affected the results of all the listeners. Wider stimulation pulse widths, for example, led to improved pitch ranking results for some listeners. Most listeners benefited from wider electrode separation distances. Statistical analysis showed that there was a significant improvement in the pitch ranking performance of the listeners during experiments where the stimulation rate was the same as the rate indicated on the clinical MAP of the listener. Person-specific current distribution models were used to predict the cochlear position of different stimuli because of different features that could underlie place pitch, for each of the experiments for four of the listeners who participated in this study. The model predictions were related to the measured pitch ranking results using correlation and mutual information analysis. The results indicated that the current centroid at electrode level, the position of the peak current at the auditory nerve level (because of either an individual stimulating electrode or because of summed currents) and the centroid of neural activation could underlie place pitch. All these features except the position of the peak of the current distribution at the auditory nerve level because of each individual stimulating electrode could underlie current steering effects. Results showed that the centroid of the current distribution at the auditory nerve level probably does not underlie place pitch. Knowledge about the impact that different stimulation parameters have on the ability to achieve a current steering effect could result in more efficient implementation of current steering effects. Proper knowledge of which features underlie place pitch and current steering effects could be used to create models that can be used to predict the results of place pitch experiments.
Viability of cochlear travelling wave signal processing for cochlear implants
(University of Pretoria, 2016-02) Hanekom, J.J. (Johannes Jurgens); Schmidt, Larry
English: The travelling wave encodes acoustic information by stimulating the auditory nerve fibres. Understanding the travelling wave and its process is important for the development of cochlear implants speech processors. The development of a normal hearing auditory model, using a hydrodynamic model of the travelling wave to predict the nerve fibre spiking diagrams, marked the first stage of this study. This study then proceeded to look at the development of a travelling wave speech processing algorithm and model the electrical response due to the stimulation from the vocoder speech processor, and the travelling wave speech processor. The final stage was to predict whether temporal encoding occurred during cochlear implant stimulation for the vocoder speech processor and the travelling wave speech processor. The results showed that the travelling wave normal hearing model was able to predict the nerve fibre characteristics seen in measurements from literature. This showed that the mechanical encoding performed by the travelling wave is vital to the encoding of information in auditory nerve fibres. The travelling wave speech processor was able to encode temporal cues for pitch up to 1060 Hz, where the results for the vocoder speech processor showed the 300 Hz limit seen in other literature of phase locking. Mimicking the travelling wave in cochlear implant speech processors may potentially benefit the delivery of information to the auditory cortex for cochlear implant users. However, these results must be legitimised using animal models and psychoacoustic experiments.
Signal processing by octopus cells for acoustic and electrical hearing : a modelling study
(University of Pretoria, 2016-07) Hanekom, J.J. (Johannes Jurgens); Blignaut, Gertruida Elizabeth
A computational model of a single octopus cell as well as a population of octopus cells was developed. The models were used to investigate the ability of octopus cells to compensate for the travelling wave delay, remove jitter from the neural activity and encode pitch for normal hearing. Furthermore the response of octopus cells to cochlear implant (CI) stimulation with the ACE strategy was investigated to determine whether pitch can be extracted from CI stimulation in the same way as from acoustic stimulation. Their ability to extract the pulse rate from single-electrode stimulation was also investigated. The response of the octopus cells to single-electrode stimulation at different pulse rates was used to predict pulse rate difference limens, which were compared to psychoacoustic measurements found in literature. It was found that octopus cells are sensitive to the delay of synaptic inputs on their dendrites but are broadly tuned to this delay. By evaluating the jitter together with the travelling wave delay present in the activity of auditory nerve fibres (ANFs), it was determined that octopus cells may rather act as coincidence detectors, which extract common interspike intervals (ISIs) from many ANFs. The octopus cell model was found to encode the frequency of pure tones in their ISIs for pure tone acoustic stimulation. They were also found to encode the pitch of a vowel in their ISIs, which was the same as the fundamental frequency extracted from the vowel with a speech processing algorithm. The octopus cell model responded to the pulse rate of the CI stimulation and could therefore not extract the frequency of pure tones from CI stimulation in the same way as from acoustic stimulation. The entrainment of the modelled octopus cell population decreased when the pulse rate of a single electrode increased beyond 300 pps. Pulse rate difference limens were predicted from the standard deviation of the ISIs of the octopus cell population response to single electrode stimulation. The predicted difference limens were in the same range as measured values, which suggests that octopus cells may play a role in the measured perceptual limit at 300 pps. From the findings of this study it is suggested that CI stimulation strategies should be developed to encode pitch in the periodicity of their stimulation to enable octopus cells to extract pitch information from CI stimulation.
Automatic tuning of a MIMO PI controller of a flotation bank
(University of Pretoria, 2024-11) Le Roux, Johan Derik; Craig, Ian K.; albertusr7@gmail.com; Richter, Albertus Viljoen
The literature on the automatic tuning of PID controllers is surveyed. The automatic tuning methods are sorted into model based and model-free methods. The methods are further subdivided into the manner the system is perturbed. The method of Bayesian optimization is presented and discussed within the context of automatic controller tuning. The method used to constrain the Bayesian optimization is presented. The level flotation model is given and linearized. The controllers are given and discussed. The controller tuning strategies for both SISO and MIMO controllers are presented. A Bayesian optimization automatic tuner is implemented on SISO and MIMO PI controllers used to control the pulp levels in a flotation bank. The implemented automatic tuner achieves performance improvement for both SISO and MIMO cases without any noise present. The MIMO controller tuning is also implemented on a system with measurement noise present and the Bayesian optimization automatic tuner settings performed on par with a state of the art forward-feeding controller. The Bayesian optimization automatic tuner is constrained to ensure safety and stability. The constraints are found using a structured singular value analysis.
Model development and validation of an industrial natural gas well production network
(University of Pretoria, 2024-10) Craig, Ian K.; Le Roux, Johan Derik; DzedzemaneR@gmail.com; Dzedzemane, Rudzani
A transient state-space non-linear model is developed for a natural gas production network fed from multiple gas wellheads. The state-space model is developed by making use of the spectral element method for pipeline spatial discretization. Wellhead models are integrated into the pipeline models by making use of suitable boundary conditions based on the characteristic compatibility method. The models are validated against a large scale natural gas well production network. The validation shows that the model has a good prediction performance based on a low normalized root mean square error of at most 5.08% and a high Pearson correlation coefficient with measured plant data of at least 0.94. The good prediction response of the developed transient models make them suitable for use in model-based optimal control of natural gas well production networks. The resulting dynamic model can be easily adapted to a gas network of any configuration due to its modular form.
Deep learning approaches for fingerprint localization in low-power wide area networks
(University of Pretoria, 2024-10) Myburgh, Hermanus Carel; De Freitas, Allan; albert.lutakamale@tuks.co.za; Lutakamale, Albert Selebea
In recent years, low-power wide area networks (LPWANs), particularly long-range wide area networks (LoRaWAN), have been increasingly adopted into large-scale internet of things (IoT) applications due to their ability to offer energy-efficient and cost-effective long-range wireless communication. The need to provide location-stamped communications to IoT applications for meaningful interpretation of physical measurements from IoT devices has increased the demand to incorporate location estimation capabilities into LPWAN networks. Factors such as high-power consumption, high implementation costs, and poor localization performance in urban canyons or environments with many obstructions render outdoor localization solutions based on standalone GPS technology unfit for deployment in large-scale IoT applications, where the emphasis is on energy efficiency and cost-effectiveness. Implementing localization methods in short-range wireless communication networks, such as Bluetooth and ZigBee networks, to estimate locations of target nodes in large outdoor environments is also not economically feasible due to their short-range nature, as there will be a requirement for dense deployment of wireless nodes, leading to high implementation costs. In LoRaWAN (one of the key LPWAN technologies operating in unlicensed frequency bands), fingerprint-based localization methods are known to be robust in challenging environments with multipath and non-line-of-sight phenomena, making them relatively more accurate than range-based methods. However, most currently available fingerprint-based localization methods in LoRaWAN networks rely on conventional ‘shallow’ machine learning models. While such models may yield satisfactory results under specific conditions, their complexity tends to increase as the size of training datasets increases, ultimately resulting in a decline in localization accuracy. In this thesis, driven by the goal of improving the performance and efficiency of fingerprint-based localization methods in LoRaWAN networks, two deep learning-based fingerprint-based methods to estimate the locations of target nodes in LoRaWAN networks are proposed. The first proposed method is a branched convolutional neural network (CNN) localization method enhanced with squeeze and excitation (SE) blocks (referred to as the CNN-SE method). The second proposed method is a hybrid CNN-transformer fingerprint-based localization method (referred to as the CNN-transformer method). The main contribution of the first method is the joint use of CNN (proven to be very efficient in learning useful positional information in structured data) and SE blocks, which improves channel-wise interdependencies. The novel contribution of the second method is the development of a hybrid CNN-transformer fingerprinting-based localization model by leveraging the strengths of both CNNs and transformers. CNNs capture features from the input data at the local level, while the attention mechanism of the transformer captures features from the input data at the global level. Adopting a 0.7/0.15/0.15 data split scheme for the training, validation, and test set, respectively, and using the entire LoRaWAN dataset, the CNN-SE method achieved localization accuracies of 291.51 m and 147.55 m mean and median localization errors, respectively, on the test set, using the powed data representation scheme. With the CNN-transformer method, the localization accuracy of 288.1 m and 143.7 m mean and median localization errors, respectively, were achieved, using the same experimental settings. The localization accuracies achieved by these two methods have outperformed the localization accuracies of the currently available state-of-the-art fingerprint-based localization methods in the literature, evaluated using the same publicly available LoRaWAN dataset. An R2 score of 0.93 obtained by both methods further indicates the high degree to which the proposed methods have been able to fit data in their respective regressors, enabling them to localize target nodes with satisfactory localization accuracies.
Perception of timbre features by cochlear implant users
(University of Pretoria, 2024-08) Hanekom, J.J. (Johannes Jurgens); utiaan@gmail.com; Uys, Rudolph Christiaan
Timbre perception is fundamental to music enjoyment. Various cochlear implant (CI) studies have investigated the identification of musical instruments and the perception of timbre, finding that the ability of CI users to perceive timbre is poor compared to normal hearing (NH) listeners. To better understand timbre perception for CI users, the limitations that cause poor timbre perception in CI users should be investigated when performing timbre discrimination tasks. Therefore, the present study investigated the perception of the timbre by measuring discrimination abilities using just-noticeable differences (jnds) for a set of representative acoustic features that underlie timbre perception. Two spectral features, brightness and irregularity (referred to as brightness and IRR), and a temporal feature, logarithmic rise-time (LRT), were identified in the literature as salient timbre features. The timbre features were used in a synthesis model to create a set of nine synthetic instrument tones. The latter allows for independent variation of the timbre features. Synthetic tones were used in a two-alternative forced-choice (2AFC) experiment to measure jnds for NH individuals and CI users for each of the timbre features. The data showed that the jnds of CI users were larger than those of NH individuals. The findings suggested that CI users had difficulty to attend to the timbre feature when performing the discrimination tasks. To investigate whether or not CI users had access to the timbre features, electrodograms were used to analyse the jnds. Electrical stimulation pulse trains of the original instrument sound were generated and compared with the electrical stimulation pulse trains generated at jnd for each of the CI users. Difference metrics were calculated to determine whether CI users had access to the timbre feature or only to the difference between the reference and probe electrical stimulation signal. Spatial and temporal differences between reference and probe stimulation signals showed that CI users did not have access to the timbre feature, but rather to the differences in electrical pulse trains. The extent to which CI users received the timbre features was investigated using feature information transmission analysis (FITA). This estimated the percentage of available information of the timbre features that CI users received. Confusion matrices were predicted from the jnds of CI users to perform the FITA. The results showed that the information received by CI users is user-dependent and that the information received for each of the features is mostly the same within users. These findings support the notion that CI users probably did not attend to the timbre features and conceivably did not have adequate access to these. The representation of the spectral harmonics of the musical instrument tones by the electrical stimuli was investigated. The spectral representation in the electrical stimulus pattern was found to be a distorted version relative to that of the acoustic sound. The study aimed to answer the question To which extent is musical timbre perceived by CI users and what underlies this? A core objective was to understand what constraints underlie timbre perception. It was concluded that CI users do not attend to the timbre features when performing timbre discrimination tasks, and that the electrical stimuli representing the instrument have a distorted spectrum relative to that of the acoustic sound.
Bandwidth extended designs of double-ridged guide horn antennas
(University of Pretoria, 2024-08) Odendaal, Wimpie; Joubert, Johan; benniejacobs777@gmail.com; Jacobs, Benjamin
The explosive growth in bandwidth requirements on antennas and antenna systems, driven by various industries, also drives the need for the ever‐increasing bandwidth of antennas used for testing. The broadband Double-Ridged Guide Horn (DRGH) antenna finds widespread use in antenna measurement and ElectroMagnetic Compatibility/Interference (EMC/I) testing. An example of a DRGH used extensively for testing is the 1-18 GHz DRGH antenna. A study using ElectroMagnetic (EM) simulation was performed to determine the factors that limit the bandwidth of these antennas. All the parts and sub-assemblies of the DRGH were investigated to determine the impact of each part and or sub-assembly on the electrical performance of the antenna. This was done in simulation using reduced complexity models and parametric studies. The design changes that resulted from this study were implemented in several prototype antennas used for verification. It was found that it is possible to design DRGH antennas with bandwidth ratios of 100:1 and possibly beyond. It is expected that at higher frequencies, the limit will be the manufacturing tolerances and technology, and at lower frequencies, the maximum permissible size of the antenna.
Optimal pathway towards building EPC rating improvements align with building energy performance certificate programme
(University of Pretoria, 2024-10-15) Ye, Xianming; u13166426@tuks.co.za; Mokaile, Sifiso
This dissertation investigates an optimal pathway for commercial buildings to improve their Energy Performance Certificate (EPC) ratings by efficiently adopting Energy Efficiency Measures (EEMs). With buildings contributing approximately 40\% of global energy consumption and 36\% of carbon dioxide emissions, enhancing energy performance is critical. EPCs have been introduced globally to assess and promote energy efficiency in buildings. However, the uptake of EPCs and adherence to the recommended EEMs have been limited, especially in countries like South Africa and Scotland. In South Africa, only 1\% of the commercial buildings requiring EPCs have obtained them, and many building owners are hesitant to implement the recommended measures due to lack of trust, financial, and time constraints. This research addresses these challenges by proposing a model that optimises the selection and implementation of EEMs to achieve higher EPC ratings cost-effectively. The optimisation model, developed using MATLAB’s Genetic Algorithm (GA) solver, minimises the investment required while improving the energy performance of buildings step-by-step. The model considers key EEMs such as lighting system upgrades, HVAC improvements, and the integration of renewable energy sources like solar panels and battery storage. The approach allows building owners to make gradual improvements, balancing cost and time, leading to higher EPC ratings over time. To validate the proposed pathway, the study applied the model to a case study of a commercial building in Pretoria, South Africa. The results revealed that by adopting the optimal sequence of EEMs, the building could achieve significant energy savings while progressing through the EPC rating scales. The model demonstrated that a step-by-step approach can reduce the upfront financial burden compared to an aggressive all-at-once strategy. The study also makes recommendations for policymakers to refine EPC standards and support measures that incentivise building owners to participate in energy saving projects.
Reinforcement learning microservices scheduler in intelligent edge computing
(University of Pretoria, 2024-07-01) Abu-Mahfouz, Adnan Mohammed; Hancke, Gerhard P.; u22851217@tuks.co.za; Afachao, Kevin E.
The proliferation of internet of things (IoT) devices and resource-intensive applications has necessitated the development of intelligent edge computing frameworks. These frameworks aim to address challenges in the resource management, service latency, and data privacy of IoT devices. This research investigates the complex problem of microservice scheduling within intelligent edge computing environments. The focus is on optimising quality of service (QoS) metrics such as the latency, network bandwidth utilisation, and energy consumption during execution of resource-intensive applications. To address this challenge, a novel approach called the Bi-generic A2C Microservice Proxy Policy (BAMPP) is proposed. It leverages reinforcement learning (RL) principles to optimize microservice deployment in dynamic Edge-Cloud ecosystems. BAMPP uniquely considers the intricate inter-dependencies among microservices and adapts to user mobility in real-world scenarios. This research utilises a simulation platform to reproduce the intelligent edge computing environment, integrating real-world datasets to evaluate the performance of BAMPP against comparative algorithms. The research focuses on three key research points: identifying crucial factors influencing microservice scheduler performance, leveraging RL for optimised scheduling, and assessing the impact of random user mobility on service deployment. The results demonstrate BAMPP's superior performance in reducing energy consumption, minimizing network usage, decreasing execution and migration latency, and enhancing reliability in microservice scheduling compared to current systems. This research contributes to the field of intelligent edge computing by introducing a novel modeling approach, developing an advanced algorithm for joint optimization of scheduling and resource management, and providing comprehensive performance evaluations using realistic simulations. The results of this study have important ramifications for raising the effectiveness and performance of microservice applications in intelligent edge environments, potentially leading to cost savings, enhanced sustainability, and widespread implementation across diverse edge computing scenarios.
Optimal integration of solar home systems and appliance scheduling for residential homes under severe national load shedding
(University of Pretoria, 2024-09) Ye, Xianming; u15094822@tuks.co.za; Twala, Sakhile Nqobile
In developing countries such as South Africa, users experienced more than 1 030 hours of load shedding outages in just the first half of 2023 due to inadequate power supply from the national grid. Residential homes that cannot afford to take action to mitigate the challenges of load shedding are severely inconvenienced as they have to reschedule their demand involuntarily. This study presents optimal strategies to guide households in determining suitable scheduling and sizing solutions for solar home systems to mitigate the inconvenience experienced by residents due to load shedding. To start with, this study predicts the load shedding stages that are used as input for optimal strategies using the K-Nearest Neighbour (KNN) algorithm. Based on an accurate forecast of future load shedding outages, this study formulates inconvenience for residents and loss of power supply during load shedding as the objective function. When solving the multi-objective optimisation problem, four different strategies to fight against load shedding are identified, namely (1) optimal home appliance scheduling (HAS) under load shedding; (2) optimal HAS supported by solar panels; (3) optimal HAS supported by batteries, and (4) optimal HAS supported by the solar home system (SHS) with both solar panels and batteries. Among these strategies, appliance scheduling with an optimally sized 9.6 kWh battery and a 2.74 kWp panel array of five 550 Wp panels, eliminates the loss of power supply probability and reduces inconvenience by 92% when tested in the South African load shedding cases in 2023. More than 18.5 million households in South Africa are affected by load shedding. This results in a potential of 18.5 million unique load profiles. Creating unique optimal solar home systems for each household without evaluating similarities in their load profiles risks duplicating solar home system strategies. Clustering is an unsupervised machine learning method that can group households based on their inherent similarities, minimising intracluster similarity and maximising intercluster dissimilarity. K-means clustering is used in a case study of 781 South African households metered for a year, forming representative clusters of energy demand profiles to identify optimal strategies for multiple households that minimise the impact of load shedding. Three load clusters are identified as optimal, using the Davies-Bouldin criterion to minimise the ratio of within- and between- cluster distances. From K-means clustering, 43% of households are clustered in a low-energy demand load profile with an average daily energy consumption of 4.5 kWh, 42% in a medium energy and 15% in a high energy demand profile, with an average daily energy usage of 10.8 kWh and 22.1 kWh, respectively. Additionally, based on a 94.4% accurate, hourly, one-year-ahead prediction of load shedding outages using the KNN algorithm, we formulate each cluster’s inconvenience and loss of power supply due to load shedding as a multiobjective mixed-integer nonlinear optimisation problem. The results show that optimal scheduling of a low, medium and high energy consumption cluster with optimally sized 2.4 kWh and 0.39 kWp, 4.8 kWh and 0.78 kWp and 7.2 kWh and 1.17 kWp, battery and panel arrays, respectively, minimises the loss of power supply and substantially reduces the inconvenience of involuntary rescheduling by 86.8%, 71.1% and 86.4% for clusters 1, 2, and 3, respectively.
Monopulse radar analysis for cross-polarisation jamming
(University of Pretoria, 2023-11-23) Du Plessis, Warren Paul; u14255309@tuks.co.za; Mosoma, Khahliso
Cross-polarisation jamming is an electronic attack (EA) jammer that takes advantage of the design weakness in the radar antenna. The monopulse antenna with symmetric antennas in the four quadrants and feed symmetry has Condon lobes in its cross-polarisation signal component. The peaks of the Condon lobes are in the ±45◦ and ±135◦ diagonal planes. The cross-polarisation jammer receives the tracking signal from the tracking radar, interchanges the polarisation components, and re-transmits it to the tracking radar. If the jammer has a high JSR, the tracking radar will be forced to use one of the Condon lobes as the tracking lobe. Six monopulse antennas are analysed for cross-polarisation jamming. The jammer’s effects on the radar’s angle tracking accuracy are analysed as the JSR increases. How the antenna polarisation purity affects the effectiveness of cross-polarisation jamming is investigated. How the jammer's polarisation inaccuracy affects its ability to induce angular tracking error is investigated. The simulated results are validated using the measurements of the manufactured antenna. The cross-polarisation jammer can induce angular tracking error, but needs high polarisation accuracy. The mathematical models of the antenna cross-polarisation patterns are derived using three different approaches. These models are used to theoretically analyse cross-polarisation jamming and compare the results with the Feko simulations and measurement results. The axial symmetry in antennas causes Condon lobes in their cross-polarisation component. Two antennas with axial symmetry will have two Condon lobes, while four antennas located in four quadrants with axial symmetry will have four Condon lobes in each quadrant. One of the six antennas was used to validate the axial-symmetry effect on the Condon lobes. The analysis shows that the antenna radiating elements must be symmetrical, and the feed network must be symmetrical to result in symmetrical Condon lobes. The size of the Condon lobes is influenced differently in different antennas. The focal-length-to-diameter (F/D) size influences the Condon lobes in the parabolic reflector antennas. To investigate the effects of F/D size on the Condon lobes, a parabolic reflector antenna with different F/D sizes is designed and analysed. The analysis shows that increasing the F/D reduces the Condon lobes and increases the polarisation purity of the antenna.
The analysis of distributed resources on a load sharing reticulation network
(University of Pretoria, 2023-12-01) Naidoo, Raj; vandermerwe.ca@gmail.com; Van der Merwe, Carel Aron
Traditional reticulation network designs are outdated, based on single value static yearly maximum demands, and do not consider the dynamic nature of load-side DR installations. The increasing presence of privately driven downstream renewable and storage system integration (supported by increasing energy costs, maturing of storage, PV, and inverter technology systems, and an unreliable external network supply) requires time-based analysis to advance beneficial, and mitigate detrimental, shared network parameter changes. Fundamental integration network impacts must be re evaluated for grid integration acceptability and a modernised design approach, dependent on the capacity, capability, implementation, load-to-generation balancing, and power management of symbiotic integrated load-side DR (DG and/or ES) systems. These initial performance factors were analysed by conducting time based impact studies. Key concepts and approaches to the integration of PV DG, BESSs, and the combined DR system were identified and modelled at increasing levels of power penetration and energy arbitrage within the main distinctive reticulation network load profile forms in a visualised time based impact analysis. By identifying individual DR operational parameters and limits, an optimal approach to DR utilisation and power control is defined. Variables include load profiles, load diversity, demands, load factors, PV DG and BESS parameters, system power control, voltage profiles, utilisation factors, reactive power requirements, and fault levels. The maximum levels of DR penetration were defined (creating an upper penetration limit) following the evaluation of DR network parameter impacts and forms the foundation of the power flow control algorithm governing PV DG and BESS operation for equipment synergy and the optimisation of integration advantages. The proposed power control enforces permanent load side maximum demand reductions by up to 32%, with additional energy arbitrage operation enabled during peak period demands. This is achieved by limiting bi-directional power flow internally and maximising the combined DR system capability, utilisation, and operational synergy. Intermittent PV DG is selected for generation support, while more controllable BESS operation is chosen for targeted demand reduction applications in a give-and-take interface across all seasonal changes. The time based analysis, integration methodology, DR penetration limits, and the developed power flow control algorithm provide an expectation baseline for future DR network integration studies, guidance for service agreement inclusions, and the modernisation of traditional network designs without the necessity of an external network smart grid system. This will encourage the integration of higher rated privately driven renewable and energy storage systems to enhance grid advancement for both external and load-side DR integrated networks.
Delay- and disruption-tolerant routing algorithms to support human activity on mars
(University of Pretoria, 2024) Palunčić, Filip; Maharaj, Bodhaswar Tikanath Jugpershad; jason.kamps@tuks.co.za; Kamps, Jason Jack
Deep-space activity is expected to increase rapidly in the coming decades. Most notably, crewed missions to Mars will take place. With humans venturing light minutes away from Earth for the first time, communication becomes challenging. Humans have specific communication needs that become difficult to support in deep space where large propagation delays, high error rates, and intermittent connections are prevalent. Delay- and disruption-tolerant networking (DTN) and the Bundle Protocol provide a reliable communication solution in such challenging environments. The overall performance of DTN protocols is highly dependent on their routing algorithms. With Mars being humanity’s next target in our exploration of the Solar System, this study deals with finding and examining the most suitable routing protocols in the context of Earth-Mars communication. Realistic scenarios of space missions are constructed to enable the comparison of various DTN routing algorithms in simulation. Routing algorithm performance is analysed, and an enhancement to Contact Graph Routing (CGR) is proposed to address a deficiency of the algorithm, improving routing performance in networks featuring parallel channels.
Bi-directional DC-DC converter with energy management and protection capabilities For LVDC grids
(University of Pretoria, 2024-06) Gitau, Michael Njoroge; u15209840@tuks.co.za; Doma, Anesu Emmanuel
The work outlines a framework for enhancing the efficacy of current LVDC microgrid protection techniques. Currently, the two most significant challenges are the detection and interruption of fault currents. The primary aim of a protection strategy is to maintain the dependability of a power system by selectively isolating the components that are responsible for the fault occurrence. Consequently, it is imperative to interrupt the fault current before it reaches the components' maximum ratings. A proposal has been put forth for the implementation of a bidirectional converter to verify the functionality of a "converter cascaded with an Impedance Source Circuit Breaker (ISCB)" system. Contemporary investigations on DC microgrids suggest that the converter and impedance source breaker integration is functional; however, these two pivotal components have been analyzed separately, with the presumption of effortless integration. The combination is expected to exhibit fault current interruption capabilities and function as an energy hub. The analysis and design of a converter operating in Average Current Mode control (ACM) and an ISCB are conducted as separate entities. This work presents a proposed methodology for validating protection features. The obtained simulated results provide confirmation of the successful interruption of the circuit and ripple reduction on the DC branch input current.
Energy-efficient adaptive data rate optimisation scheme for LoRaWAN
(University of Pretoria, 2023-11) Hancke, Gerhard P.; Abu-Mahfouz, A.M.I. (Adnan); rachel.kufakunesu@tuks.co.za; Kufakunesu, Rachel
The Internet of Things (IoT) is a paradigm that has revolutionised wireless network technologies worldwide owing to its low power consumption, low cost of deployment, long-range communication, and its ability to accommodate a substantial number of end devices (EDs). Many industries have adopted the use of IoT to improve decision-making, to function more efficiently, to automate various operations, and to minimise energy usage, thus helping in the reduction of greenhouse gas emissions. Among the IoT technologies, Low Power Wide Area Networks (LPWANs) perform a pivotal function in delivering efficient and scalable connectivity for this ever-expanding wireless communication system. One prominent LPWAN technology, Long Range Wide Area Network (LoRaWAN), has achieved widespread adoption attributable to its operation in the license-free sub-gigahertz frequency band, scalability, low bit rate, and energy efficiency. LoRaWAN networks consist of low-power end devices connected in a star-of-star topology to a network server (NS) through a gateway (GW), enabling seamless exchange of data connected through an IP-based back-haul connection. The EDs are energy-constrained as they are battery operated and can be static or mobile and can sometimes be deployed in difficult-to-reach places, hostile or hazardous environments, necessitating a long battery life lasting several years. Wireless communication is the major source of energy consumption due to interference and packet collisions during packet transmission. It is essential to limit energy utilisation while maintaining the communication between the end devices and the gateway. This study focuses on enhancing energy efficiency in LoRaWAN networks to extend their network lifetime. An essential aspect of LoRaWAN is the Adaptive Data Rate (ADR) scheme, which optimises Quality of Service (QoS) requirements, battery life of EDs, and overall network performance. ADR manages transmission settings such as transmission power (TP), spreading factor (SF), bandwidth (BW), and coding rate (CR) based on the link budget. By optimising ADR, we can reduce airtime, increase network capacity, and improve energy efficiency. Following the introduction of the LoRaWAN protocol in 2015 by the LoRa Alliance, there has been an absence of a standardised ADR implementation in the LoRa specification. It does not define the way the network server controls EDs regarding data rate adaptation. This has led to various ADR schemes being proposed by different vendors to accommodate diverse IoT scenarios and QoS requirements, posing challenges to reliability and suitability. A comprehensive literature review of existing ADR schemes highlighted their focus on scalability, throughput, and energy efficiency. The existing ADR schemes use different algorithms with different computational complexities to optimise the data rate, depending on the different goals such as coverage, received signal strength, congestion, capture effect and channel contention. The issue of energy consumption emerged as a major challenge. To address this challenge, this research study proposed and implemented a novel fuzzy-logic-based adaptive data rate (FL-ADR) scheme for energy-efficient LoRaWAN communication. The impact of multiple GWs on a LoRaWAN network was investigated and the results were incorporated in implementing the LoRaWAN network using a ns-3 based LoRaWAN simulator, a widely used open source simulation platform. The proposed FL-ADR scheme performance was contrasted with other state-of-the-art algorithms. Network performance was monitored by analysing the energy consumption, interference or collision rate, the packets that were lost because the GWs were busy, confirmed packet success rate (CPSR), the uplink packet delivery ratio (UL-PDR) and the energy efficiency of the algorithms. These metrics were evaluated for different data intervals and different network sizes. The simulation results showed that the proposed FL-ADR scheme achieved substantial energy savings of 43% and 14% compared to the standard Semtech-ADR algorithm and the ns-3-ADR algorithm respectively. Although the CPSR and UL-PDR dropped slightly, the FL-ADR algorithm exhibited lower interference/collision rates, confirming its energy efficiency. The FL-ADR managed to efficiently adjust SF and TP despite a trade-off with CPSR and UL-PDR. Additionally, we developed an SNR-based Spreading Factor Interference Rate controlled Adaptive Data Rate Algorithm, SSFIR-ADR, an ADR algorithm that improves packet delivery ratio by reducing packet collision and managing interference. We implemented multiple static end devices connected to a single gateway to resolve the packet delivery ratio challenge without compromising energy consumption. SSFIR-ADR decreases signal interference by managing the SF allocation to reduce the probability of simultaneous transmissions with the same spreading factor in a particular annulus region in the network using a stochastic approach. The performance was evaluated using three state-of-the-art algorithms. The simulation results demonstrated that our proposed approach exhibits an improved packet delivery ratio and interference rate compared to existing solutions. These significant results contribute to the novelty of the proposed adaptive data rate algorithms. In conclusion, this research contributes valuable energy-efficient ADR algorithms for LoRaWAN communication, offering a practical and reliable solution to extend network lifetime and enhance overall network performance in IoT deployments.
Non-line-of-sight identification and mitigation for indoor localization using ultra-wideband sensor networks
(University of Pretoria, 2020) Hancke, Gerhard P.; silva.bjc@gmail.com; De Carvalho e Silva, Bruno Jorge
With the advent of Industry 4.0, indoor localization is central to many applications across multiple domains. Although impulse-radio ultra-wideband (IR-UWB) enables high precision time-of-arrival (TOA) based ranging and localization for wireless sensor networks, there are several challenges, including multi-user interference and non-line-of-sight (NLOS) conditions. NLOS conditions occur when the communication path between receiver and transmitter is obstructed, and these conditions are frequent indoors due to walls and other obstructions. To maintain location accuracy and precision similar to line-of-sight (LOS) conditions, identification and mitigation of these NLOS conditions is crucial. For identification and mitigation methods to be implemented in sensor networks, they must be of low complexity to minimize their influence on localization requirements. This thesis investigates NLOS identification and mitigation for IEEE 802.15.4a IR-UWB sensor networks. The objective of this thesis is to improve location accuracy in NLOS conditions for IR-UWB sensor networks. A comprehensive review of the state-of-the-art in NLOS identification and mitigation is conducted, and limitations of these methods with regards to the use of multiple channels, dependence on training data, mobility and complexity (particularly for applications with time constraints) are highlighted. This thesis proposes identification and mitigation methods that address the limitations found in state-of-the-art methods. A distance residual-based method for NLOS identification is proposed. Compared to conventional NLOS identification which relies on knowledge of LOS and NLOS channel statistics, or analysis of the standard deviation of range measurements over time, this identification method does not rely on these parameters. A NLOS classification method that distinguishes between through-the-wall and around-the-corner conditions using channel statistics extracted from channel impulse responses is proposed. Unlike most methods in literature that focus on distinguishing between LOS and NLOS, this method classifies NLOS conditions into through-the-wall and around-the-corner, therefore providing more context to the location estimate, and consequently enabling mitigation methods to be used for specific types of NLOS conditions. A through-the-wall ranging error mitigation method that relies on floor plans is proposed. A novel model for through-the-wall TOA ranging is proposed and experimentally evaluated. The conventional throughthe- wall TOA ranging model in literature requires many parameters which cannot be calculated in realistic scenarios. Compared to through-the-wall TOA ranging models found in literature, the proposed model relies on information from floor plans to reduce the number of unknown parameters in the model. The results show that NLOS errors caused by through-the-wall propagation are significantly mitigated with the proposed method, resulting in location accuracy which approaches the LOS case. A NLOS mitigation method which corrects location estimates affected by random ranging errors is proposed. This method relies on geometric constraints based on the fact that biases introduced by NLOS conditions in TOA range measurements are positive. The method is evaluated for cases where NLOS ranges are identifiable and cases where they are not identifiable. For the latter case, the results show that the proposed method significantly outperforms state-of-the-art optimization-based mitigation methods in terms of execution time, while retaining similar performance in terms of location accuracy.
Operational cost and emissions minimisation of a multi-microgrid system through energy management
(University of Pretoria, 2019-12) Zhang, Lijun; Ye, Xianming; u12018997@tuks.co.za; Gildenhuys, Tiaan
Existing energy management problems for a multi-microgrid (MMG) system only minimise the operational cost. This study proposes a multi-objective operational cost and emissions minimisation (OCEM) problem for a grid-tied MMG system. The energy management strategy provided by the OCEM problem takes into account the power sharing among all the microgrids (MGs) as well as a price-based demand response programme. The benefits of the OCEM problem are demonstrated by comparing its results with those of a single-objective operational cost minimisation (OCM) problem reported in the literature. These results are obtained by applying both problems to the same MMG case study, which consists of three interconnected MGs and solving these two optimisation problems with a hybrid optimisation algorithm between the genetic algorithm and sequential quadratic programming in MATLAB. When compared to the best solution provided by the OCM problem, the optimal trade-off solution (OTS) provided by the OCEM problem decreases the emissions by 73.07% with a 18.86% increase in the operational cost. The OCEM problem does provide a decision maker with the flexibility to choose the best solution according to the trade-off between the emissions and operational costs, which are competing objectives. The OTS provided by the OCEM problem has several advantages in comparison to the best solution provided by the OCM problem, namely: 1. Increase in the utilisation of the distributed energy resources in particular, the energy generated by the micro-turbines increased by 80.94%. 2. The energy imported from and exported to the main grid decreased by 52.52% and increased by 7.64%, respectively. As a result, the net energy imported from the main grid is negative, which contributes towards emission reduction and main grid support in terms of energy generation. 3. Reduction in the maximum demand of the MMG system from the main grid as the maximum power flow from the main grid to the MMG system decreased by 32.31%. The MMG case study demonstrates the capability of the OCEM problem in designing an energy management strategy, which is cost-effective and minimises the emissions.
Model-plant mismatch diagnosis using plant model ratios for a grinding mill circuit under model predictive control
(University of Pretoria, 2023-11-01) Le Roux, Johan Derik; Craig, Ian K.; heinz.m014@gmail.com; Mittermaier, Heinz Karl
Model-based controllers often extend improved performance to mineral processing plants by leveraging predictive models to account for system dynamics, handling constraints, adapting to changing conditions, and optimizing control inputs. Inaccurate models will cause a deterioration of controller performance, which is often the case for grinding mill circuits. The plant model ratio was developed to diagnose parametric model plant mismatches for first-order plus time delay models. Using a simulation study, the plant model ratio is applied to test the feasibility of using the plant model ratio on a grinding mill circuit. By applying different scenarios of mismatch, some limitations of the plant model ratio are identified and discussed in light of a grinding mill circuit model that is used in model-based controllers. The plant model ratio is capable of identifying parametric model plant mismatches for the model of a grinding mill circuit, specifically changes in the direction of responses. This may occur in cases where disturbances push a grinding mill to operate to the right of the peak of a grind curve.

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