Research Articles (Electrical, Electronic and Computer Engineering)

Permanent URI for this collectionhttp://hdl.handle.net/2263/1693

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Priority-based data flow control for long-range wide area networks in Internet of Military Things
(MDPI, 2025-04) Kufakunesu, Rachel; Myburgh, Hermanus Carel; De Freitas, Allan; rachel.kufakunesu@tuks.co.za
The Internet of Military Things (IoMT) is transforming defense operations by enabling the seamless integration of sensors and actuators for the real-time transmission of critical data in diverse military environments. End devices (EDs) collect essential information, including troop locations, health metrics, equipment status, and environmental conditions, which are processed to enhance situational awareness and operational efficiency. In scenarios involving large-scale deployments across remote or austere regions, wired communication systems are often impractical and cost-prohibitive. Wireless sensor networks (WSNs) provide a cost-effective alternative, with Long-Range Wide Area Network (LoRaWAN) emerging as a leading protocol due to its extensive coverage, low energy consumption, and reliability. Existing LoRaWAN network simulation modules, such as those in ns-3, primarily support uniform periodic data transmissions, limiting their applicability in critical military and healthcare contexts that demand adaptive transmission rates, resource optimization, and prioritized data delivery. These limitations are particularly pronounced in healthcare monitoring, where frequent, high-rate data transmission is vital but can strain the network’s capacity. To address these challenges, we developed an enhanced sensor data sender application capable of simulating priority-based traffic within LoRaWAN, specifically targeting use cases like border security and healthcare monitoring. This study presents a priority-based data flow control protocol designed to optimize network performance under high-rate healthcare data conditions while maintaining overall system reliability. Simulation results demonstrate that the proposed protocol effectively mitigates performance bottlenecks, ensuring robust and energy-efficient communication in critical IoMT applications within austere environments.
LSTM-SAC reinforcement learning based resilient energy trading for networked microgrid system
(AIMS Press, 2025-03) Sharma, Desh Deepak; Bansal, Ramesh C.
On the whole, the present microgrid constitutes numerous actors in highly decentralized environments and liberalized electricity markets. The networked microgrid system must be capable of detecting electricity price changes and unknown variations in the presence of rare and extreme events. The networked microgrid system comprised of interconnected microgrids must be adaptive and resilient to undesirable environmental conditions such as the occurrence of different kinds of faults and interruptions in the main grid supply. The uncertainties and stochasticity in the load and distributed generation are considered. In this study, we propose resilient energy trading incorporating DC-OPF, which takes generator failures and line outages (topology change) into account. This paper proposes a design of Long Short-Term Memory (LSTM) - soft actor-critic (SAC) reinforcement learning for the development of a platform to obtain resilient peer-to-peer energy trading in networked microgrid systems during extreme events. A Markov Decision Process (MDP) is used to develop the reinforcement learning-based resilient energy trade process that includes the state transition probability and a grid resilience factor for networked microgrid systems. LSTM-SAC continuously refines policies in real-time, thus ensuring optimal trading strategies in rapidly changing energy markets. The LSTM networks have been used to estimate the optimal Q-values in soft actor-critic reinforcement learning. This learning mechanism takes care of the out-of-range estimates of Q-values while reducing the gradient problems. The optimal actions are decided with maximized rewards for peer-to-peer resilient energy trading. The networked microgrid system is trained with the proposed learning mechanism for resilient energy trading. The proposed LSTM-SAC reinforcement learning is tested on a networked microgrid system comprised of IEEE 14 bus systems.
A quantum-inspired optimization strategy for optimal dispatch to increase heat and power efficiency
(Wiley, 2024-05-30) Vanitha, K.; Jyothi, B.; Seshu Kumar, R.; Chandrika, V.S.; Singh, Arvind R.; Naidoo, Raj; u17410411@tuks.co.za
Combined heat and power (CHP) systems are widely used in industries for their high energy efficiency and reduced carbon emissions. The optimal dispatch of CHP systems involves scheduling the operation of various equipment to minimize the total operational cost while meeting the heat and power demand of the facility. In this research work, a novel quantum-inspired optimization algorithm is proposed for the first time to solve the optimal dispatch problem of CHP systems. The proposed algorithm combines the principles of quantum mechanics with classical optimization algorithms to achieve a better solution. The algorithm uses quantum gates to perform quantum operations on the optimization variables, which allows for the exploration of a larger search space and potentially better solutions than classical algorithms. The proposed algorithm also incorporates a classical optimizer to refine the numerical evaluations acquired from the quantum operations. The performance of the adopted optimization technique was demonstrated by associating it with various other optimization techniques based on factors such as the speed of convergence, computational time, and the quality of the solution. The comparison is made on two standard CHP systems subjected to various quality and inequality constraints. The simulation results indicate that the quantum-inspired optimization technique surpassed the other algorithms in both solution quality and computational efficiency. The implemented algorithm provides a promising solution to the optimal dispatch problem of CHP systems. Future research can further explore the application of quantum-inspired optimization algorithms in other energy systems and optimize the algorithm’s parameters to improve its performance.
Optimal hybrid power dispatch through smart solar power forecasting and battery storage integration
(Elsevier, 2024-05) Poti, Keaobaka D.; Naidoo, Raj; Mbungu, Nsilulu T.; Bansal, Ramesh C.
This study presents a strategy to optimize hybrid power system dispatch for commercial sectors in South Africa while utilizing the day-ahead method to forecast solar photovoltaic (PV) power. The approach utilizes numerical weather prediction (NWP) models obtained from open weather maps and incorporates power plant specifications to generate predictions of the PV power plant’s output. These predictions are then integrated into an optimal control strategy incorporating battery storage. The use of optimal algorithms helps manage PV power plant curtailment during periods of over-generation. It is crucial to optimize PV power systems and ensure a continuous power supply for solar power plants, even during unfavorable weather conditions. Besides, the study develops a model that solves the challenging questions of combining solar power forecasting with an optimal dispatch and demand management scheme. Therefore, there is a need to incorporate battery storage systems through the developed optimal control method to maximize the energy from the PV system and minimize the power from the utility grid. The obtained results demonstrate the effectiveness of the developed model. The winter season presented a lower MAE of 21 kW, an RMSE of 35.4 kW, and a MAPE of 3,1% for PV power output forecasting, showing that the errors during prediction are lower compared to other seasons. It has been observed that 60% of the load is supplied through a combination of PV power and battery storage. Therefore, evidence of the developed optimal hybrid power dispatch with an innovative solar power forecasting model suggests that accurate forecasting can improve system planning and mitigate the necessity of procuring grid power at high electricity prices.
Performance analysis of different control models for smart demand–supply energy management system
(Elsevier, 2024-06) Mbungu, Nsilulu T.; Bansal, Ramesh C.; Naidoo, Raj; Siti, Mukwanga W.; Ismail, Ali Ahmed; Elnady, A.; Abokhali, Ahmed G.; Hamid, Abdul Kadir
Several features of innovative grid technologies can be deployed to improve the overall performance of the power system environment. This can be seen from the generation to the consumption of energy. The two-way communication of smart metering introduces the novel functionalities of the energy management system. This paper presents a practical implementation of using the intelligent metering system. It consists of implementing a nanogrid that optimally coordinates the energy from the solar panel, battery storage and utility grid to supply the end user. The developed model is validated with an optimal value of the state of charge of the distributed energy storage to maximise energy from the solar panel and battery storage while minimising the power received from the utility grid. A demand response scheme is employed to formulate the performance index of the energy management system using three optimal control models: adaptive open-loop control, adaptive closed-loop control and model predictive control schemes. The formulation of the performance index of each approach is a function of the energy flow from different resources depending on the power consumption. The three models have given different insights into the performance of the smart nanogrid, which may be used to the advantage of the grid owner or end user. Through the performance of the optimal strategies, it can be observed that energy management is ensured, and real-time monitoring of the entire system is guaranteed. The performance models facilitate the minimisation of the power from the utility, resulting in savings between 23.7% and 39.240% of the total energy demand from the end user. Besides, the system design is validated by an electrical system to form a real-world innovative nanogrid application in residential environments.
CARA : convolutional autoencoders for the detection of radio anomalies
(Oxford University Press, 2025-02) Brand, Kevin; Grobler, Trienko L.; Kleynhans, Waldo
With the advent of modern radio interferometers, a significant influx in data is expected. This influx will render the manual inspection of samples infeasible and thus necessitates the development of automated approaches to find radio sources with anomalous morphologies. In this paper, we investigate the use of autoencoders for anomalous source detection, based on the assumption that autoencoders will reconstruct anomalies poorly. Specifically, we compare an autoencoder architecture from the literature to two other autoencoder architectures, as well as to four conventional machine learning models. Our results showed that the reconstruction errors of these autoencoders were generally more informative with respect to identifying anomalies than machine learning models were when trained on PCA components. Furthermore, we found that the use of a memory unit in our autoencoders resulted in the best performance, as it further restricted the ability of autoencoders to generalize to anomalous sources. Whilst investigating the use of different reconstruction error metrics as anomaly scores, we determined that they were more informative when combined than they were in isolation. Thus, applying the machine learning models to the combined anomaly scores from the autoencoders resulted in the best overall performance. Particularly, random forests and XGBoost models were the most effective, with isolation forests also being competitive when using a small number of labelled anomalies to tune their hyperparameters. Such isolation forests are also more likely to generalize to unseen classes of anomalies than supervised models such as random forests and XGBoost.
Optimal decarbonisation pathway for mining truck fleets
(KeAi Communications, 2024-09) Yu, Gang; Ye, Xianming; Ye, Yuxiang; Huang, Hongxu; Xia, Xiaohua; xianming.ye@up.ac.za
The fossil fuel powered mining truck fleets can contribute up to 80% of total emissions in open pit mines. This study investigates the optimal decarbonisation pathway for mining truck fleets. Notably, our proposed pathway incorporates power generation, negative carbon technologies, and carbon trading. Technical, financial, and environmental models of decarbonisation technologies are established, capturing regional variations and time dynamic characteristics such as cost trends and carbon capture efficiency. The dynamic natures of characteristics pose challenges for using the cost-effective analyses approach to find the optimal decarbonisation pathway. To address this, we introduce a mixed-integer programming optimisation framework to find the decarbonisation pathway with minimum life cycle costs during the planning period. A case study for the optimal decarbonisation pathway of truck fleets in a South African coal mine is conducted to illustrate the applicability of the proposed model. Results indicate that the optimal decarbonisation pathway is significantly influenced by factors such as land cost, annual budget, and carbon trading prices. The proposed method provides invaluable guidance for transitioning towards a cleaner and more sustainable mining industry.
Integrating demand response with unit commitment in insular microgrid considering forecasting errors and battery storage
(Wiley, 2024-07) Swami, Rekha; Gupta, Sunil Kumar; Bansal, Ramesh C.
In this paper, DR programs are integrated with the unit commitment economic dispatch model for a single day to lower total operating costs for an insular microgrid. The proposed model takes into account the forecasting errors associated with wind, solar, and load demands. A new combined DR program is presented to enhance microgrid operation and financial effectiveness, benefiting microgrid consumers. The price elasticity and consumer profit are the foundation for DR modeling. The optimization problem is developed as mixed-integer nonlinear programming (MINLP) and solved using GAMS software. For the case study, an insular microgrid consisting of two microturbines, a wind turbine, solar photovoltaic, and battery storage is considered. Optimization is carried out under both with and without the DR program. The outcomes show that by implementing TOU and DLC DR programs, the operating cost is reduced by 13.55% and 9.68%, respectively. While consumers experience a financial loss in TOU-DR, they earn profit in DLC-DR. Therefore, a combination of the two, i.e., TOU + DLC DR, is proposed, reducing operating costs by 10.73% while increasing profit for users. The suggested approach benefits the microgrid operator as well as its users, encouraging the construction and operation of insular microgrids in rural or isolated areas.
Multi agent framework for consumer demand response in electricity market : applications and recent advancement
(Elsevier, 2024-12) Saini, Vikas K.; Kumar, Rajesh; Sujil, A.; Bansal, Ramesh C.; Ghenai, Chaouki; Bettayeb, Maamar; Terzija, Vladimir; Gryazina, Elena; Vorobev, Petr
Smart grid can offer load sharing and utilize distributed energy resources to reduce energy consumption costs and potentially earn revenue through energy services. Information and communication technologies (ICT) in the smart grid have opened a lot of possibilities for developing residential Demand Response (DR), which is essential in smart grid applications. DR is a technique that enables customers to participate in the operation of the electricity grid either by shifting or reducing the loads during peak time in response to price signals. The DR program helps utilities ensure power balance and lower the cost of electricity in both wholesale and retail electricity markets. Multi-Agent System (MAS) is a distributed artificial intelligence technique that can be used for the implementation of DR programs in the electricity market. This paper aims to provide a comprehensive review of the MAS application for the implementation of DR programs in electricity markets. This paper highlights a review of 264 research papers that discusses MAS-based DR, MAS-based DR in the electricity market, and various platforms for the development of MAS-based DR. It also summarizes the potential of MAS in other applications of the smart grid along with the MAS research challenges, benefits, constraints for implementation and future research directions in this field.
Time based stiction compensation
(Elsevier, 2024) Gous, Gustaf Zacharias; Le Roux, Johan Derik; Craig, Ian Keith; ian.craig@up.ac.za
Sticking valves tend to cause cycles in control systems used in industry, degrading product quality and yield. Many attempts have been made to alleviate the impact of stiction. Mechanical knockers are used with success to knock loose the sticking components. Most other stiction compensation methods attempt to find ways to move the control output by an amount greater than the stiction band, while still getting the valve position as close as possible to the desired position. This paper shows how, instead of overcoming stiction and getting the valve position to the control output, the valve can be moved such that over time, the valve is on average at the correct position, while still moving the valve in increments that are larger than the stiction band.
The effect of disturbances on plant-model mismatch detection using the plant-model ratio : a surge tank case study
(Elsevier, 2024) Mittermaier, Heinz Karl; Le Roux, Derik; Craig, Ian Keith
The surge tank in a bulk tailings treatment plant aims to reject flow and density disturbances. However, for large disturbances, there may be an inversion in the gain between the water inflow and tank slurry density for a linearized model of the plant. The plant-model ratio (PMR) is a method to diagnose model-plant mismatch (MPM), such as gain-inversion, in the absence of disturbances. This article evaluates the influence of disturbances on the ability of the PMR to diagnose MPM for the surge tank. If the disturbance is measured, as in the case of the surge tank, the PMR is able to detect MPM such as gain-inversion. A controller can be adapted according to the MPM information from the PMR diagnosis.
Stability-constrained contingency analysis for modern power systems
(Elsevier, 2024) Ratnakumar, Rajan; Venayagamoorthy, Ganesh Kumar
Modern power systems comprise diverse nonlinear components, and an increasingly large number of low inertia renewable power sources necessitate the modernization of conventional power system security measures. Contingency analysis (CA), a routine process for power system operators, ensures grid security under unforeseen circumstances by identifying potential issues and enabling proactive measures for uninterrupted power flow. Electromechanical oscillations (EMOs) in the power system that are a threat to stability must be regularly monitored and mitigated. An online hierarchical EMO index integrating time and frequency response analysis can be utilized for system stability assessment. The integration of an EMO index threshold into contingency analysis is presented in this paper to enhance system security. This new approach is referred to as the stability-constrained contingency analysis (SCCA). Typical results for a modified two-area, four-machine power system with large solar photovoltaic plants simulated on a real-time digital simulator (RTDS) are presented. These results demonstrate that SCCA flags potential issues that can arise from EMOs for certain contingencies, whereas traditional CA does not, as it solely considers bus voltage limits and line ratings.
Non-linear model predictive control to improve the mineralogical efficiency of flotation circuits
(Elsevier, 2024) Oosthuizen, Dirk Johannes Jacobus; Le Roux, Johan Derik; Craig, Ian Keith
Flotation optimisation strategies often trade off grade for recovery, without attempting to improve the mineralogical efficiency of the flotation process as part of the automatic control and optimisation strategy. Non-linear flotation characteristics such as air recovery complicate the implementation of such an optimisation strategy due to rapidly changing dynamics, and consequently there is a need for a non-linear controller implementation with real-time parameter estimation, to perform the optimisation. This simulation study demonstrates how the parameters of an air recovery model can be estimated dynamically from measurements commonly available on an industrial flotation circuit, and how dynamic optimisation of the flotation process - in the form of non-linear model predictive control - shifts the grade-recovery curve upwards - implying improved metallurgical efficiency through air recovery maximisation.
Model-plant mismatch detection using the plant-model ratio : the influence of multivariable systems containing both fast and slow dynamics
(Elsevier, 2024) Mittermaier, Heinz Karl; Le Roux, Johan Derik; Craig, Ian Keith
The plant-model ratio, developed to diagnose model-plant mismatch present in model-based controllers, inherits the same limitations from the frequency-based analysis that the method is based on. Nonetheless, the plant-model ratio shows the capacity to counteract the effect of non-linear dynamics within processes due to the ability to diagnose parametric model-plant mismatches for first-order plus time delay models. The plant-model ratio is developed before being validated on the Wood-Berry distillation column. One of the prominent limitations of frequency analysis, the filtering effects of time constant differences, is investigated and quantified for the Wood-Berry distillation column, showing the effect of time constant differences on each parametric model-plant mismatch diagnosis.
Modelling of consumer dynamics to improve fuel gas blending control
(Elsevier, 2024) Sibiya, M.D.; Wiid, Andries Johannes; Le Roux, Johan Derik; Craig, Ian Keith; ian.craig@up.ac.za
Although fuel gas systems represent a large part of industrial chemical processes, there has been limited literature on their modelling and control. The available literature typically neglects the effects of fuel gas consumer dynamics, leaving much of the system’s important dynamic behaviour omitted. This paper aims to contribute to the existing literature on fuel gas control and improve an existing fuel gas control benchmark problem by including the effects of fuel gas consumer dynamics on the system. Two model predictive controllers (MPC) were designed, where the first MPC uses a model that neglects the consumer dynamics and the second MPC uses a model that includes the consumer dynamics. It was found that the MPC neglecting consumer dynamics has a pressure variability 5.8 times higher than the MPC that includes the dynamics. It also has a relative sensitivity index (RSI) of 7.2, indicating the presence of model-plant mismatches (MPM) affecting controller performance.
Automatic tuning of level controllers in a flotation bank using Bayesian optimisation
(Elsevier, 2024) Richter, Albertus Viljoen; Le Roux, Derik; Craig, Ian Keith; derik.leroux@up.ac.za
A flotation bank consisting of 6 cells in series under Single-Input-Single-Output (SISO) Proportional Integral (PI) level control is automatically tuned using Bayesian Optimisation (BO). Open loop step tests from the valve position to the level are used to identify first-order plus time-delay (FOPTD) models for each flotation cell. The PI controller settings are tuned according to the Skogestad Internal Model Control (SIMC) tuning rules. Stability bounds derived from µ-analysis are defined using these SIMC settings. As the optimum achieved by the Bayesian optimiser is largely dependent on the parameter space provided to the tuning algorithm, this space is selected first to ensure stability and secondly for performance. The BO framework is able to tune each of the six SISO PI controllers to provide significantly improved level control over the original SIMC controllers with regards to different forms of the integrated error when the plant is subjected to step changes in the level setpoints and disturbances to the feed flow. This improvement comes at the cost of an increased number of tests to conduct.
Modelling cross-polarisation patterns of axially-symmetrical monopulse antennas for cross-polarisation jamming analysis
(Institute of Electrical and Electronics Engineers, 2025) Mosoma, Khahliso; Du Plessis, W.P. (Warren Paul)
Most radar systems do not consider the situation where the return is dominated by the polarisation orthogonal to the polarisation transmitted by the radar. A cross-polarisation jammer exploits this limitation by transmitting a signal with this orthogonal polarisation to cause an angular error. Mathematical models of crosspolarisation monopulse patterns are derived, and their results are shown to be similar to those of five simulated monopulse antennas and one measured monopulse antenna. The polarisation accuracy of the jammer is shown to play a crucial role, with the jammer needing high polarisation accuracy. The induced angular error increases slowly when the jammer-to-signal ratio (JSR) is between 0 dB and 20 dB and increases faster when the JSR is greater than 20 dB.
The effect of temperature on household hourly electricity consumption : evidence from South Africa
(Elsevier, 2025-03) Ye, Yuxiang; Koch, Steven F.; Ye, Xianming; steve.koch@up.ac.za
Climate change is expected to negatively affect Africa, possibly leading to increased energy needs. However, meeting that need could prove problematic; more than a decade of load-shedding in South Africa is suggestive in that regard. In this research we examine the effect of temperature on electricity consumption, focusing on mainly rural households in South Africa. We apply a series of fixed effects panel models to hourly temperature and electricity consumption data across eight months and twelve locations in the country. We find limited evidence that increased temperatures drive increased electricity use; rather, electricity use increases as temperatures decline, although at temperatures below 10 °C, the gradient is approximately level. Given that few of our study’s households own cooling or heating appliances, the result is not entirely surprising. However, without such appliances, poor rural households will not be able to cope with rising temperatures.
TCP congestion control algorithm using queueing theory-based optimality equation
(MDPI, 2025-01) Ngwenya, Dumisa Wellington; Hlophe, Mduduzi Comfort; Maharaj, Bodhaswar Tikanath Jugpershad; sunil.maharaj@up.ac.za
Internet congestion control focuses on balancing effective network utilization with the avoidance of congestion. When bottleneck bandwidth and network buffer capacities are exceeded, congestion typically manifests as packet loss. Additionally, when packets remain in buffers for too long, a queueing delay occurs. Most existing congestion control algorithms aim to solve this as a constraint satisfaction problem, where constraints are defined by bandwidth or queueing delay limits. However, these approaches often emphasize finding feasible solutions over optimal ones, which often lead to under-utilization of available bandwidth. To address this limitation, this article leverages Little’s Law to derive a closed-form optimality equation for congestion control. This optimality equation serves as the foundation for developing a new algorithm, TCP QtColFair, designed to optimize the sending rate. TCP QtColFair is evaluated against two widely deployed congestion control algorithms: TCP CUBIC, which utilizes a cubic window growth function to enhance performance in high-bandwidth, long-distance networks and TCP BBR (Bottleneck Bandwidth and Round-trip propagation time), developed by Google to optimize data transmission by estimating the network’s bottleneck bandwidth and round-trip time. In terms of avoiding queueing delays and minimizing packet loss, TCP QtColFair outperforms TCP CUBIC and matches TCP BBR’s performance when network buffers are large. For effective network utilization, TCP QtColFair outperforms both TCP BBR and TCP CUBIC. TCP QtColFair achieves an effective utilization of approximately 96%, compared to just above 94% for TCP BBR and around 93% for TCP CUBIC.
Bayesian optimization for automatic tuning of a MIMO controller of a flotation bank
(Elsevier, 2025-03) Richter, Albertus Viljoen; Le Roux, Johan Derik; Craig, Ian Keith; ian.craig@up.ac.za
flotation bank consisting of 6 cells in series where each level is controlled by a Proportional–Integral (PI) controller is tuned using Bayesian Optimization (BO) in simulation. A Multi-Input–Multi-Output (MIMO) inventory controller is tuned to optimize the level response of the entire bank. The objective function defining optimality is a trade-off between disturbance rejection and reference tracking in the form of a weighted average of the integral squared error and the integral time absolute error of the level reference tracking error for each cell. The MIMO inventory controller used is a lower diagonal matrix where each element has a PI controller structure. The controller settings selected by the BO are constrained, assuming that the plant is linear, such that only controllers which produce stable closed-loop responses will result. Structured singular value analysis is performed, before tuning, to confirm that this is the case. The BO automated tuner is able to tune multiple PI elements to provide an overall improvement of the flotation bank level control. The method is applied successfully with and without measurement noise on a simulated plant. For use in industry, since the process is simple to model, the controller can be tuned off-line in simulation. To compensate for model-plant mismatch, once the controller is implemented the BO automatic tuner can be allowed a limited number of steps to obtain the optimal controller parameters. This provides a valuable time-saving tool for a process control engineer to tune an industrial plant quickly and efficiently.

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