The emergence of stator-mounted wound-field flux modulation machines have created new possibilities for high torque density electric machines without brushes and slip rings. In this study, the comparative analysis of the wound-field flux switching machine (WFFSM) and DC-excited Vernier reluctance machine (DCVRM) is undertaken in terms of the double stator (DS) topology, targeting improved torque density. The finite element analysis (FEA) and parametric study of the 24-slots/10-pole DS WFFSM (DSWFFSM) and 12-slots/10-pole DS DCVRM (DSDCVRM) is undertaken using the same specifications of 1.5 kW at 300 r/min. The no-load and on-load performance is evaluated in terms of flux linkage, back-EMF, cogging torque, rated torque, overload capability and losses. The DSWFFSM demonstrates higher rated torque density (+14.9%), lower cogging torque (–72.6%) and higher efficiency (+1.6%) compared to the DSDCVRM. The parametric analysis also reveals the low torque ripple and high-power factor potentials of both machines over a wide operating range of excitation and armature currents. Due to comparative advantage, the prototype of the DSWFFSM is developed and tested to validate the study, with satisfactory results.

Motivated by South Africa’s need for the transition to a net-zero economy, this study investigates the integration of renewable energy sources (RESs) into oil refineries, considering the unique challenges and opportunities therein. The research focuses on optimising RES allocation using particle swarm optimisation (PSO), a data-driven approach that adapts to real-time operational conditions. Traditional energy management systems often struggle with the inherent variability of RESs, leading to suboptimal energy distribution and increased emissions. Therefore, this study proposes a PSO-based renewable energy allocation strategy specifically designed for oil refineries. It considers factors like the levelised cost of energy, geographical location, and available technology. The methodology involves formulating the optimisation problem, developing a PSO model, and implementing it in a simulated oil refinery environment. The results demonstrate significant convergence of the PSO algorithm, leading to an optimal configuration for integrating RESs and achieving cost reductions and sustainability goals. The optimisation result of ZAR 4,457,527.00 achieved through iterations is much better than the result of ZAR 4,829,638.88 acquired using linear programming as the baseline model. The mean cost, indicating consistent performance, has remained at its original value of ZAR 4,457,527.00, highlighting the convergence. The key findings include the average distance measurement decreasing from 4.2 to 3.4, indicating particle convergence; the swarm diameter decreasing from 4.7 to 3.8, showing swarm concentration on promising solutions; the average velocity decreasing from 7.8 to 4.25, demonstrating refined particle movement; and the optimum cost function achieved at ZAR 4,457,527 with zero standard deviation, highlighting stability and optimal solution identification. This research offers a valuable solution for oil refineries seeking to integrate RESs effectively, contributing to South Africa’s transition to a sustainable energy future.

Accurate fault location in transmission lines remains a critical challenge for modern power systems, particularly as networks become increasingly complex with the integration of renewable energy sources and smart grid technologies. Traditional fault location methods often need help with high-impedance faults, non-homogeneous line parameters, and dynamic system conditions, leading to extended outage durations and reduced system reliability. This study addresses these challenges by developing an enhanced fault location method that combines conventional electromagnetic principles with advanced machine learning techniques. The methodology employs an approach that integrates modified impedance-based calculations with convolutional neural networks and machine learning regression. The method was validated using a modified IEEE 39-bus test system through simulations, incorporating several fault scenarios and system conditions. Testing utilised synchronised measurements from both transmission line ends, with data captured at 4096 samples per second. Results demonstrate significant improvements over existing techniques, achieving a 99.1% fault detection rate, 98.2% classification accuracy, and 1.2% mean absolute percentage error in location estimation. The method showed particular strength in challenging scenarios, reducing errors by 79.4% for high-impedance faults and maintaining accuracy under variable renewable generation conditions. The proposed method advances power system protection by providing a robust, adaptive solution suitable for modern grid requirements. Its conventional instrumentation implementation facilitates practical adoption, offering improved reliability and reduced outage durations in real-world applications.

Efficiency in the industrial sector is a priority when seeking to minimize production costs and reduction in losses. Harmonic generation and associated losses need to be treated carefully with broad implementation of variable speed induction motor applications. The effect of these induced losses can add up to a significant loss. This study uses statistical tools alongside synthesized models to deduce and compare outcomes from regulated and unregulated converters in which harmonics may be triggered by switching algorithm of modern switch-mode power supplies and other inductive loads attached to supply bus system. These harmonics accumulate over time due to losses propagating through connected equipment when changes occur on the bus system. In this regard, results obtained from investigating and comparing three original equipment manufacturer variable speed drives that were exposed to voltage unbalance and complex harmonic conditions are presented in this study. A conclusion accomplished from the investigation and comparison suggests that generation of 6-pulse uncontrolled rectifier losses is negligible. Experimental validation confirms that unregulated 6-pulse converter produces on average more harmonics compared to the regulated converter of standard as recommended in guideline to permissible harmonic tolerance.

Accurate prediction of battery thermal runaway (TR) is a critical requirement for electric vehicle (EV) battery management systems (BMSs), as TR remains one of the most severe failure modes in lithium-ion batteries. Conventional neural network training methods may suffer from local optimum entrapment, slow convergence, and unstable performance when applied to nonlinear battery safety data. To address these limitations, this paper proposes a Hybrid Particle Whale Optimization Algorithm-optimized feedforward neural network (HPWOA-FNN) for continuous TR probability prediction and binary high-risk event classification using multivariate EV charging sensor data. The proposed HPWOA combines the rapid convergence capability of Particle Swarm Optimization (PSO) during the initial exploration phase with the exploitation and refinement capability of the Whale Optimization Algorithm (WOA) during the second phase. A global-best transfer mechanism is introduced at the PSO-WOA phase boundary to preserve the best solution identified during exploration and initialize the WOA leader, thereby improving convergence continuity and reducing premature stagnation. The model is evaluated using a 500-sample EV battery-charging dataset containing 12 electrothermal, electrical, mechanical, and environmental features. The proposed HPWOA-FNN outperforms standalone PSO-, WOA-, and Stochastic Fractal Search Algorithm (SFSA)-optimized FNN models across all regression metrics, achieving MSE = 0.000989, RMSE = 0.031442, MAE = 0.027250, R² = 0.9702, and MAPE = 3.8075%. For binary high-risk event detection, HPWOA-FNN achieves the highest AUC of 0.9817 and the lowest false-negative count, reducing missed high-risk events to 7 compared with 9 for PSO, 12 for WOA, and 17 for SFSA. Feature-importance analysis identifies maximum temperature and internal resistance as the dominant predictors, consistent with established thermal runaway mechanisms. The results demonstrate that HPWOA-FNN provides an accurate, interpretable, and computationally practical framework for EV battery thermal runaway prediction and BMS decision support.

Protection schemes are usually implemented in the planning of transmission line operations. These schemes are expected to protect not only the network of transmission lines but also the entire power systems network during fault conditions. However, it is often a challenge for these schemes to differentiate accurately between various fault locations. This study analyses the deficiencies identified in existing protection schemes and investigates a different method that proposes to overcome these shortcomings. The proposed scheme operates by performing a wavelet transform on the fault-generated signal, which reduces the signal into frequency components. These components are then used as the input data for a multilayer perceptron neural network with backpropagation that can classify between different fault locations in the system. The study uses the transient signal generated during fault conditions to identify faults. The scientific research paradigm was adopted for the study. It also adopted the deduction research approach as it requires data collection via simulation using the Simscape electrical sub-program of Simulink within Matrix laboratory (MATLAB). The outcome of the study shows that the simulation correctly classifies 70.59% of the faults when tested. This implies that the majority of the faults can be detected and accurately isolated using boundary protection of transmission lines with the help of wavelet transforms and a neural network. The outcome also shows that more accurate fault identification and classification are achievable by using neural network than by the conventional system currently in use.