Daniel Esene Okojie

Electric vehicle (EV) smart charging changes the operating point of the vehicle on-board charger (OBC), so conversion efficiency and grid-side power quality can vary materially with the charging current. This study reanalyses an experimental dataset of 38 EV models represented by 39 test units manufactured between 2011 and 2022. The source study established the measured current-dependent efficiency and reactive-power behaviour; the present work extends those measurements through vehicle-level physics-informed loss decomposition, a 24-variable descriptive feature set, formal statistical comparisons, exploratory clustering with stability analysis, and annual energy scenario sensitivity. A three-component loss model separating fixed, current-proportional, and ohmic effects reproduced the measured efficiency curves with a median root mean square error of 0.22 percentage points. For clustering, an exact redundant efficiency descriptor was removed, and eight variables were retained. Stage 1 separated four motor-winding-integrated chargers from 32 dedicated OBCs (silhouette coefficient 0.474; Ward adjusted Rand index 1.00). The finer four-way partition of the dedicated OBC subset had a lower silhouette coefficient of 0.324 and showed substantial bootstrap sensitivity; it is therefore reported as exploratory rather than as a universal OBC typology. Peak efficiency increased by 0.53 percentage points per model year, whereas the fitted fixed-loss coefficient showed no significant temporal trend. Across 21 paired vehicles, the mean same-current difference between the three-phase and curtailed single-phase operation was 6.32 percentage points; because total transferred power also changes with phase count, this value is not interpreted as an isolated causal phase effect. For a 2500 kWh/year battery-delivered reference demand, the minimum operation supported a current produced at an extreme-case fleet-average, with an additional conversion loss of approximately 190 kWh/year relative to operation at the most efficient measured set-point. Sensitivity analysis shows that the additional energy scales strongly with annual demand and with the fraction of energy charged at low current. The resulting vehicle-specific loss parameters provide a reproducible basis for OBC-aware smart-charging studies, while the dataset-derived behavioural groups require validation on independent vehicles and operating conditions.

High-voltage electric-vehicle (EV) fast charging requires accurate coordination between the off-board charger and the battery management system during voltage and current negotiation. This study evaluates a Hybrid Particle Swarm Optimization-Whale Optimization Algorithm (HPWOA) schedule for training a dual-output feedforward neural network that directly estimates ChargePower_kW and ChargeCurrent_A. Ten protocol-state and battery-condition variables were used as inputs. The 158-dimensional neural-weight vector was optimized using 75 Particle Swarm Optimization (PSO) iterations, followed by 75 Whale Optimization Algorithm (WOA) iterations. Using the supplied 500-record dataset, a reproducible 30-seed sample-level evaluation was conducted with a common 3775 fitness-function-evaluation budget for PSO, the WOA, the SFSA, and the HPWOA. The Stochastic Fractal Search Algorithm (SFSA), therefore, used 30 iterations because it evaluates five diffusion candidates per individual. The reported HPWOA mean ± standard deviation (SD) was RMSE = 4.658 ± 0.986 kW and R² = 0.843 ± 0.071 for power, and RMSE = 12.686 ± 2.687 A and R² = 0.858 ± 0.059 for current. The HPWOA outperformed the WOA and SFSA, but not standalone PSO. A conventional mini-batch Adam-trained dual-output neural network produced RMSE = 1.704 ± 0.121 kW and 4.946 ± 0.273 A, and R² = 0.980 ± 0.003 and 0.979 ± 0.002, respectively. Charger-grouped five-fold validation gave the HPWOA R² = 0.857 ± 0.045 (power) and 0.873 ± 0.048 (current). An analytical P = V × I reconstruction was physically consistent in construction and did not show a statistically significant power–RMSE difference from direct HPWOA outputs. The results, therefore, position the two-phase schedule as a reproducible comparative baseline rather than as a demonstrated replacement for gradient-based training or a physically constrained reconstruction.

The integration of the Internet of Things (IoT) into power quality monitoring (PQM) has modernized electrical systems by enabling real-time acquisition and analysis of PQ data. While these advances improve monitoring efficiency and responsiveness, they introduce challenges related to scalability, cybersecurity, interoperability, and the lack of standardized protocols. This paper presents a structured review of IoT-based PQM technologies, with an emphasis on system architectures, machine learning techniques, and blockchain-based security approaches. The evolution of PQM systems is outlined, and recent real-world implementations are examined to highlight practical strengths and limitations. Research gaps are identified, and recommendations are provided to guide future development. Overall, the paper synthesizes the state of the field and offers actionable insights for researchers, utilities, and policymakers seeking reliable, secure, and adaptable PQM infrastructures. Cite this article as: O. U. Omeje, A. O. Ejimofor and D. E. Okojie, “Internet of things–based power quality monitoring systems: technologies, challenges, and future directions—A review,” Electrica, 26, 0183, 2026, doi:10.5152/electrica.2026.25183.

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.

Electric vehicle (EV) adoption in South Africa remains constrained by high upfront purchase costs, limited charging infrastructure, and policy uncertainty, creating a need for lower-cost and locally relevant pathways to transport decarbonisation. This study evaluates the feasibility of converting a legacy light commercial pickup platform from internal combustion engine (ICE) propulsion to battery-electric propulsion through integrated component sizing, longitudinal vehicle simulation, and techno-economic assessment. A retrofit architecture comprising a traction battery, inverter-controller, electric motor, and DC-DC converter was developed using first-principles vehicle dynamics and energy-demand analysis. The resulting configuration employed a 40 kW AC induction motor, an approximately 28 kWh battery pack, a 40–60 kW inverter with 60 kW peak capability, and a 0.75–1.2 kW auxiliary DC-DC converter. Simulation over a representative 1000 s drive cycle showed stable speed tracking, sustained vehicle motion over approximately 10 km, and peak battery currents exceeding 300 A during acceleration, while regenerative braking reduced net cumulative energy consumption relative to gross demand. The economic analysis indicated that the retrofit pathway yielded the lowest cumulative total cost of ownership over most of a 10-year horizon, with breakeven relative to the used ICE baseline occurring at approximately 3.4 years. Lifecycle analysis further showed that the retrofit configuration achieved the lowest combined production and operational carbon burden among the compared vehicle pathways. These findings indicate that ICE-to-EV retrofitting of legacy light commercial vehicles can provide a technically feasible, economically competitive, and environmentally advantageous electrification strategy for South Africa and comparable emerging markets.

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.

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.

Motivated by South Africa's need in the transition to a net-zero economy; this study 10 investigates the integration of renewable energy sources (RES) into oil refineries, considering 11 therein unique challenges and opportunities. The research focuses on optimizing RES allocation 12 using Particle Swarm Optimization (PSO), a data-driven approach that adapts to real-time 13 operational conditions. Traditional energy management systems often struggle with the inherent 14 variability of RES, leading to suboptimal energy distribution and increased emissions. Therefore, 15 this study proposes a PSO-based renewable energy allocation strategy specifically designed for oil 16 refineries. It considers factors like the levelized cost of energy, geographical location, and available 17 technology. The methodology involves formulating the optimization problem, developing a PSO 18 model, and implementing it in a simulated oil refinery environment. The results demonstrate 19 significant convergence of the PSO algorithm, leading to an optimal configuration for integrating 20 RES and achieving cost reductions and sustainability goals. The optimization result of 4,457,527.00 21 Rand achieved by iterations is much better than the result of 4,829,638.88 Rand acquired using 22 Linear Programming as the baseline model. The mean cost, indicating consistent performance, has 23 remained at its original value of 4,457,527.00 Rand, highlighting the convergence. Key findings 24 include the average distance measure decreasing from 4.2 to 3.4, indicating particle convergence; 25 swarm diameter decreasing from 4.7 to 3.8, showing swarm concentration on promising solutions; 26 average velocity decreasing from 7.8 to 4.25, demonstrating refined particle movement; and the 27 optimum cost function achieved at 4,457,527 Rand with zero standard deviation, highlighting 28 stability and optimal solution identification. This research offers a valuable solution for oil 29 refineries seeking to integrate RES effectively, contributing to South Africa's transition to a 30 sustainable energy future.

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.