Vol 13 Issue 4 October 2025-December 2025

Abstract: This paper analyses the performance and safety issues of present-day electric vehicles (EVs) in terms of available literature, current technology developments, environmental and economic concerns, challenges and future perspectives. Performance metrics such as range, efficiency, charging speed, thermal management and drivetrain responsiveness are assessed in combination with safety considerations like battery systems and high-voltage safety including crashworthiness and software/control risks as well as charging-infrastructure risks. Specific passenger EVs, fleet promotions, and policy reactions are highlighted in case studies. The paper ends with suggestions on future directions, policy and industry practices that further EVs performance and safety to drive their widespread adoption worldwide.

Keywords: electric vehicles (EVs), current technology developments, environmental and economic concerns.

Title: MODERN ELECTRIC AUTOMOTIVE SYSTEMS PERFORMANCE AND SAFETY ANALYSIS

Author: Idrees M A Alkandari, Mansour A KH Aljamaan

International Journal of Electrical and Electronics Research  

ISSN 2348-6988 (online)

Vol. 13, Issue 4, October 2025 - December 2025

Page No: 38-41

Research Publish Journals

Website: www.researchpublish.com

Published Date: 31-October-2025

Abstract: This study examines the technical and economic viability of integrating steam turbines into embedded power generation systems in the Enugu region of Nigeria. Faced with unstable grid supply and increasing industrial energy demands, the region presents a strong case for decentralized generation solutions. Using a combined-cycle configuration that recovers exhaust heat from gas turbines, the research models the performance of a steam turbine system designed to enhance overall efficiency and energy utilization. Mathematical models were developed to simulate energy recovery and turbine output using MATLAB/Simulink. The results show that the steam turbine contributed approximately 0.70 MW of additional power with a local efficiency of 24%, representing a significant secondary utilization of thermal energy without additional fuel input. These findings highlight the potential of cogeneration to improve operational efficiency, reduce transmission losses, and promote sustainable industrial energy supply. The study recommends targeted policy incentives, local capacity development, and streamlined regulations to facilitate the adoption of steam turbine-based embedded generation across the region.

Keywords: Steam Turbine, Enugu Region, Embedded Power Generation, Cogeneration, Operational Efficiency.

Title: STEAM TURBINE DEPLOYMENT IN EMBEDDED POWER GENERATION SYSTEM IN ENUGU REGION, NIGERIA

Author: Omeni C. G, Odeh A. A, Onuigbo C.M, Ene P.C

International Journal of Electrical and Electronics Research  

ISSN 2348-6988 (online)

Vol. 13, Issue 4, October 2025 - December 2025

Page No: 31-37

Research Publish Journals

Website: www.researchpublish.com

Published Date: 24-October-2025

Abstract: The reliability and efficiency of power transmission in Nigeria's 330kV network are significantly threatened by voltage instability, particularly under conditions of heavy load, fault disturbances, and network contingencies. This study presents an intelligence-based Super Capacitor (IBSC) technique as a strategic solution for enhancing voltage stability across the transmission network. The proposed method integrates advanced artificial intelligence algorithms with high-performance energy storage devices (super capacitors) to dynamically support voltage levels, compensate for reactive power, and mitigate transient disturbances. Simulation models developed in MATLAB/SIMULINK environment demonstrate that the intelligence-based super capacitor system provides a rapid response to voltage fluctuations and improves system resilience during voltage sag events. Comparative analysis with traditional compensation techniques reveals superior performance of the IBSC approach in maintaining voltage profiles within acceptable thresholds of 0.95 to 1.05 per unit volts. The research highlights the potential of intelligent energy storage integration in modern power systems and recommends the adoption of the proposed technique for improved voltage regulation, grid stability, and overall power system reliability in Nigeria’s transmission network. The conventional faulty bus1 per unit volts was 0.93p.u.v. This caused an intermittent power supply in the transmission network. On the other hand, when an intelligent based super capacitor was integrated into the system, it instantly stabilized the voltage to 1.028p.u.v. And the conventional faulty bus 25 per unit volt was 0.947p.u.v, thereby caused consistent power failure. Meanwhile, when an intelligent-based super capacitor was imbued into the system, it simultaneously attained voltage stability of 1.04p.u.v, thereby enhancing a consistent power supply in the transmission network. Finally, with these results obtained, it showed that the average percentage improvement in voltage stability in the Nigerian 330kV transmission network when an intelligent-based super capacitor was integrated into the system was10%.

Keywords: Improving, voltage, stability, Nigeria, 330kv, transmission, network, intelligent, super, capacitor.

Title: IMPROVING VOLTAGE STABILITY IN THE NIGERIAN 330KV TRANSMISSION NETWORK USING INTELLIGENT-BASED SUPER CAPACITOR

Author: Nnaji I.O., Mgbachi C.A.C., Onuigbo Chika Martha

International Journal of Electrical and Electronics Research  

ISSN 2348-6988 (online)

Vol. 13, Issue 4, October 2025 - December 2025

Page No: 22-30

Research Publish Journals

Website: www.researchpublish.com

Published Date: 23-October-2025

Abstract: This study presents the integration and simulation of a hybrid deep learning framework for induction motor fault diagnosis. The framework combines Convolutional Neural Networks (CNN) for feature extraction with Bidirectional Long Short-Term Memory (Bi-LSTM) for temporal sequence learning. Vibration data representing both normal and imbalance fault conditions were sourced from a benchmark induction motor dataset, pre-processed through downsampling, and balanced using the Synthetic Minority Oversampling Technique (SMOTE). To ensure reliable performance estimation, the model was evaluated under three validation schemes: Stratified K-Fold cross-validation (K=5), Nested Cross-Validation with hyperparameter tuning, and TimeSeriesSplit validation to preserve temporal order. Results show that the CNN-Bi-LSTM achieved the highest average accuracy of 93% ± 0.01 under Stratified K-Fold, outperforming classical baselines such as Support Vector Machines (85%), K-Nearest Neighbors (89%), and Multilayer Perceptron (92%). Nested CV confirmed the model’s generalization ability during hyperparameter optimization, while TimeSeriesSplit demonstrated its adaptability to sequential data streams, a critical feature for real-time monitoring. These findings confirm that integrating CNN and Bi-LSTM yields a robust predictive maintenance framework capable of capturing both spatial fault signatures and temporal dependencies. The hybrid model demonstrates strong potential for deployment in industrial environments, offering reliable and accurate fault detection that supports proactive maintenance strategies.

Keywords: Induction motor, Fault diagnosis, CNN-Bi-LSTM, Predictive maintenance, Stratified K-Fold, Nested Cross-Validation, TimeSeriesSplit.

Title: Integration and Simulation of a CNN–Bi-LSTM Hybrid Model for Fault Diagnosis in Induction Motors

Author: Odeh A.A, C.M. Onuigbo, P.C. Ene, Opata C. E, C. Okechukwu

International Journal of Electrical and Electronics Research  

ISSN 2348-6988 (online)

Vol. 13, Issue 4, October 2025 - December 2025

Page No: 1-21

Research Publish Journals

Website: www.researchpublish.com

Published Date: 06-October-2025