Department of Electrical and Information Engineering

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FAULT IDENTIFICATION SYSTEM FOR ELECTRIC POWER TRANSMISSION LINES USING ARTIFICIAL NEURAL NETWORKS
(International Journal of Scientific & Engineering Research Volume 9, Issue 2, 2018-02) Mbamaluikem Peter O.,; Aderemi Oluwaseun S.,; Awelewa Ayokunle A.
Electric power transmission line faults hinder the continuity of electric power supplied and increase the system downtime thereby increasing the loss of electric power transmitted. Early fault detection and classification leads to prompt clearance of faults with an attendant effect of improved reliability and efficiency of the power system network. In view of this, this paper develops an arti-ficial neural network (ANN)-based detector and classifier to indicate and classify respectively a fault on Nigeria 33-kV electric power transmission lines. The transmission lines are modeled in Simulink using SimPowerSystems toolbox in MATLAB. Fault simulations are carried out, and the resulting instantaneous values of voltages and currents are used to develop the proposed fault identification sys-tem using multilayer perceptron feedforward artificial neural networks with backpropagation algorithm. Results are presented to vali-date the effectiveness and efficiency of the developed identification system for detecting and classifying faults. The Mean Square Error (MSE), linear regression and the confusion matrix are used as performance evaluators for the system. The ANN-based identification system achieved MSE of 4.77399e-10 and an accuracy of 100% for fault detection. This indicates that the performance of the developed ANN-based identification system is highly satisfactory and may be practically implemented on the Nigeria transmission lines.
Optimal Maintenance Strategy for Power Transmission Infrastructure
(Conf. Series: Earth and Environmental Science 1492, 2024) Somefu T. E.; Oluseyi P. O.; Babatunde O. M.; Somefun C. T.; Longe O. M.; Samuel Isaac A.; Awelewa Ayokunle
In modern electricity markets, reducing operational costs while improving reliability is a primary concern for power system operators. However, Nigeria's aging power transmission network remains vulnerable, necessitating the implementation of optimal maintenance strategies to enhance system reliability. This study introduces a method for mitigating degradation in transmission components through condition-based maintenance, using a hybrid approach that combines the nonhomogeneous continuous time Markov chain (NHCTMC) for system state detection and the differential evolution (DE) algorithm for optimizing maintenance actions. The method is tested on a substation transmission network under various maintenance scenarios. Results indicate a significant improvement in system reliability (90.3%) and an efficient condition-based maintenance strategy achieving 91.3% power delivery. This approach offers promising potential for enhancing the power delivery capacity of the network.
Bridging the Artificial Intelligence Knowledge and Skill Gaps in Africa: a Case of the 3rd Google Tensorflow Bootcamp and FEDGEN Mini-Workshop
(2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals, 2024) Adetiba Emmanue; Wejin John S.; Oshin Oluwadamilola; Ifijeh Ayodele H.; LAWAL, Comfort Oluwaseyi; Thakur Surendra Colin; Awelewa Ayokunle A.; Kala Raymond Jules; Ajayi Priscilla O.; Akanle Matthew B.; Sweetwiliams Faith O.; Nnaji Uche; Owolabi Emmanuel; Idowu-Bismark Olabode; Sobola Gabriel
In transiting from one civilization to another, technology has played a vital and positive role. In the 21st century, one of the digital developments that is paving ways for human life improvement is machine-assisted technology using Artificial Intelligence (AI). Artificial Intelligence has successfully enhanced man’s capacity in solving complex problems and processes. However, as developed nations continue to reap from the adoption of AI in various fields of human endeavors, the continent of Africa has remained behind, especially in AI-based skills and research. Various governments in developing nations have encouraged the adoption of AI, especially in institutions of learning. However, theoretical adoption without practical experience has remained an ineffective way of bridging the digital divide. In this paper we present the outcome of a practical approach to bridging the AI divide among students and researchers in Africa through funding support from the Google TensorFlow College Outreach Award. A 3-day hybrid bootcamp was organized (11th to 13th December, 2023) using the Google funding in order to equip postgraduate students and researchers with AI and collaborative research skills. A pre-survey method was employed to ascertain the knowledge level of the bootcamp participants. From the pre-surveyed feedback, training sessions on various AI domains were presented, and participant equipped with practical AI skills using a deployed AI-based cloud programming platform running on the private Federated Genomic Cloud (FEDGEN) infrastructure at Covenant University. A post-survey feedback was used to ascertain the effectiveness of this approach. A comparative analysis of the pre-survey and post-survey reveals a 70% improvement of AI skills among participants. This shows that having continuous training session for students and researchers is an effective method in closing the AI skills gap between developed and developing nations.
Impact of solar photovoltaic injection on power quality covenant university distribution network
(Scientific African, 2025) Samuel Isaac A.; Davies Henry A.; Awelewa Ayokunle A.; Abba-Aliyu Shehu; Katende James
This study highlights challenges and solutions and examines the effects of injecting Solar Photovoltaic Distributed Generation (PVDG) on Covenant University’s power quality (PQ) distribution network. Injecting solar PVDG helps the University to reduce grid dependency, lower carbon emissions, and improve energy efficiency. Real-time data of power quality parameters were collected using a 434 series II power analyser over 7 days, including weekdays and weekends during peak and off-peak hours. And the data were compared with IEEE standards. Simulation and analysis were done using both Neplan and Homer. Homer Pro was used to optimize PVDG integration, while Neplan was used for the load flow and harmonic analysis. The significant PQ disturbances identified include voltage imbalances, high total harmonic distortion (THD), and overloads. To address these issues, advanced compensation improvements were made using Unified Power Flow Controllers (UPFC) and Static Synchronous Compensator (STATCOM). Postinjection of the solar PVDG results showed a 0.89 % reduction in active power losses, a 1.3 % improment in power factor (PF), and a 15.6 % decrease in the source current at the 33 kV feeder. The results underscore the importance of optimized solar PVDG injection to maintain power quality and enhance network efficiency
Evaluation and improvement of power quality of distribution network: a case study of Covenant University, Ota
(Frontier Energy Efficiency, 2025-01-09) Samuel Isaac A.; Daudu Afah Toyin; Somefun Tobiloba E.; Awelewa Ayokunle A.; Abba-Aliyu Shehu
Power quality is a global concern, particularly as electronic devices are increasingly supporting modern economies. This research evaluates and proposes improvements for power quality of the distribution network at Covenant University, Ota, Nigeria, where electrical equipment usage contributes to power quality challenges. Measurements and evaluations were carried out in three stages: first, measuring power quality at five campus powerhouses using a Circutor aR6 power analyzer; second, assessing these measurements with Power Vision software; third, simulating the evaluated network with NEPLAN software. The study was conducted during an active school session, with measurements taken at 500 kVA, 11 kV/415 V/230 V on the outgoing circuits for each transformer. The results were benchmarked against IEEE power quality standards and identified issues such as harmonics, total harmonic distortion (THD), overload, and a lagging power factor. The proposed improvements, derived from NEPLAN simulation, included active harmonic filters to reduce harmonics, a shunt capacitor for power factor correction, and load sharing for managing transformer overloads. Simulation results demonstrated that THD was significantly reduced across all powerhouses: CDS from 7.28% to 0.91%, EIE from 10.52% to 3.54%, CST from 16.03% to 0.58%, the Library from 11.92% to 0.12%, and the Male Hostel from 16.71% to 0.24%. These adjustments enhanced THD within specified limits. Additionally, the shunt capacitor increased the power factor to 0.96 from −0.96. These enhancements are expected to extend equipment life, reduce heat loss, and lower utility costs.
DEVELOPMENT OF A PAIRING-ANOMALY DETECTION SYSTEM IN VIDEO SURVEILLANCE USING OBJECT DETECTION MODEL
(2025-03) SODIPO, QUEEN BUSAYO; Covenant University Diseertation
Anomaly detection in social behaviours among students is critical to maintaining a safe and respectful academic environment. This research focuses on developing an object detection-based pairing-anomaly detection system to identify and classify unusual social behaviours, such as hand-holding, which can be perceived as an anomaly and potentially lead to more serious issues like sexual misconduct. Object detection, a most significant branch of computer vision, attempts to locate and identify objects in images or video frames. The other is anomaly detection, identifying data sets that do not follow expected behaviour configurations. Combining object detection and anomaly detection approaches provides a powerful solution to detect and flag anomalous or problematic behaviour in any setting. Object detection-based models have recently gained significant attention for their efficiency in detecting and identifying anomalies of interest in complex scenes at high precision. This study shows the application of deep learning for anomalous event detection with an object detection architecture. Utilising the YOLO (You Only Look Once) architecture, the system is designed to detect and localise anomalies in real-time. The model is trained on a custom holding hands dataset, specifically designed to capture instances of hand-holding alongside the Pascal VOC (Visual Object Classes) benchmark dataset, to ensure versatility across varied scenarios. This approach incorporates transfer learning and data augmentation techniques to and optimise model performance with limited labelled data. Evaluation metrics, including mean accuracy precision (mAP), recall, F1 score, and AUC (Area Under Curve), demonstrate the model's effectiveness, with the Custom Holding Hands Dataset achieving an impressive mAP score of 99.5%. The system is integrated into a web application, enabling real-time anomaly detection and classification. This research contributes to developing computer vision-based pairing-anomaly detection systems for social behaviour analysis, with potential applications in maintaining a safe and respectful academic environment.
DEVELOPMENT OF AN AUTONOMOU AGENT FOR A NUMBER STRATEGY GAME USING DEEP Q-NETWORK
(Covenant University Ota, 2025-03) NKWOR, JANE CHINELO; Covenant University Dissertation
Deep Q-Networks (DQNs) have emerged as a pivotal reinforcement learning algorithm for training autonomous agents in complex decision-making tasks. This study investigates the application of Deep Q-Networks in Numero, a number strategy game that requires logical reasoning and iterative feedback processing. Numero is a number strategy game where players predict an opponent's secret four-digit number in the fewest steps possible by analysing feedback and refining strategies. The study explores Numero's unique challenges, such as sparse reward structures, high-dimensional state-action spaces, and non-deterministic feedback mechanisms. To address these challenges, a Deep Q-Network algorithm augmented with Prioritised Experience Replay(PER) was designed and developed to enhance sample efficiency by prioritising critical experiences during training. The autonomous agent interacts with the custom environment, sampling mini-batches from the replay buffer, performing backpropagation, and updating Q-values to improve decision-making. Hyperparameters, such as learning rate, discount factor, replay buffer and exploration rate, were tuned to optimise the agent's learning efficiency. Comparative analysis was conducted using Reservoir Sampling without Replacement and the Minimax algorithm as a baseline approach. Experimental results show that the algorithm achieved a higher success rate (correctly predicted numbers) and faster convergence than Minimax, reducing the average number of steps required to guess the secret number by more than 100%. Additionally, this algorithm demonstrated superior adaptability in handling dynamic feedback, outperforming Reservoir sampling in long-term decision-making. These findings reveal the effectiveness of Deep Q-Networks in structured feedback-driven environments, suggesting their potential application in logical reasoning and decision-making tasks and that the autonomous agent learns effective decision-making strategies through iterative training and fine-tuning, demonstrating improved performance in predicting the opponent's secret number. Further research directions include extending this approach to multi-agent settings where multiple autonomous agents can compete or collaborate to refine their strategic reasoning and explore its application in real-world scenarios requiring structured feedback processing.
Cuckoo search algorithm approach for optimal placement and sizing of distribution generation in radial distribution networks
(International Journal of Electrical and Computer Engineering Vol. 15, No. 3,, 2025-06) Ojo Kayode; Fanifosi Seyi; Awelewa Ayokunle A.; Samuel Isaac A.
Radial distribution networks (RDNs) often experience power loss due to improper distribution generation (DG) allocation. Strategic DG placement can reduce power loss, minimize costs, and improve voltage profiles and stability. This research optimizes DG placement and sizing in RDNs using the cuckoo search algorithm (CSA). The objective function considers losses across all network branches, and CSA identifies optimal DG locations and sizes. Tested on IEEE 33-bus, IEEE 69-bus, and Nigeria's Imalefalafia 32-bus RDN, the Cuckoo Search technique results in optimal DG locations at buses 6, 50, and 18 with corresponding sizes of 2.4576, 1.852, and 2.718 MW, respectively. Voltage improvements are 0.9509, 0.9817, and 0.9821 p.u, while total active and reactive power losses for IEEE 33-bus are reduced by 49.03% and 45.00%, and for IEEE 69-bus by 63.67% and 61.14%. The CSA approach significantly enhances voltage profiles and reduces power losses in these networks.
ASSESSMENT OF THE IMPACT OF FAULTS IN A DISTRIBUTION NETWORK: A CASE STUDY OF COVENANT UNIVERSITY
(Covenant University Ota, 2025-07) ECHEMITA, Timothy; Covenant University Dissertation
Electrical faults pose significant challenges to the reliable and safe operation of distribution networks, often causing equipment damage, service interruptions, and reduced protection system effectiveness. This research investigates the impact of faults within the Covenant University distribution network. The objectives were to develop a representative network model, identify potential fault types, and assess their influence on overall system performance. A detailed MATLAB/Simulink model of the distribution network was created, and simulations were conducted for five primary fault types: single line-to-ground, double-line, double-line-to- ground, three-phase, and three-phase-to-ground faults, all under steady-state load conditions. The simulation results demonstrated distinct variations in fault current magnitudes and voltage responses depending on the fault type, with three-phase faults producing the highest currents. These results were compared against the interrupting capacities of protective devices installed in the Chapel, College of Science and Technology (CST), and Electrical and Information Engineering (EIE) powerhouses. The analysis revealed instances where simulated fault currents exceeded device ratings, indicating potential weaknesses in the existing protection scheme. Overall, the study emphasizes the importance of simulation-based fault assessment in evaluating protection adequacy and enhancing system resilience. Additionally, the findings provide a reference framework for protection analysis in similar institutional microgrids
DEVELOPMENT OF A HIERARCHICAL ANOMALY DETECTION MODEL IN A FEDERATED CLOUD INFRASTRUCTURE USING ENHANCED GRAPH SAMPLING AND AGGREGATION
(Covenant University Ota, 2025-08) LAWAL, Comfort Oluwaseyi; Covenant University Dissertation
Modern distributed computing systems generate massive volumes of log data, making manual analysis infeasible. Existing methods treat log entries as independent events, failing to leverage structural dependencies and temporal correlations. This limitation is critical in federated cloud infrastructures where anomalies propagate across interconnected services. This research developed a hierarchical anomaly detection model that employs Federated Hierarchical Graph Sampling and Aggregation (Fed-HiGraphSAGE) techniques to enable multi-level anomaly classification in distributed cloud environments while preserving data privacy. FedHiGraphSAGE was built on an Enhanced Hierarchical GraphSAGE architecture, incorporating node features, edge attributes, and hierarchical structure to classify anomalies across five semantic levels: Anomaly, Anomaly-Type, Cloud Component, Application-Type and Specific-cloud-module. The model employs federated learning capabilities, dynamic graph management, hierarchical diagnostic capabilities, adaptive thresholding, and memory-efficient training. It also implemented a HierarchicalStratifiedBalancer to address class imbalance. The model was trained and evaluated using federated learning across three data-contributing regions: Afe Babalola University, Landmark University, and DRC_Congo, with Covenant University serving as the federated learning coordinator. A total of 54,919 system logs were processed from these three regions to simulate real-world federated deployment. The model demonstrated exceptional performance with region-specific accuracies of 91.97% (Afe Babalola), 98.27% (Landmark), and 98.76% (DRC_Congo). Hierarchical metrics confirmed effective multi-level classification with h-precision ranging from 91.82% to 98.99%, h-recall from 90.60% to 98.53%, and h-f1 from 89.95% to 98.66%. The model generated detailed hierarchical anomaly classifications and demonstrated significant performance adaptability across regions while maintaining global model coherence, with federated training reducing the global client’s loss from approximately 0.47 to 0.02 over 15 rounds. This research advances automated system monitoring by demonstrating that federated learning with graph-based representations and hierarchical classification significantly improves anomaly detection performance while enabling cross-regional knowledge sharing. The model’s ability to maintain exceptional performance across multiple classification levels while providing explainable results establishes a new benchmark for automated log analysis in complex distributed systems