Department of Electrical and Information Engineering

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    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
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    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
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    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.