College of Engineering

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    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.
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    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.
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    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.
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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.
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    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.
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    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
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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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    CORROSION INHIBITION BEHAVIOUR OF CALF THYMUS GLAND DNA ON MILD STEEL IN SULPHAMIC ACID
    (Covenant University Ota, 2025-04) Ekere Isaac E.; Covenant University Thesis
    Inorganic acid cleaners based on sulphamic acid are frequently employed in industrial equipment cleaning, descaling and acidizing. This application of sulphamic acid in industrial cleaning is not entirely without its drawback as the cleaning action usually leads to dissolution and loss of base metals. The addition of corrosion inhibitors is one of the industrial practices employed to minimize equipment corrosion damage. The purpose of this work was to assess the viability of deoxyribonucleic acid (DNA), extracted from calf thymus gland, as an inhibitor for mild steel corrosion in sulphamic acid medium, and in comparison, with salmon Fish DNA and INDION 5489, a commercial inhibitor. The inhibition process was investigated using weight loss, potentiodynamic polarisation, SEM/EDX and FTIR measurements. Response surface method (RSM) and artificial neural network (ANN) were employed to determine the optimum corrosion inhibition conditions. The weight loss measurements obtained the highest inhibition efficiency of 82.71% at 303 K and immersion time of 6 h by addition of 2.5 mg/L of calf thymus DNA, CTGDNA. The corrosion rate was also observed to decrease with an increase in inhibitor concentration. Potentiodynamic polarisation curves showed a shift in Ecorr < 85 mV an indication that CTGDNA is a mixed inhibitor, suppressing both cathodic and anodic reactions. An RSM generated polynomial model obtained an optimum efficiency of 72% at 303 K, 5.5 mg/L after 2.12 h immersion. Estimation by ANN, with minimal errors, and a higher R2 of 0.983 in comparison to 0.925 for RSM were close to the experimental inhibition efficiency. CTGDNA adsorption on mild steel modelled the Langmuir isotherm with a linear regression coefficient of 0.99. The increase in the activation energy from 37.54 kJ/mol to 52.5 kJ/mol after 2 h immersion; with a similar trend for 4 and 6 h demonstrated that addition of CTGDNA favoured physioisorption. The small and negative value of entropy was an indication that the adsorption of CTGDNA was spontaneous. FTIR confirmed the presence of protective film formed by CTGDNA inhibitor on the mild steel surface at various concentration. SEM images showed reduction in the degradation of mild steel surface in the uninhibited solution after addition of CTGDNA. The comparative studies obtained a weight loss of 0.0036, 0.0047, 0.0072 and 0.0086 mg in 10% sulphamic acid in the presence of CTGDNA inhibitor, salmon fish DNA, conventional cleaning solution and blank solution of 10% sulphamic acid without an inhibitor, respectively. This confirmed that the CTGDNA inhibitor enhanced the 10% sulphamic acid cleaning solution as a suitable and viable cleaning agent for mild steel in comparison with INDION 5489.
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    Sulphamic Acid Corrosion Inhibition: A review Isaac
    (ASEN Journal of Chemical Engineering Vol. 24 No 2, 2024) Ekere Isaac E.; Agboola O.; Ayeni Augustine O.
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    DEVELOPMENT OF SUSTAINABLE ECO-CONCRETE WITH KENAF FIBRE AND COATED RECYCLED CONCRETE AGGREGATE
    (Covenant University Ota, 2025-06) TAIWO-ABDUL DAMILOLA OMOZUAWO; Covenant University Dissertation
    The urgent global demand for sustainable infrastructure has driven innovations in eco-efficient construction materials. This study explores the development of high-performance, sustainable concrete by integrating pozzolanic-treated recycled concrete aggregates (RCA) and kenaf fibre as eco-friendly alternatives to natural coarse aggregates and synthetic reinforcements. The research addresses the inherent limitations of RCA—such as high porosity, residual mortar, and weak interfacial zones—through a surface modification technique involving a blended calcined clay-cement slurry. Simultaneously, kenaf fibre is incorporated to enhance the tensile and flexural properties of the concrete matrix. Concrete mixes were produced with varying RCA replacement levels (30%, 45%, 60%, and 90%) using both untreated and pozzolanic-treated RCA. Comprehensive characterisation, including X-ray fluorescence (XRF), X-ray diffraction (XRD), and scanning electron microscopy (SEM), was employed to assess material and microstructural properties. Mechanical performance was evaluated through compressive, tensile, and flexural strength tests, alongside water absorption and density tests for durability analysis. Statistical optimisation using Response Surface Methodology (RSM) and ANOVA determined the influence of treatment and fibre incorporation on concrete performance. The results indicate that pozzolanic treatment significantly improved RCA concrete properties, with optimal performance observed at 45–60% RCA replacement. Treated mixes achieved a 28-day compressive strength of 36 MPa, a 5.3 MPa split tensile strength, and reduced water absorption to 3%, reflecting improved durability and structural integrity. These enhancements demonstrate the synergy between calcined clay treatment and natural fibre reinforcement. This study substantiates the viability of producing eco-concrete with treated RCA and kenaf fibre, promoting circularity, reducing carbon footprint, and contributing to sustainable development goals. It provides a framework for future applications in structural concrete, aligning with low-carbon construction practices.