College of Engineering

Permanent URI for this communityhttp://itsupport.cu.edu.ng:4000/handle/123456789/28755

Welcome to the page of the College of Engineering.

Browse

Filters

2018 - 201912020 - 20254

Settings

Author: search.filters.author.Awelewa Ayokunle A.

Search Results

Now showing 1 - 5 of 5
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    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