School of Electrical Engineering and Technology (SEET)

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School of Electrical Engineering and Technology (SEET)

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Start date: 2020Has files: Yes

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    Classification and Severity Estimation of Eccentricity Faults in Salient Pole Synchronous Machine using Deep Learning
    (IEEE, 2025) Yusuf, Latifa; Moa, Belaid; Ilamparithi, Thirumarai Chelvan
    The presented research work is focused on the classification and severity estimation of eccentricity faults in Salient Pole Synchronous Machines. Building on our comparative study of Artificial Neural Network and Convolutional Neural Network for eccentricity fault classification, we propose an end-to-end deep learning model, namely Hierarchical Convolutional Neural Network, for eccentricity classification and severity estimation. The deep learning model inherently consists of an eccentricity detection component for fault classification and a severity estimation component for fault quantification. The deep learning model is built using the experimental data of a 3-phase, 2-kW, salient pole synchronous machine. The machine is subjected to 20%, 40%, and 60% severities of static and dynamic eccentricity faults under different loading conditions. Stator line currents and line-to-line voltages obtained from different operating conditions are used to train, validate and test the proposed model. To enhance the model's performance, time delay construction was incorporated to augment the datasets and carefully evaluate the impact of selected raw input features, specifically stator currents and voltages, as well as the load. Among the evaluated scenarios, the use of voltage with time delay (V, TD) as input features produced the best results, achieving 100% classification accuracy and a root mean square error of 0.0046 for static eccentricity and 0.0188 for dynamic eccentricity estimation. Results indicate that the model performs excellently in fault classification and severity estimation. Compared to traditional machine learning models, the presented model is an end-to-end deep learning architecture devoid of manual feature extraction and is robust to load variations.
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    Analysis of Reluctance Synchronous Motor Under Hybrid Fault Condition
    (IEEE, 2023-09) Ghalavand, Fatemeh; Yusuf, Latifa; Ilamparithi, Thirumarai Chelvan
    A small degree of static eccentricity is inevitable due to manufacturing tolerances and assembly imperfections. Therefore, when stator inter-turn fault happens, it is important to analyze it along with static eccentric condition. Unfortunately, there is not much literature that analyzes such a condition. This paper focuses on the analysis of a Reluctance Synchronous Machine (RSM) when subjected to stator inter-turn and static eccentricity faults simultaneously. In particular, the work focuses on determining the impact of relative position between the minimum airgap point and the stator inter-turn fault. The goal of the paper is achieved by simulating a 1.5 hp, 4-pole, RSM using Finite Element (FE) software. Line current data is captured under different fault conditions and motor current signature analysis is carried out. Furthermore, the lower sideband harmonic frequency is reconstructed in time domain using Inverse Fourier Fast Transform. Clarke’s transformation is applied on the reconstructed harmonic frequency currents to estimate the alpha, beta components. Afterwards, Principal Component Analysis (PCA) is implemented on the alpha, beta currents. The major benefits of the work include establishing the impact of hybrid faults on motor current signatures, developing a new measure to predict the relative position of the point of minimum airgap.
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    Permanent Magnet Synchronous Generator Connected to a Grid via a High Speed Sliding Mode Control
    (2022-06-12) Omokhafe J. Tola; Edwin A. Umoh; Enesi A. Yahaya; Osinowo E. Olusegun
    Wind power generation has recently received a lot of attention in terms of generating electricity, and it has emerged as one of the most important sources of alternative energy. Maximum power generation from a wind energy conversion system (WECS) necessitates accurate estimation of aerodynamic torque and system uncertainties. Regulating the wind energy conversion system (WECS) under varying wind speeds and improving the quality of electrical power delivered to the grid has become a difficult issue in recent years. A permanent magnet synchronous generator (PMSG) isused in the grid-connected wind-turbine system under investigation,followed by back-to-back bidirectional converters. The machine-sideconverter (MSC) controls the PMSG speed, while the grid-side converter(GSC) controls the DC bus voltage and maintains the unity power factor.The control approach is second-order sliding mode controls, which are usedto regulate a nonlinear wind energy conversion system while reducingchattering, which causes mechanical wear when using first-order slidingmode controls. The sliding mode control is created using the modifiedsuper-twisting method. Both the power and control components are builtand simulated in the same MATLAB/Simulink environment. The studysuccessfully decreased the chattering effect caused by the switching gainowing to the high activity of the control input.
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    Development of stability charts for double salience reluctance machine modeled using hill’s equation
    (Bulletin of Electrical Engineering and Informatics, 2024-06-10) Enesi Asizehi Yahaya; Emenike Chinedozi Ejiogu
    The paper presents a novel algorithm for the development of stability charts. The second-order differential homogeneous equation describing a double salient reluctance machine with a capacitance connected to its stator winding is transformed into hill’s equation. The circuit components are the stator coil time-varying inductance of a double salient reluctance machine, capacitance and resistance. All these are modeled by hill’s equation. The double salient reluctance machine acts as an energy conversion system. The maximum and minimum inductance of the energy conversion system is measured in laboratory by inductance, capacitance, and resistance (LCR) meter. These values help to determine the inductance modulation index. The inductance modulation indetx, the characteristic constant and the characteristic parameter obtained from modeling equations are used in the MATLAB/Simulink model. The MATLAB/Simulink simulations generate stable and unstable oscillations to form stability charts. The proposed stability charts are in good agreement with the Ince-Stritt stability chart, which is widely applied in physics, mechanics and in electrical engineering, especially where the state of stability of a system or an electric oscillatory circuit is to be determined
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    Investigating the Thresholding Effect and Fingerprint Transformation Using Cross-Correlation Similarity Matching
    (Faculty of Science Lafiya, 2025-01-17) Garuba O.R., Abdullahi, I.M., Dogo, E.M., & Maliki, D
    One of the leading diseases globally is cancer and breast cancer is not exempted. The objective of the WHO Global Breast Cancer Initiative (GBCI) is to reduce global breast cancer mortality by 2.5% per year, thereby averting 2.5 million breast cancer deaths globally between 2020 and 2040. The three pillars toward achieving these objectives are: health promotion for early detection; timely diagnosis; and comprehensive breast cancer management. In this study we propose an early and comprehensive detection technique in combating breast cancer diagnosis by combining the strength of both PSO (Particle Swarm Optimization) and BPSO (Binary Particle Swarm Optimization) to achieve optimal solution. The results obtained indicated the superiority of the Hybrid PSO-BPSO model in detection over an existing solution by achieving an accuracy of 98.82% on both the WBCD and WDBC datasets.
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    Blockchain Technology in Healthcare Systems: Applications, Methodology, Problems, and Current Trends.
    (2021-04-19) Dauda, I. A., Nuhu, B. K., Abubakar, J., Abdullahi, I. M., & Maliki, D.
    Blockchain Technology (BT) is a secured ledger that has the potential to enhance the safety, quality as well as efficiency of healthcare provision. This will benefit healthcare administrators and healthcare end-users. This paper is focused on expanding the significance of blockchain technology in healthcare information. It identifies those aspects that are not being recorded by many researchers in establishing the prospects of Blockchain Technology in the healthcare domain. Accordingly, the paper looked at Blockchain involvement in administering healthcare services such as telemedicine, health information exchange, and electronic prescribing. The review can discover the huge potential of Blockchain technology in healthcare such as in storing healthcare data on a shared Block that is accessible to concerned stakeholders without undue privacy distresses. This research provides the desired guide and identified open perspectives for researchers that will improve the level of adoption of Blockchain in the healthcare domain.
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    Unimodal Medical Image Registration using Elite Opposition Bacterial Foraging Optimization Algorithm
    (JOURNAL OF SCIENCE TECHNOLOGY AND EDUCATION, 2022-09-08) Maliki, D., Muazu, M.B., Kolo J.G., & Olaniyi, O.M
    Medical imaging applications frequently use image registration for a variety of purposes, and the search of an ideal image transformation parameters that align the two images (reference and floating) is still an optimization challenge. Medical image registration has been optimized using different metaheuristics optimization strategies. One method, the Bacterial Foraging Algorithm (BFOA), has issues of poor exploration and low convergence to a better solution. This research work presents the Elite Opposition Bacterial Foraging Optimization Algorithm (EOBFOA) for optimizing unimodal medical image registration. The EOBFOA is an enhanced version of Bacterial Foraging Algorithm (BFOA) using the Elite Opposition Strategy. The proposed EOBFOA uses Root Mean Square Error (RMSE) as a measure to determine the accuracy of the image registration process. The performance of the image registration using the EOBFOA was compared against other existing nature inspired algorithms. The obtained results shown that the proposed EOBFOA outperformed other algorithms in searching for the best optimum transformation parameters for the image registration.
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    Development of an IoT Based Irrigation Control System using Convolutional Neural Network
    (JOURNAL OF SCIENCE TECHNOLOGY AND EDUCATION, 2023-06-22) Adamu, M., Abdul-Malik, U. T., Maliki, D
    The automation of irrigation activities has the potential to revolutionize traditional manual and static irrigation practices, leading to increased productivity with reduced human intervention. Manual irrigation practices often result in water wastage or inadequate water supply to specific crops, as different crops have varying water requirements (crop water need). Moreover, manual irrigation methods consume significant time and effort, especially when the farmland is located at a distance. This paper presents an IoT-based irrigation system that utilizes computer vision technology to capture and recognize crops in the irrigation field using a Convolutional Neural Network (CNN) model. The developed system continuously monitors and maintains the optimal soil moisture content for each specific crop, employing soil moisture and temperature sensors. The control unit of the system is implemented using the Raspberry Pi 3b+ platform. The performance of the developed system was evaluated using two key metrics: Accuracy and Response time. The CNN model achieved high accuracy, with a stabilized accuracy of 95 percent after 50 epochs of training and validation, using a dataset of 800 pictures. This indicates the system's capability to accurately identify crops in the field. The response time of the system was assessed through ten trials, resulting in an average response time of 14.3 seconds, which is considered satisfactory. The findings of this study demonstrate the effectiveness of the proposed IoT-based irrigation system in automating irrigation processes and optimizing water usage. By integrating crop recognition, soil moisture monitoring, and temperature sensing, the system ensures efficient irrigation practices, reducing water wastage and minimizing human effort. The successful implementation of the developed system paves the way for intelligent and dynamic irrigation systems, fostering higher agricultural productivity and sustainable water resource management
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    An Electronic Voting System with Directed Acyclic Graph (DAG)- Based Blockchain Using ShimmerEVM
    (El-Amin University Journal of Computing (EAUJC)., 2024-04-07) Maliki. D., C. Oruche, I. M. Abdullahi, B. G Najashi, O. R. Isah
    This research introduces an innovative electronic voting system that enhances transparency, anonymity, and reliability, aiming to revolutionize both traditional and existing electronic voting methodologies. The system increases accessibility, security, and efficiency in the electoral process. Advanced web development technologies, including NextJs, TailwindCSS, TypeScript, and JWT tokens, are integrated for an improved e-voting experience. This system employs encryption and cryptographic hashes to secure sensitive information, alongside smart contracts on ShimmerEVM— a Directed Acyclic Graph (DAG)-based blockchain—to ensure data persistence and immutability. A user-friendly front-end interface serves as a portal to the web application, enabling seamless interaction with the ShimmerEVM network. A critical feature of the system is the activation of a biometric hardware component, essential for voter registration and participation. ShimmerEVM facilitates the execution of smart contracts, offering a decentralized, transparent, and secure environment without relying on traditional blockchain technology. The focus of this system is on the implementation of security-centric smart contracts, which are pivotal in maintaining voting data integrity and mitigating the risks of vote count manipulation.
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    Intelligent Bi-modal Timetable-aware Biometric Attendance System for Enhanced Classroom Attendance
    (Journal of Contents Computing, 2022-08-22) Abdullah I. M., Maliki, D., Abubakar, A., Jung, Y. A., Kim, K., & Aliyu, A
    Attendance management is integral to many organizations and academic institutions. The manner in which attendance is managed has evolved over the years encompassing various techniques and methodologies. Although significant improvements have been made, existing systems are mostly standalone systems without proper monitoring and control from a central point. This makes it difficult for several attendance devices to be linked by a common scheme like a timetable schedule. This is integral because the lives of students in every academic institution revolves around a schedule of events and classes. There is also a need to maximize the use of the data generated from attendance systems to make meaningful decisions and predictions about students’ academic activities. The research led to the implementation of several modules which includes the key timetable schedule module and an elaborate API structure deployed to a central server for a centralized communication network between attendance devices instead of standalone devices as traditional systems have employed. The research also investigates factors affecting the performance of students and which of these factors is the greater determinant. From the results and dataset investigated, it was found that attendance is in fact not the primary determinant of students’ academic performance. Extra educational support, extra-curricular activities and family support are the top ranked factors affecting academic performance in accordance with the results obtained from this work.