Electrical & Electronics Engineering

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Electrical & Electronics Engineering

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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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    EFFECTS OF UNIFIED POWER FLOW CONTROLLER (UPFC) ON DISTANCE RELAY TRIPPING CHARACTERISTICS IN THE NORTH-CENTRAL NIGERIAN 330kV NETWORK
    (Nigerian Journal of Technology (NIJOTECH), 2015-10) Yusuf, Latifa
    This paper investigates the effects of UPFC on Distance Relay tripping characteristics in the Nigerian 330kV (North-Central) Network. Its operation is based on impedance measurement at the relaying point. However, the system performance is often impeded by certain operational or structural factors such as load angle, the voltage magnitude ratio at the line ends, pre-fault line loading, and short circuit levels at the line ends. The Unified Power Flow controllers (UPFC) incorporated into the Nigerian 330kV (North-Central) Network were modelled in the environment of Power System Computer Aided Design (PSCAD) and kept within the protected zone of the relay to increase the Apparent Resistance, causing the relay to malfunction. Therefore, it is deduced by simulation analysis that the presence of UPFC in a faulted transmission line loop, protected by distance relay, greatly affects the trip boundaries of the distance relay by setting it to either an over-reaching or an under-reaching state. Hence, the tripping characteristics of distance relay with UPFC located at various points with respect to a fault on a transmission line culminated in three scenarios, the results of which are presented and discussed in this paper.
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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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    Analysis of Double Salient Reluctance Machine Using Total Surface Gap Area
    (2nd International Engineering Conference (IEC2017) Federal University of Technology, Minna, Nigeria, 2017-06-12) Enesi, A. Y; Ejiogu, E. C; Anih, L. U
    In this paper, we analyze the stator-rotor design of a double salient reluctance machine using total surface gap area. The high number of poles in a 4-phase reluctance machine makes it suitable for the analysis. An expression is derived for the total surface gap area which includes the sum of the area of the air-gap (between the inner stator radius and the outer rotor radius), the area between the gaps of the stator poles and the area between the gaps of the rotor poles. The rated torque and the rated power output are expressed through the total surface gap area and the geometrical parameters. The total surface gap area is used to predict the torque ripple and the average torque developed by the machine for different pole arcs, air gaps, number of poles, number of phases and frequencies which are investigated by MATLAB simulation. The stator and the rotor of the machine are drawn by ANSYS software for the purpose of visualization.
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    Parametric osicillations in electric oscillatory system
    (3rd International Engineering Conference (IEC2019), Federal University of Technology, Minna, Nigeria, 2019-06-12) Enesi A.Y; Ejiogu E.C
    The paper presents the parametric oscillations generated in an electric oscillatory system. Parametric oscillations are oscillations that are periodically modulated with time. The modulation depth and the carrier frequency are investigated by MATLAB/Simulink Model developed from Mathieu's equation. With this model, parametric oscillations are generated. The maximum and minimum amplitudes of oscillations for each characteristic number, a and the characteristic parameter, q is determined. The time taken for one oscillation (which is the period) for each characteristic number and characteristics perameter is determined. The relationship between the carrier frequency, the modulation depth and the characteristic number are established through graphical illustrations. These are approximate results of the solutions of Mathieu equation in electric oscillatory system.
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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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    Performance Analysis of Data Normalization Methods
    (International Engineering Conference 2017, 2017-10-17) Ajiboye, Johnson Adegbenga; Aibinu M.A
    Statistical Data Normalization is a very important input preprocessing operation that should be done before data is fed into the training network. However, there is need for a suitable selection of normalization technique since normalization on the input has potential of varying the structure of the data and may impact on the outcome of the analysis. This paper investigates and evaluates some important statistical normalization techniques by studying thirty published papers that used wine dataset available in the UCI repository and their impact on performance accuracy. Results reveal that Min-Max normalization technique had the best performance accuracy of 95.91% on the average among all the other normalization types.
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    ANALYSIS OF SPECTRUM OCCUPANCY PREDICTION RESULTS FOR MAITAMA ABUJA
    (International Conference on Communication and Information Science (ICCIS), 2024) Ajiboye, Johnson Adegbenga; Mary Adebola Ajiboye; Babatunde Araoye Adegboye; Daniel Jesupamilerin Ajiboye; Jonathan Gana Kolo; Abiodun Musa Aibinu
    This research investigates the efficacy of Artificial Neural Networks (ANN) in predicting spectrum occupancy in Maitama, Abuja, Nigeria, focusing on frequency bands ranging from 30 MHz to 300 MHz. The primary objective was to evaluate the accuracy of ANN-based predictions of spectrum usage and compare these predictions with actual measurements. The study employed ANN to forecast spectrum occupancy across various frequency bands, and the predicted data were then compared with empirical measurements to assess the performance of the model. The analysis revealed that prediction errors were generally low across all frequency bands, with most errors falling below 1.5%. Specifically, the 30-47 MHz sub-band demonstrated an average percentage difference between the actual and predicted value of 0.087%, with a maximum error of 1.12% occurring at frequency of 44.65 MHz. For the 47.05-68 MHz band, the average percentage difference was slightly higher at 0.106%, and the maximum error was 2.18% occurring at frequency of 50.2 MHz. In the 68.05-74.8 MHz band, the average percentage error was 0.040%, but with highest error of 0.232% at frequency of 73.95 MHz. The 74.85-87.45 MHz band showed the most accurate predictions with an average error of just 0.010%, and a maximum error of 0.174% at 75.1 MHz. Overall, the highest prediction error was 0.106% in the 47.05-68 MHz band, whereas the lowest was 0.010% in the 74.85-87.45 MHz band. These results highlight the high accuracy of ANN in predicting spectrum usage, demonstrating its potential for effective spectrum management and planning in Maitama, Abuja.
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    STATE OF THE ART ON PATH LOSS MODEL DEVELOPMENT
    (Humminbird Publications and Research International, 2024-01-29) Ibukun Aderele Adeyemi; Jonathan Gana Kolo; Ajiboye, Johnson Adegbenga
    This is a study of path loss prediction modelling. Path loss modelling is widely applied in determining mobile wireless signal propagation in a given environment. This helps radio network planners to have an accurate view of requirements to obtain good quality of service when deploying radio networks. The empirical models are exhaustively analysed and compared with the emerging machine learning models. Also, mention is made of RIS models which are beginning to gather some attention due to their focus on the programmable electromagnetic properties. The study was able to establish empirical models as the most simple and efficient method of path loss prediction models. Attention is paid to the application of these models in both 900MHz and 1800MHz in urban, suburban and rural areas. This is due to the wide application of these frequencies in mobile wireless communication. The machine learning models present better results and give a high level of accuracy for diverse environments. However, they require large volume of data and environmental features extraction at both 2D and 3D to get reliable model. This makes it imperative to carry out field measurement tasks that are basically synonymous with methodologies employed in empirical approach to modelling. The variation in vegetation determines the best fit model for each particular case as well as the derivation of path loss exponent. The RIS modelling approach gives positive views especially at higher frequencies. The tuneable properties of the surfaces give a wide berth in application across different frequency spectrum. Complex and large volume of computation required in use of RIS implies that machine learning models, especially deep learning models will be better off incorporated into the process. It is thus beneficial to the researcher to ensure that a good grasp of the different approaches highlighted is obtained such that the benefits available are merged to produce finer results.