Browsing by Author "Abdullateef, A. I"

Now showing 1 - 17 of 17
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    Acquisition of Current and Vibration Data for Rewound Burnt Three-Phase Induction Motor
    (2019) Abdullateef, A. I; Fagbolagun, O.S; Sanusi, M. F
    Induction motors are used in the industries for various applications because they are reliable and rugged. However, late detection of faults and inappropriate maintenance of the machine often leads to damage of the windings which results in production losses and outright replacement. This study presents the use of a data acquisition system (DAS) to acquire current and vibration data of re-designed and re-wound burnt three-phase induction motor for the purpose of the motor faults analysis. The designed number of turns per pole per phase is 253 turns which were distributed in four slots per phase at 64, 63, 63 and 63 turns respectively. The DAS developed was used to acquire the current and vibration data at both normal and fault conditions through tapings prepared for the purpose. The average value of normal current data in the red, yellow and blue phases from the sampled data are 3.4 A, 3.1 A and 2.7A respectively. The average currents during short circuit between phase A and phase B are 11.3 A and 12.2 A respectively while vibration fault data stands at 1.62. Also, for the inter-turn short circuit in phase A, the average value of the currents in the phases are 12.1A, 3.1A and 2.7A respectively. The Mean Square Error value of the acquired and measured data is 0.00002.
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    Consumer Load Prediction Based on NARX for Electricity Theft Detection
    (IEEE, 2016) Abdullateef, A. I; Salami, M.J.E; Tijani, B.I
    A range of load prediction techniques has largely been used for energy management at various levels. However, the data used for the prediction are cumulative energy data, which reveal the activities of consumers and not individual consumer, on the distribution power network. Individual consumer data is essential for real time prediction, monitoring and detect of electricity theft. A new approach of monitoring individual consumer based on consumer load prediction using nonlinear autoregressive with eXogenous input (NARX) network is considered in this study. One month average energy consumption data acquired from consumer load prototype developed was used. Consequently, 5-minute step ahead load prediction was achieved. The NARX architecture was based on nine hidden neurons and two tapped delay and the network trained using Bayesian regulation backpropagation technique. The data set contains a total of 8928 data points representing energy consumed at five minute interval for one month. The data was divided into two sets at ratio 70:30 for training and validation, respectively. The training data equals 6206 while the validation data is 2722. MATLAB environment was used for the processing of the data. The training and validation MSE is 0.0225 and 0.0533 respectively, while the total time for the training is 0.016s.
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    Control of an inverted pendulum using mode-based optimized lqr controller
    (IEEE, 2013) Tijani, I.B; Akmeliawati, R; Abdullateef, A. I
    This paper presents an evolutionary optimization based LQR controller design for an inverted pendulum system. The objective is to address the challenges of appropriate design parameters selection in LQR controller while providing optimal performance compromise between the system control objectives with respect to pendulum angle and position response. Hence, a Multiobjective differential evolution algorithm is proposed to design an LQR controller with optimal compromise between the conflicting control objectives. The performance of the MODEbased LQR is benchmarked with an existing controller from the system manufacturer (QANSER). The performance shows the effectiveness of the proposed design algorithm, and in addition provides an efficient solution to conventional trial and error design approach.
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    Design and Implementation of a Cloud-Based Load Monitoring Scheme for Electricity Theft Detection on a Conventional Grid
    (Jordan Journal of Electrical Engineering, 2022-12) Abdullateef, A. I; Sulaiman, A; Issa, A. O; Zakariyya, Sikiru Olayinka
    Electricity theft is one of the problems - encountered by the utilities - that leads to losses of revenue. Manual monitoring of the consumers’ activities which shows the energy data consumed on the conventional grid has largely contributed to electricity theft on the grid. Significantly, the energy meter deployed to monitor the load cannot store and transmit energy data in real-time. This has made electricity theft on the grid unnoticed. This paper presents the development of a monitoring scheme for an electronic meter on the conventional grid with the capability to monitor, store and transmit consumers’ energy data to the cloud. It consists of two units: the indoor and the outdoor unit. Energy data is transferred wirelessly between the units via the Wi-Fi modules. The outdoor unit compares the data and transfers the outcome to the ThinkSpeak cloud server. The transferred energy data can be accessed in real-time from the cloud or downloaded in comma-separated values format for further use. In order to verify the functionality of the proposed scheme, two scenarios of electricity theft - partial bypassing and full bypassing - are carried out. The obtained results show that the scheme can detect the theft and log the data to the cloud successfully
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    Detection and Classification of Stator Short-Circuit Faults in Three-Phase Induction Motor
    (Journal of Applied Science & Environmental Management, 2020) Abdullateef, A. I; Fagbolagun, O.S; Sanusi, M. F; Akorede, M.F; Afolayan, M.A
    Induction motors are the backbone of the industries because they are easy to operate, rugged, economical and reliable. However, they are subjected to stator’s faults which damage the windings and consequently lead to machine failure and loss of revenue. Early detection and classification of these faults are important for the effective operation of induction motors. Stators faults detection and classification based on wavelet Transform was carried out in this study. The feature extraction of the acquired data was achieved using lifting decomposition and reconstruction scheme while Euclidean distance of the Wavelet energy was used to classify the faults. The Wavelet energies increased for all three conditions monitored, normal condition, inter-turn fault and phase-to-phase fault, as the frequency band of the signal decreases from D1 to A3. The deviations in the Euclidean Distance of the current of the Wavelet energy obtained for the phase-to-phase faults are 99.1909, 99.8239 and 87.9750 for phases A and B, A and C, B and C respectively. While that of the inter-turn faults in phases A, B and C are 77.5572, 61.6389 and 62.5581 respectively. Based on the Euclidean distances of the faults, Df and normal current signals, three classification points were set: K1 = 0.60 x 102, K2 = 0.80 x 102 and K3 = 1.00 x 102. For K2 ≥ Df ≥ K1 inter-turn faults is identified and for K3 ≥ Df ≥ K2 phase to phase fault identified. This will improve the induction motors stator’s fault diagnosis.
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    Detection and Confirmation of Electricity Thefts in Advanced Metering Infrastructure by Long Short-Term Memory and Fuzzy Inference System Models
    (2024) Otuoze, A. O; Mustafa, M. W; Sultana, U; Abiodun, E. A; Jimada-Ojuolape, B; Ibrahim, O; Omeiza, I. O. A; Abdullateef, A. I
    The successful implementation of Smart Grids heavily relies on energy efficiency, particularly through the Advanced Metering Infrastructure (AMI) and Smart Electricity Meters (SEM). However, cyber-attacks pose a threat to SEM, with electricity theft being a primary motivation. Despite the valuable data provided by SEM for analytical purposes, existing methods to identify theft involve cumbersome and costly on-site inspections. This research proposes an electricity theft detection model using the Long Short-Term Memory (LSTM) network. The model employs a collective anomaly approach, defining prediction errors through a threshold and forecast horizon. Suspicious consumption profiles are analysed, and a fuzzy inference system (FIS) implemented in MATLAB 2021b is used to model security risks based on these profiles. The study utilizes energy consumption data from four diverse consumer profiles (consumers 1, 2, 3, and 4) to develop consumer-specific LSTM models for detection and an FIS model for confirmation. Tampered consumer data is identified and confirmed based on selected AMI parameters. While all consumers exhibit suspicious profiles at times, only consumers 2 and 3 are confirmed as engaging in electricity theft. This research provides a robust approach to detecting and verifying fraudulent consumption profiles within the context of AMI, offering a more reliable dimension to theft detection and confirmation.
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    The effect of the use of white noise for masking stuttered speech reconstruction.
    (2018) Alim, S.A; Alang Rashid, N.K; Abdullateef, A. I
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    Effective Remote Control of Several Outdoor Security Lights by SMS and Wifi Technology
    (Arid Zone Journal of Engineering, Technology and Environment, 2017) Akorede, M. F; Fatigun, J. J; Opaluwa, J. A; Abdullateef, A. I; Pouresmaeil, E
    The aim of this study was to design and develop a control system using Short Message Service (SMS) and wireless networking (WiFi) technology to remotely control outdoor security lights in large organisations. The device comprises four main units, namely: the mobile phone or a computer system, the Global System for Mobile communication (GSM) modem, the switching unit and the WiFi module. One feature that makes the developed system better than other related existing works is its ability to use two means of control. It makes use of WiFi when the operator is within the coverage area of the network of about 100 m to the device, at no cost, otherwise it uses SMS containing certain codes to control the lights. A Subscriber Identity Module (SIM) card is placed in the GSM modem and SMS from the transmitter are sent to that mobile number. The module is also constantly checked by the microcontroller unit, processes the information, extracts the message and command from the GSM modem and WiFi module respectively and then acts accordingly. Owing to its simplicity, C programming is used to programme the microcontroller. The developed device when tested with three lighting points operating on 230 V power supply, gives an impressive performance in terms of accuracy and promptness with both SMS and WiFi technology.
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    Fingerprint Based Student Attendance Management System With Automatic Excel Computation
    (LAUTECH Journal of Engineering and Technology, Faculty of Engineering Ladoke Akintola University, Ogbomosho. Available online at, 2018) Abdullateef, A. I; Ekwemeka, B.C; Itopa, V; Makinwa, T.B; Alim, S.A
    Fingerprint is considered to be the best and most widely used biometrics recognition and verification pattern due its uniqueness for every individual. This study focused on the development of a fingerprint students’ attendance system carried out to curb the problems associated with manual methods of taking students attendance in institutions. The design was carried out using appropriate mathematical model, formulae and block diagram representation while Proteus software simulator was used to simulate functionality of the designed circuit. An attendance algorithm was developed and implemented using coolTerm software and Excel spreadsheet. The system was tested using 15 students’ fingerprints which involves enrollment, authentication and report generation processes. Each student was enrolled with a unique identification. During verification and attendance capture at different times, the system exhibits extremely low (0%) False Acceptance Rate (FAR), extremely high (100%) True Accept Rate (TAR) and extremely low (0%) False Reject Rate (FRR). This study has established the effectiveness of students attendance capture using fingerprint as a more secure, credible and error free to impersonation and buddy punching as associated with the existing manual-paper based system
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    Identifying interaction boundary of inverter-based generation in assessing system strength of power grids using relative electrical distance concept
    (Elsevier, 2024) Sanni, S. O; Mohammed, O. O; Abdullateef, A. I; Mohammed, D. S. S; Oricha, J. Y
    The increasing use of inverter-based generation (IBG) in power grids raises concern about system strength. This is partly due to the inherent interactions among multiple IBGs in close proximity to one another. This paper proposes an approach to identifying the existential boundary of interaction in a network using the relative electrical distance (RED) concept. The mathematical formulation of the RED concept to address the interaction problem among the IBGs involved utilising the power system network’s admittance matrix to capture its structural characteristics. An interaction matrix derived from the RED values of all IBG pairs was then developed to identify the interacting IBG groups. The proposed approach was demonstrated using the IEEE 39-bus system and a practical 72-bus Nigerian power grid. Results showed that RED values effectively group interacting IBGs, with values closer to 0 signifying higher interaction levels, values closer to 1 indicating lower interaction, and a value of 1 denoting no interaction. Time-domain simulations confirmed the accuracy of the approach, demonstrating that the effect of control interaction propagates proportionally to neighbouring IBGs based on RED values. However, fault currents can influence the impact of control interactions. This approach, which requires less computational effort, provides a quick identification tool for potential areas of concern based on the degree of interaction, enhancing the reliability of power grids with high IBG penetration.
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    Induction Motor Stator Fault Classification Using PCA-ANFIS Technique
    (ELEKTRIKA-Journal of Electrical Engineering,, 2020) Abdullateef, A. I; Sanusi, M.F; Fagbolagun, O.S
    Induction motors are used commonly for industrial operations due to their ease of operation coupled with ruggedness and reliability. However, they are subjected to stator faults which result in damage and consequently revenue losses. The classification of stator fault in a three-phase induction motor based on Adaptive neuro-fuzzy inference system (ANFIS) in combination with Principal Component Analysis (PCA) is proposed in this study. A burnt motor was redesigned and rewound while data acquisition was developed to acquire the current and vibration data needed for the fault classification. The data feature extraction for the fault classification was carried out by PCA while backpropagation and the least-squares algorithms were used for the training of the data. Three principal components, which severs as input for the ANFIS, were used to represent the entire data. The ANFIS was tested under four different paradigms, while the membership function type and epoch number were changed at each instant. The ANFIS model based on the triangular membership function and 10 epoch number was found appropriate and used, bringing the accuracy of the model to over 99% with the lowest ANFIS training RMSE error of 1.1795e-6. The ANFIS validation results of the fault classification show that the results are accurate, indicating that the PCAANFIS technique is applicable in fault diagnosis and classification of stator faults in induction motors.
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    Intelligent Technique for Electricity Theft Identification Using Autoregressive Model.
    (LAUTECH Journal of Engineering and Technology, Faculty of Engineering Ladoke Akintola University, Ogbomosho. Available online at, 2018) Abdullateef, A. I; Salami, M.J. E; Akorede, M. F
    electricity power providers, however, less attention has been given to identification of the types of electricity theft. Data were acquired from the Consumer Load Prototype developed at two different levels using Sensor- A connected to the Pole Terminal Unit and Sensor-B connected to the Consumer Terminal Unit. The output of the sensors were connected to BNC-2110 device and linked to the PCI 6420E channel, which log the data in the computer for further analysis. LABVIEW (2012) software was programmed to acquires data at a sampling frequency of 500Hz and decimated at 10s interval before logging into the computer hard disk. The feature extraction of the data acquired was achieved using autoregressive technique and model order selectionwas based on minimum description length. The model coefficient AR (20), data acquired and predicted data were used for theft identification. Meter-bypassing theft was identified when the energy consumption from sensor A and sensor B are different, however sensor B reads zero value and there are disparities in the model coefficients. Illegal connection before the meter theft was identified whenever there is difference in energy consumption as evaluated form sensor A and sensor B and there is no zero value recorded from sensor B, while Meter tampering was detected when the energy consumption as evaluated form sensor A and sensor B are different and there are no disparities in the model coefficients.
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    Investigating the impact of solar PV and wind energy systems on the strength of a longitudinal power grid
    (Elsevier, 2024) Sanni, S. O; Abdullateef, A. I; Mohammed, O. O; Aman, M. N; Raji, A. K; Fajuke, I. D
    The integration of inverter-based generation (IBG), such as solar photovoltaic (PV) and wind systems, presents unique challenges for power grid strength, especially in regions with limited conventional generation capacity. This study investigates the impact of such integration on the strength of Nigeria’s power grid, focusing on sparsely connected northern region with abundant renewable resources. Using the network response short-circuit ratio (NRSCR) metric, which accounts for interactions between IBGs, we assess grid strength in three case studies. Simulations were conducted with the Power System Simulation for Engineering (PSS®E) software. Static analysis reveals inherent weakness in the northern region, characterized by low short-circuit capacity. Integrating IBGs at nine strategic locations shows varying responses: strong buses (NRSCR > 10) adapt well, while weak buses (NRSCR < 10), particularly in Kano and Katsina, constrain the operational capacity of the IBGs. Furthermore, close interaction among IBGs at these locations significantly impacts the system’s dynamic response, although planned addition of conventional generators offers marginal improvement. This work highlights the potential of renewable resources to address energy shortages but also underscores the crucial need for detailed design studies at each point of interconnection (PoI), considering the longitudinal grid layout in the northern region and potential limitations imposed by IBG interactions. The findings also offer a structured approach that can be applied to power grids with comparable IBG integration levels, similar to the energy landscape of Nigeria.
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    Modified Narx Network for Low Voltage Consumer Load Prediction
    (Zaria Journal of Electrical Engineering Technology,, 2019) Abdullateef, A. I; Salami, M.J.E; Tijani, I.B; Ajala, M.T
    This paper is a continuation of our previous work on Nonlinear autoregressive with eXogenous input (NARX) for load prediction. Application of NARX network in real-time might be difficult since the tapped delay was selected by trial and error, leading to nine hidden neurons which makes the network complex. This NARX network was modified based on Genetic Algorithm (GA) and Differential Evolution (DE) resulting in a new model coded NARX-DE-GA. GA and DE search for the number of hidden neurons and tapped delay automatically. The NARX-DE-GA was used to predict the consumer load using one month energy data with 8928 data points. The results show that NARX- DE -GA outperformed the NARX network. The training mean square error (MSE) value for NARX- DE -GA is 0.0253 while validation is 0.0612. These values are slightly higher when compared with NARX network in previous study which are 0.0225 and 0.0533 respectively. However, the network structure which is one input and output tapped delay, and one hidden neuron is simple and applicable in real time
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    New consumer load prototype for electricity theft monitoring.
    (Published by Institute of Physics (IOP), 2013) Abdullateef, A. I; Salami, M.J.E; Musse, M.A; Onasanya, M.A; Alebiosu, M.I
    Illegal connection which is direct connection to the distribution feeder and tampering of energy meter has been identified as a major process through which nefarious consumers steal electricity on low voltage distribution system. This has contributed enormously to the revenue losses incurred by the power and energy providers. A Consumer Load Prototype (CLP) is constructed and proposed in this study in order to understand the best possible pattern through which the stealing process is effected in real life power consumption. The construction of consumer load prototype will facilitate real time simulation and data collection for the monitoring and detection of electricity theft on low voltage distribution system. The prototype involves electrical design and construction of consumer loads with application of various standard regulations from Institution of Engineering and Technology (IET), formerly known as Institution of Electrical Engineers (IEE). LABVIEW platform was used for data acquisition and the data shows a good representation of the connected loads. The prototype will assist researchers and power utilities, currently facing challenges in getting real time data for the study and monitoring of electricity theft. The simulation of electricity theft in real time is one of the contributions of this prototype. Similarly, the power and energy community including students will appreciate the practical approach which the prototype provides for real time information rather than software simulation which has hitherto been used in the study of electricity theft.
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    Parametric Model Based Approach for Consumer Load Prediction.
    (2019) Abdullateef, A. I; Akorede, M.F; Abdulkarim, A; Salami, M.J.E
    Various load prediction techniques have been proposed to predict consumer load which represents the activities of the consumer on the distribution network. Usually, these techniques use cumulative energy consumption data of the consumers connected to the power network to predict consumer load. However, these data fail to reveal and monitor the activities of individual consumer represented by the consumer load consumption pattern. A new approach of predicting individual consumer load based on autoregressive moving average model (ARMA) is proposed in this study. Sub- optimal technique of parameter estimation based on Prony method was used to determine the model order of the ARMA models ARMA (10, 8), ARMA (8, 6) and ARMA (6, 4). ARMA (6, 4) was found to be appropriate for consumer load prediction with an average mean square error of 0.00006986 and 0.0000685 for weekday and weekend loads respectively. The energy consumption data acquired from consumer load prototype for one week, with 288 data points per day used in our previous work, was used and 5-minute step ahead load prediction is achieved. Furthermore, a comparison between autoregressive AR (20) and ARMA (6, 4) was carried out and ARMA (6, 4) was found to be appropriate for consumer load prediction. This facilitates the monitoring of individual consumer activities connected on the power network
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    THE IMPLICATIONS OF LOW SHORT-CIRCUIT CAPACITY ON THE STRENGTH OF POWER GRID: A NIGERIAN CASE STUDY
    (Faculty of Engineering and Technology, 2023) Sanni, S. O; Abdullateef, A. I; Mohammed, O. O
    The quest to integrate renewable energy sources (RES) into power grids arises from the need to adopt a sustainable source of energy while achieving energy security. RES such as solar photovoltaic (PV) and wind energy systems are also called inverter-based generators (IBGs) because of their inverter-interfaced connections with the grid. The inverter's quick dynamic response and low short-circuit capacity (SCC) can cause distress to the grid and potentially result in power system stability problems. This paper has assessed the SCC and system strength of the Nigerian grid based on proposed IBG integrations in the northern part of the country. A shortcircuit study was conducted, system strength was evaluated using the Network Response Short Circuit Ratio (NRSCR), and a dynamic voltage stability analysis was performed. These studies revealed that most of the proposed points on the grid have low SCC but suitable for the size of IBG integration; however, some identified weak points may affect the stability of the grid. The results of this investigation provide valuable insights on the impact of IBG integration in renewable energy-rich, SCC-deficient areas of a grid.