Browsing by Author "Akorede, M. F"

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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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    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
    Various studies have investigated electricity theft, an illegally act, perpetrated to the detriment of the 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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    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.