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All Journal Mechatronics, Electrical Power, and Vehicular Technology JURNAL NASIONAL TEKNIK ELEKTRO Journal of Information Technology and Computer Engineering Andalas Journal of Electrical and Electronic Engineering Technology
Daniel Opoku
Kwame Nkrumah University of Science and Technology
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering

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An alternative design and implementation of a solid state on-load tap changer Benjamin Kommey; Elvis Tamakloe; Gideon Adom-Bamfi; Daniel Opoku
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 12, No 2 (2021)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2021.v12.104-109

Abstract

Power quality and reliability are of great importance in the modern world, whether it be the power generated by the power utilities or the power consumed by the customer respectively. They need these supplies to be at its optimum value so that the cost is effective, and the safety of devices assured otherwise problems such as overvoltage, under-voltage, and voltage sags caused by disturbances in the power supply could be disastrous. On-load tap changers (OLTC) have therefore been used since the inception of electrical engineering. The main function of the OLTC is to change the turns of the transformer winding so that the voltage variations are limited without interrupting the secondary current.The major idea is that the electronic switches and other smart systems provide more controllability during the tap changing process, unlike mechanical switches.This paper presents an alternative design and implementation of a low-cost solid-state OLTC and employs a control strategy that is microcontroller-based, ensuring the desired flexibility and controllability required in programming the control algorithms.It eliminates the limitations of both mechanical and hybrid OLTCs (arcing, slow response time, losses) and is user-friendly (provides an effective communication medium). Voltage regulation is achieved by varying the turns of the transformer winding whiles it is energized, supplying load current and with the tap selection carried out on the primary side. Therefore, this approach provides a less expensive system but ensures the efficiency and reliability of voltage regulation.
Ultrasonic Sensor-Based Automated Water Dam Shutter Benjamin Kommey; Seth Djanie Kotey; Daniel Opoku
JITCE (Journal of Information Technology and Computer Engineering) Vol 4 No 01 (2020): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (493.054 KB) | DOI: 10.25077/jitce.4.01.1-4.2020

Abstract

Monitoring the level of water in dams is necessary to ensure optimal operation and safety. Water level monitoring is normally done manually by a full-time operator. This results mostly in a waste of water due to the inability of the operator to accurately determine the quantity of water to release from the dam gate. The aim of this paper is to present the design of a system to automatically open and close dam gates based on the level of water in the dam. The system is based on a low-cost microcontroller and an ultrasonic sensor to read water level in the dam. SMS messages are sent to nearby residents to warn them of the opening of the dam gate. An alarm is also sounds before the eventual opening of the dam gate.
Short-Term EV Charging Demand Forecast with Feedforward Artificial Neural Network Francis Boafo Effah; Daniel Kwegyir; Daniel Opoku; Peter Asigri; Emmanuel Asuming Frimpong
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 2: July 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n2.1094.2023

Abstract

The global increase in greenhouse gas emissions from automobiles has brought about the manufacture and usage of large quantities of electric vehicles (EVs). However, to ensure proper integration of EVs into the grid, there is a need to forecast the charging demand of EVs accurately. This paper presents a short-term electric vehicle charging demand forecast using a feedforward artificial neural network optimized with a modified local leader phase spider monkey optimization (MLLP-SMO) algorithm, a proposed variant of spider monkey optimization. A proportionate fitness selection is employed to improve the update process of the local leader phase of the spider monkey optimization. The proposed algorithm trains a feedforward neural network to forecast electric vehicle charging demand. The effectiveness of the proposed forecasting model was tested and validated with electric vehicle public charging data from the United Kingdom Power Networks Low Carbon London Project. The model's performance was compared to a feedforward neural network trained with particle swarm optimization, genetic algorithm, classical spider monkey optimization, and two conventional forecasting models, multi-linear regression and Monte Carlo simulation. The performance of the proposed forecasting model was assessed using the mean absolute percentage error of forecast and forecasting accuracy. The model produced a forecast accuracy and mean absolute percentage error of 99.88% and 3.384%, respectively. The results show that MLLP-SMO as a trainer predicted better than the other forecasting models and met industry standard forecast accuracy.
Remaining Useful Lifetime Prediction of Distribution Transformer Using Dynamic Multi-Scale Attention-based CNN-LSTM Elvis Tamakloe; Benjamin Kommey; Jerry John Kponyo; Daniel Opoku; Francis Boafo Effah
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 6 No. 1 (2026): May 2026
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v6i1.210

Abstract

Oil-immersed transformers are critical assets in the energy industry linking most power utilities to end-users. Their failure results in prolong outages, leading to huge revenue loss incurred during downtimes and replacement cost. In extreme cases, transformers in an unhealthy state poses a significant threat to the safety of grid operators. Interestingly, traditional reactive and preventive methods have been inefficient in determining when legitimate maintenance actions are due, often leading to either early over-maintenance of healthy transformers or late under-maintenance of serviceable and unhealthy transformers. Predictive maintenance based on determining the remaining useful lifetime (RUL) acts as an actionable step that resolves these challenges by delivering exactly the most appropriate time to undertake maintenance whiles ensuring optimal utilization of resources which saves maintenance cost, reduces downtimes and ensures operator safety and grid reliability. This work proposed an advanced Dynamic Multi-Scale Attention (DMSA) model and leverages on multi-modal data fusion from electrical, mechanical, thermal, and environmental sources to provide an improved data-driven solution for accurate prediction of the RUL of distribution transformers. This technique addressed the drawbacks of employing single modality approaches in capturing complex operational interactions. In this work, dynamic scaling model is incorporated to adaptively adjust the attention weights based on the importance of the input features. For short term predictions, the proposed model experimentally achieved an enhanced performance of 0.2300 mean absolute error and 0.9872 coefficient of determination value. Additionally, the DMSA CNN-LSTM model demonstrated accurate prediction, evidenced by a concordance correlation coefficient value of 0.9936. These statistical gains were achieved in a computational time of 587.3387s, demonstrating superior scalability in the event of real time deployment. Furthermore, the long-term prediction was performed using Prophet to fit the data which predicted a RUL of 25 years at 95% confidence interval which corresponded with the reference standard in IEEE STD C57.91.
Co-Authors Benjamin Kommey Benjamin Kommey Daniel Kwegyir Elvis Tamakloe Emmanuel Asuming Frimpong Francis Boafo Effah Gideon Adom-Bamfi Jerry John Kponyo Peter Asigri Seth Djanie Kotey