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All Journal JURNAL NASIONAL TEKNIK ELEKTRO

Francis Boafo Effah

Kwame Nkrumah University of Science and Technology

Author-ID : 3119987

Electrical & Electronics Engineering

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Utilizing Unified Power Flow Controller for Voltage Stability Improvement of the Electric Power Transmission System of Ghana William Duodu Asihene; Francis Boafo Effah; Erwin Normanyo
JURNAL NASIONAL TEKNIK ELEKTRO Vol 9, No 1: March 2020
Publisher : Jurusan Teknik Elektro Universitas Andalas

Interconnecting power transmission systems provide reliability of electric power supply. The security of the system is however questioned when a disturbance in any part of the interconnected system causes instability in the entire network. Unified Power Flow Controller (UPFC), which is a member of the flexible alternating current transmission system (FACTS) family, has the capability of controlling active and reactive power flow in a transmission line thereby improving the voltage stability of the system especially at the 500 kV configuration level. The performance of a 161-kV UPFC modelled in SimPowerSystems is tested on Ghana’s power transmission network. The optimal placement of the UPFC is done using fast voltage stability index (FVSI) and maximum loadability assessment (MLA). The results show that the device improved the connecting bus voltage from 0.88 p.u. to 0.98 p.u. Active power loss in the network was also reduced from 13.40 MW to 10.39 MW when the UPFC was in circuit.Keywords: Ghana, Stability, Transmission system and Unified Power Flow Controller (UPFC)

Harmonics of CF and LED lamps - Maximum Penetration Perspective on Power Quality in Distribution Systems Francis Boafo Effah; Philip Gasu; Philip Okyere; Amevi Acakpovi
JURNAL NASIONAL TEKNIK ELEKTRO Vol 9, No 3: November 2020
Publisher : Jurusan Teknik Elektro Universitas Andalas

Global energy saving efforts have led to replacement of incandescent lamps with energy-efficient ones like light-emitting diode (LED) and compact fluorescent lamps (CFLs). These lamps, being non-linear loads, have the potential of injecting harmonics into distribution networks. In this paper, harmonics injection of common CFL and LED lamps at a facility point of common coupling is investigated. To gain insight into large scale penetration effects on power quality, field measurement results of popular lamps used in Ghana were replicated in MATLAB/Simulink through simulation. The field results showed that LED lamps exhibit more harmonics compared to CFL lamps. Maximum possible loading on a 100-kVA, 11kV/0.433kV distribution transformer was found to be 24.02% for CFL, 27.14% for LED, and 40.91% for a mixture of the two lamps, respectively, in order not to violate IEEE 519-2014 standard. The influence of other common loads such as ceiling fans on the lamps’ harmonics were assessed in the field measurement. The use of ceiling fans with the lamps in the facility reduced the harmonics and improved the power factor of the facility. Since the lamps exist in residential and commercial facilities with other loads, more penetration of energy-saving lamps in the distribution system will have little influence on power quality.Keywords: Compact fluorescent lamps, light emitting diodes, maximum power loading, total harmonic distortion, point of common coupling

ANN-Based Electricity Theft Classification Technique for Limited Data Distribution Systems Monister Yaw Kwarteng; Francis Boafo Effah; Daniel Kwegyir; Emmanuel Asuming Frimpong
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 1: March 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n1.1072.2023

Electricity theft has been a challenge for distribution systems over the years. Theft presents a massive cost to the system operators and other issues such as transformer overloading, line loading, etc. It has become crucial for measures to be implemented to combat illegal electricity consumption. This work sought to develop an artificial neural network-based electricity theft classifier for distribution systems with limited data, i.e., systems that can only provide consumption data alone and no auxiliary data. First, a novel data pre-processing method was proposed for the systems with consumption data only. Again, synthetic minority oversampling is employed to deal with the unbalance problem in the theft detection dataset. Afterwards, an artificial neural network (ANN)-based classifier was proposed to classify customers as normal or fraudulent. The proposed method was tested on actual electricity theft data from the Electricity Company of Ghana (ECG) and its performance compared to random forest (RF) and logistic regression (LR) classifiers. The proposed ANN-based classifier performed exceptionally by producing the best results over RF and LR regarding precision, recall, F1-score, and accuracy of 99.49%, 100%, 99.75%, and 99.74%, respectively.

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

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.

Hyperbolic Tangent - Based Adaptive Inertia Weight Particle Swarm Optimization Yaw Opoku Mensah Sekyere; Francis Boafo Effah; Philip Yaw Okyere
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.v12n2.1095.2023

This paper presents a study on using adaptive inertia weight (AIW) in particle swarm optimization (PSO) for solving optimization problems. An AIW function based on the hyperbolic tangent function was proposed, with the function parameters adaptively tuned based on the particle best and global best values. The performance of the proposed AIW-PSO was compared with standard PSO and other PSO variations using seven benchmark functions. The results showed that the proposed AIW-PSO outperformed the other variations in terms of minimum cost and mean cost while reducing the standard deviation of cost. The performance of the different PSO variations was also analysed by plotting the best cost against iteration, with the proposed AIW-PSO showing a faster convergence rate. Overall, the study demonstrates the effectiveness of using an adaptive inertia weight function in PSO for optimizing problems.

Co-Authors Amevi Acakpovi Daniel Kwegyir Daniel Opoku Emmanuel Asuming Frimpong Erwin Normanyo Monister Yaw Kwarteng Peter Asigri Philip Gasu Philip Okyere Philip Yaw Okyere William Duodu Asihene Yaw Opoku Mensah Sekyere

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