Article Per Year (5 Year)
p-Index From 2020 - 2025
0.983
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering JOIV : International Journal on Informatics Visualization Indonesian Journal of Electrical Engineering and Computer Science
Naemah Mubarakah
North Sumatra University
Computer Science & IT Electrical & Electronics Engineering

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : International Journal of Electrical and Computer Engineering

 1 
Dynamic voltage restorer quality improvement analysis using particle swarm optimization and artificial neural networks for voltage sag mitigation Siregar, Yulianta; Muhammad, Maulaya; Arief, Yanuar Zulardiansyah; Mubarakah, Naemah; Soeharwinto, Soeharwinto; Dinzi, Riswan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6079-6091

Abstract

Power quality is one of the problems in power systems, caused by increased nonlinear loads and short circuit faults. Short circuits often occur in power systems and generally cause voltage sags that can damage sensitive loads. Dynamic voltage restorer (DVR) is an efficient and flexible solution for overcoming voltage sag problems. The control system on the DVR plays an important role in improving the quality of voltage injection applied to the network. DVR control systems based on particle swarm optimization (PSO) and artificial neural networks (ANN) were proposed in this study to assess better controllers applied to DVRs. In this study, a simulation of voltage sag due to a 3-phase short-circuit fault was carried out based on a load of 70% of the total load and a fault location point of 75% of the feeder’s length. The simulation was carried out on the SB 02 Sibolga feeder. Modeling and simulation results are carried out with MATLAB-Simulink. The simulation results show that DVR-PSO and DVR-ANN successfully recover voltage sag by supplying voltage at each phase. Based on the results of the analysis shows that DVR-ANN outperforms DVR-PSO in quality and voltage injection into the network.
Dynamic voltage restorer performance analysis using fuzzy logic controller and battery energy storage system for voltage sagging Siregar, Yulianta; Azhari Nasution, Azrial Aziz; Suan Tial, Mai Kai; Mubarakah, Naemah; Soeharwinto, Soeharwinto
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1215-1227

Abstract

Power quality is a major issue in the power transfer process. This is caused by disturbances such as voltage sags, voltage spikes, and harmonics. Voltage sag is the most common disturbance in the electric power system. However, the dynamic voltage restorer (DVR) is the most effective device for voltage sags. This research uses the DVR to overcome voltage sags using fuzzy logic controller (FLC) and battery energy storage system (BESS) to improve the performance of the DVR. The results showed that DVR using FLC improved the quality of voltage recovery compared to BESS because FLC injected a greater voltage of 0.0991 pu than BESS.
Target image validation modeling using deep neural network algorithm Mubarakah, Naemah; Sihombing, Poltak; Efendi, Syahril; Fahmi, Fahmi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2042-2054

Abstract

Research on image validation models is an interesting topic. The application of deep learning (DL) for object detection has been demonstrated to effectively and efficiently address the challenges in this field. Deep neural networks (DNN) are deep learning algorithms capable of handling large datasets and effectively solving complex problems due to their robust learning capacity. Despite their ability to address complex problems, DNN encounter challenges related to the necessity for intricate architectures and a large number of hidden layers. The objective of this research is to identify the most effective model for achieving optimal performance in image validation. This study investigates target image validation using DNN algorithms, examining architectures with 3, 4, 5, and 6 hidden layers. This study also evaluates the performance of image validation across various activation functions, batch sizes, and numbers of neurons. The results of the study show that the best performance for image validation is achieved using the Leaky-ReLU and Sigmoid activation functions, with a batch size of 64, and an architecture consisting of 3 hidden layers with neuron sizes of 256, 128, and 64. This model is capable of providing real-time target image validation with an accuracy of up to 94.31%.
Co-Authors Azhari Nasution, Azrial Aziz Debataraja, Bio Efendi, Syahril Fahmi Fahmi Florence, Grace Muhammad, Maulaya Poltak Sihombing Riswan Dinzi Seniman, Seniman Siregar, Baihaqi Soeharwinto Soeharwinto, Soeharwinto Suan Tial, Mai Kai Yanuar Zulardiansyah Arief Yulianta Siregar