Article Per Year (5 Year)
p-Index From 2021 - 2026
1.628
P-Index
This Author published in this journals
All Journal INTECOMS: Journal of Information Technology and Computer Science Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Instal : Jurnal Komputer Jurnal Penelitian Inovatif Fidelity : Jurnal Teknik Elektro Journal of Artificial Intelligence and Digital Business Journal of Electrical Engineering Research
Wibowo, Pristisal
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Public Health Social Sciences

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
Analysis of Power Quality in Technology Based Power Systems Tharo, Zuraidah; Wibowo, Pristisal; Syahputra, Mhd Rizki
Journal of Electrical Engineering Research Vol. 2 No. 1 (2026): Januari 2026
Publisher : CV. Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/joeer.v2i1.23

Abstract

The rapid advancement of power system technologies has significantly transformed the way electrical power quality (PQ) is monitored, managed, and improved. The increasing penetration of non-linear loads, automation systems, and renewable energy sources has intensified power quality challenges, including harmonic distortion, voltage sags, voltage fluctuations, and transient disturbances. This study aims to analyze the impact of modern technological approaches on power quality enhancement in contemporary power systems. A descriptive-analytical research methodology was employed through a systematic review of recent scientific literature, international standards, and technical reports related to power quality management. The collected data were analyzed using qualitative content analysis and comparative evaluation to assess the effectiveness of automation, machine learning, Internet of Things (IoT)-based monitoring systems, and power electronic mitigation devices. The results indicate that integrated technological solutions significantly improve power quality performance compared to conventional approaches. Machine learning techniques, particularly Artificial Neural Networks, demonstrate high accuracy in disturbance classification and adaptive control, while IoT-based systems enable real-time monitoring and rapid response to power quality deviations. Furthermore, power electronic devices such as Active Power Filters, Static Var Compensators, and Dynamic Voltage Restorers effectively mitigate harmonics and stabilize voltage. However, challenges related to implementation cost, system complexity, cybersecurity, and regulatory inconsistency remain. The study concludes that an integrated, intelligent, and data-driven framework is essential for sustainable power quality management in modern power systems.
Co-Authors Ahmad Dani Barus, Michael J.T. Brid, Dwiki Darma Nuriono FAJRI, SATRIA Gulo, Nico Ewaldo Fan Hamdani Hamdani Hutapea, Anggi Putri Debora Nugraha, Satria Panjaitan, Roger Efendi Parlin Siagian Pratama, Muhammad Rizky Pratama, Renaldi Budi Sianipar, Tigor Pangihutan Siti Anisah Syahputra, Mhd Rizki Tarigani, Adisastra Pengalaman Zuraidah Tharo