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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics JURNAL NASIONAL TEKNIK ELEKTRO Indonesian Journal of Electrical Engineering and Computer Science JAREE (Journal on Advanced Research in Electrical Engineering)
Afif, Yusrizal
Universitas Airlangga
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

 1 
Comparative analysis of evolutionary-based maximum power point tracking for partial shaded photovoltaic Prisma Megantoro; Hafidz Faqih Aldi Kusuma; Lilik Jamilatul Awalin; Yusrizal Afif; Dimas Febriyan Priambodo; Pandi Vigneshwaran
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5717-5729

Abstract

The characteristics of the photovoltaic module are affected by the level of solar irradiation and the ambient temperature. These characteristics are depicted in a V-P curve. In the V-P curve, a line is drawn that shows the response of changes in output power to the level of solar irradiation and the response to changes in voltage to ambient temperature. Under partial shading conditions, photovoltaic (PV) modules experience non-uniform irradiation. This causes the V-P curve to have more than one maximum power point (MPP). The MPP with the highest value is called the global MPP, while the other MPP is the local MPP. The conventional MPP tracking technique cannot overcome this partial shading condition because it will be trapped in the local MPP. This article discusses the MPP tracking technique using an evolutionary algorithm (EA). The EAs analyzed in this article are genetic algorithm (GA), firefly algorithm (FA), and fruit fly optimization (FFO). The performance of MPP tracking is shown by comparing the value of the output power, accuracy, time, and tracking effectiveness. The performance analysis for the partial shading case was carried out on various populations and generations.
Adaptive virtual inertia controller based on machine learning for superconducting magnetic energy storage for dynamic response enhanced Herlambang Setiadi; Muhammad Abdillah; Yusrizal Afif; Rezi Delfianti
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp3651-3659

Abstract

The goal of this paper was to create an adaptive virtual inertia controller (VIC) for superconducting magnetic energy storage (SMES). An adaptive virtual inertia controller is designed using an extreme learning machine (ELM). The test system is a 25-bus interconnected Java Indonesian power grid. Time domain simulation is used to evaluate the effectiveness of the proposed controller method. To simulate the case study, the MATLAB/Simulink environment is used. According to the simulation results, an extreme learning machine can be used to make the virtual inertia controller adaptable to system variation. It has also been discovered that designing virtual inertia based on an extreme learning machine not only makes the VIC adaptive to any change in the system but also provides better dynamics performance when compared to other scenarios (the overshoot value of adaptive VIC is less than -5×10-5).
Co-Authors Abdul Abror Agus Mukhlisin Ahmad Rahmad Muzadi Awalin, Lilik Jamilatul Daniar Fahmi, Daniar Dimas Anton Asfani Dimas Anton Asfani Dimas Febriyan Priambodo Hafidz Faqih Aldi Kusuma Herlambang Setiadi I Made Yulistya I Made Yulistya Negara Muhammad Abdillah Muhammad Akbar Syahbani P. Vigneshwaran Pandi Vigneshwaran Pandi Vigneshwaran Pinto Anugrah Prisma Megantoro Rezi Delfianti Sigit Dani Perkasa Sinta Adisti Reina Yulistya, I Made