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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) RESISTOR (Elektronika Kendali Telekomunikasi Tenaga Listrik Komputer) TEKNOLOGIA IJITEE (International Journal of Information Technology and Electrical Engineering) Indonesian Journal of Electrical Engineering and Computer Science Indonesian Community Journal
Muhammad Abdillah
Universitas Pertamina
Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Public Health Social Sciences Other

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Journal : International Journal of Electrical and Computer Engineering

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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 Afif, Yusrizal Awan Uji Krismanto Danang Sulistyo Fakhruddin Arrazi Fathan Mujahid Satria Favian Dewanta Firdaus Bima Firmansyah Galih Bangga Herlambang Setiadi Herlambang Setiadi Herlambang Setiadi Herlambang Setiadi Herminarto Nugroho Herri Trilaksana Marza Ikhsan Marzuki Muhammad Nita Indriani Pertiwi Prayogi, Soni Prisma Megantoro Rezi Delfianti Rozan Haqi Pratama Salma Salma Syailendra Andi Tahta Amrillah Teguh Aryo Nugroho Teuku Muhammad Roffi Wahyu Agung Pramudito Wahyu Kunto Wibowo