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All Journal International Journal of Electrical and Computer Engineering Mestro
Rosyadi, Marwan
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Control energy management system for photovoltaic with bidirectional converter using deep neural network Widjonarko, Widjonarko; Utomo, Wahyu Mulyo; Omar, Saodah; Baskara, Fatah Ridha; Rosyadi, Marwan
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.pp1437-1447

Abstract

Rapid population growth propels technological advancement, heightening electricity demand. Obsolete fossil fuel-based power facilities necessitate alternative energy sources. Photovoltaic (PV) energy relies on weather conditions, posing challenges for constant energy consumption. This hybrid energy source system (HESS) prototype employs extreme learning machine (ELM) power management to oversee PV, fossil fuel, and battery sources. ELM optimally selects power sources, adapting to varying conditions. A bidirectional converter (BDC) efficiently manages battery charging, discharging, and secondary power distribution. HESS ensures continuous load supply and swift response for system reliability. The optimal HESS design incorporates a single renewable source (PV), conventional energy (PNL and genset), and energy storage (battery). Supported by a BDC with over 80% efficiency in buck and boost modes, it stabilizes voltage and supplies power through flawless ELM-free logic verification. Google Colab online testing and hardware implementation with Arduino demonstrate ELM's reliability, maintaining a direct current (DC) 24 V interface voltage and ensuring its applicability for optimal HESS.
Modification of Dynamic Voltage Restorer for Improved Power Quality in Industrial Electrical Networks Rosyadi, Marwan; Siswanto, Agus
Mestro: Jurnal Teknik Mesin dan Elektro Vol 7 No 1 (2025): Edisi Juni
Publisher : Fakultas Teknik Universitas 17 Agustus 1945 Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47685/mestro.v7i1.683

Abstract

Reliable power quality is a crucial factor in maintaining continuity and operational efficiency in industrial power grids. Power quality disturbances such as voltage sags, voltage swells, and interruptions can cause equipment damage, decreased productivity, and increased operational costs. Dynamic Voltage Restorers (DVRs) have proven effective in addressing voltage disturbances, but conventional devices have limitations, specifically, they are only able to compensate for voltage drops up to approximately 30% of the nominal value and cannot address interruption disturbances. This study proposes a modified DVR with the addition of an energy storage system and an adaptive control algorithm to expand the voltage compensation capabilities, including under extreme voltage sags and interruption disturbances. Simulation results show that the modified DVR is able to maintain load voltages close to the nominal value under various disturbance scenarios, thereby significantly improving the power quality and reliability of the industrial power grid.
Co-Authors Agus Siswanto Baskara, Fatah Ridha Omar, Saodah Wahyu Mulyo Utomo Widjonarko .