Control energy management system for photovoltaic with bidirectional converter using deep neural network

Widjonarko, Widjonarko and Wahyu Mulyo Utomo, Wahyu Mulyo Utomo and Omar, Saodah and Fatah Ridha Baskara, Fatah Ridha Baskara and Rosyadi, Marwan (2024) Control energy management system for photovoltaic with bidirectional converter using deep neural network. International Journal of Electrical and Computer Engineering (IJECE), 14 (2). pp. 1437-1447. ISSN 2088-8708

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Official URL: https://doi.org/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.

Item Type:	Article
Uncontrolled Keywords:	Energy storage Extreme learning machine Hybrid energy source system Power management system Renewable energy
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Electrical and Electronic Engineering > FKEE
Depositing User:	Mr. Mohamad Zulkhibri Rahmad
Date Deposited:	04 Jun 2024 03:05
Last Modified:	04 Jun 2024 03:05
URI:	http://eprints.uthm.edu.my/id/eprint/11086

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