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Mas Sulung Wisnu Jati
Politeknik Elektronika Negeri Surabaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Maximum Power Point Tracking Menggunakan Algoritma Artificial Neural Network Berbasis Arus Hubung Singkat Panel Surya Muhammad Nizar Habibi; Mas Sulung Wisnu Jati; Novie Ayub Windarko; Anang Tjahjono
Jurnal Rekayasa Elektrika Vol 16, No 2 (2020)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1434.609 KB) | DOI: 10.17529/jre.v16i2.14860

Abstract

The conversion of solar energy into electrical can be utilized by using the solar panel, but the energy conversion ratio is still low. Maximum Power Point Tracking (MPPT) is a method used to increase energy production in the process of converting electrical to the solar panel. Artificial Neural Network (ANN) is one of the soft-computing methods that can be applied as MPPT with the advantage of having a learning process, very stable, fast, doesn’t require complicated mathematical modeling, and has good performance. ANN is proposed with input from the short circuit current of the solar panel and is used as a reference for the ANN to reach the maximum power. The process of detecting a short circuit current is indicated by a momentary decrease of the power by the solar panel. The results show the proposed algorithm can reach the maximum power operating point of the solar panel despite the change of radiation. When at maximum power operating point, ANN can hold the value, so the resulting value doesn’t change and doesn’t generate ripple. At radiation of 1000 W/m2 and using 100 WP, ANN can produce a maximum power of 99.97 Watts with a time of 0.063 seconds. 
Maximum Power Point Tracking Menggunakan Algoritma Artificial Neural Network Berbasis Arus Hubung Singkat Panel Surya Muhammad Nizar Habibi; Mas Sulung Wisnu Jati; Novie Ayub Windarko; Anang Tjahjono
Jurnal Rekayasa Elektrika Vol 16, No 2 (2020)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v16i2.14860

Abstract

The conversion of solar energy into electrical can be utilized by using the solar panel, but the energy conversion ratio is still low. Maximum Power Point Tracking (MPPT) is a method used to increase energy production in the process of converting electrical to the solar panel. Artificial Neural Network (ANN) is one of the soft-computing methods that can be applied as MPPT with the advantage of having a learning process, very stable, fast, doesn’t require complicated mathematical modeling, and has good performance. ANN is proposed with input from the short circuit current of the solar panel and is used as a reference for the ANN to reach the maximum power. The process of detecting a short circuit current is indicated by a momentary decrease of the power by the solar panel. The results show the proposed algorithm can reach the maximum power operating point of the solar panel despite the change of radiation. When at maximum power operating point, ANN can hold the value, so the resulting value doesn’t change and doesn’t generate ripple. At radiation of 1000 W/m2 and using 100 WP, ANN can produce a maximum power of 99.97 Watts with a time of 0.063 seconds. 
Co-Authors Anang Tjahjono, Anang Habibi, Muhammad Nizar Novie Ayub Windarko