@inproceedings{166f8b07bfa441d7be220cc4af660edf,
title = "A neural network based maximum power point tracker with KY converter for photovoltaic system on a moving vehicle",
abstract = "The application of photovoltaic system on the ship may reduce the operational cost and pollution caused by fossil fuel. In order to optimize the efficiency of the PV system, an appropriate maximum power point tracking (MPPT) must be implemented on the system. The MPPT must have fast response to overcome the rapid changes of solar irradiance due to ship movement or natural occurrence. In this paper, a combination of artificial neural network based MPPT and KY converter is proposed. The proposed method has been validated with a computer based simulation. The results show that the proposed method can optimize the PV system performance with a fast response to the change of sun irradiance.",
keywords = "KY converter, artificial neural network (ANN), maximum power point tracking (MPPT), photovoltaic (PV)",
author = "Adi Kurniawan and Eko Haryanto and Masroeri, {A. A.}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation, ICAMIMIA 2015 ; Conference date: 15-10-2015 Through 16-10-2015",
year = "2016",
month = jul,
day = "8",
doi = "10.1109/ICAMIMIA.2015.7508014",
language = "English",
series = "ICAMIMIA 2015 - International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation, Proceeding - In conjunction with Industrial Mechatronics and Automation Exhibition, IMAE",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "117--120",
booktitle = "ICAMIMIA 2015 - International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation, Proceeding - In conjunction with Industrial Mechatronics and Automation Exhibition, IMAE",
address = "United States",
}
