E3S Web Conf.
Volume 43, 2018ASTECHNOVA 2017 International Energy Conference
|Number of page(s)||8|
|Published online||29 June 2018|
Implementation of Maximum Power Point Tracking (MPPT) Technique on Solar Tracking System Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)
Department of Engineering Physic, Institut Teknologi Sepuluh Nopember Surabaya, Indonesia
2 Department of Mathematic, Institut Teknologi Sepuluh Nopember Surabaya, Indonesia
* Corresponding author: email@example.com
Characteristic I-V of photovoltaic is depended on solar irradiation and operating temperature. Solar irradiation particularly affects the output current where the increasing solar irradiation will tend to increase the output current. Meanwhile, the operating temperature of photovoltaic module affects the output voltage where increasing temperature will reduce the output voltage. There is a point on the I-V curve where photovoltaic modules produce maximum possible output power that is called Maximum Power Point (MPP). A technique to track MPP on the I-V curve is known as Maximum Power Point Tracking (MPPT). In this study, the MPPT has been successfully designed based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and integrated with solar tracking system to improve the conversion efficiency of photovoltaic modules. The designed ANFIS MPPT system consists of current and voltage sensors, buck-boost converter, and Arduino MEGA 2560 microcontroller as a controller. Varying amounts of lamp with 12V 10W rating arranged in series is used as load. Solar tracking system that is equipped with MPPT ANFIS able to increase the output power of photovoltaic modules by 46.198% relative to the fixed system when 3 lamps is used as load.
© The Authors, published by EDP Sciences 2018
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