Optimal Power Extraction from Solar PV Panels using Fuzzy Logic and Artificial Neural Network based Maximum Power Point Tracker

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Abstract

Solar energy is among most impactful type of renewable energy because of the reliable amount of sunlight the Earth receives. By installing the solar modules one can easily reduce the dependency on electrical grid and utilize the clean energy locally. One of the main disadvantages is that the solar irradiance varies even at the same place throughout the day. Due to the variation in solar irradiance photovoltaic energy generation is not constant throughout the entire day. As the operating point changes due to change in irradiation it becomes necessary to identify instant by instant that specific operating point of I-V characteristics of solar module where the maximum amount of power is getting generated. The conventional techniques to identify this maximum power point MPP are popular, simple and easy to implement, but they can only track single MPP under the uniform solar irradiance. Conventional techniques require long time and also has low tracking accuracy. Soft computing techniques are a little complex in comparison to conventional techniques but all of them are flexible as well as reliable. Soft computing techniques provide optimal solution with high efficiency. The paper design and compare Fuzzy Logic and Artificial Neural Network based MPPT for PV applications.

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Keywords

Solar photovoltaics MPPT: Maximum power point tracker Conventional Soft computing Fuzzy Logic