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A Novel Hybridization of Deep Neural Network and ANFIS Based Solar Maximum Power Point Tracking
Abstract
Solar systems, which are part of large distribution systems for power generation, have non-linear volt-ampere characteristics. These characteristics depend on the temperature of the solar cell and the amount of insolation on the panel. The maximum power point method (MPPT) is widely known and many algorithmic solutions have been proposed. Maximum Power Point Tracking (MPPT) is a desirable element in photovoltaic (PV) systems and is used to increase the extractable power of photovoltaic systems. There are many existing methods such as perturbation and observation (P&O), hill climbing (HC) and incremental conduction (IC) for MPPT in solar systems. However, recent integration of deep learning (DL) based methods has made MPPT more efficient and effective to achieve maximum power transfer to the load. In this context, two MPPT methods are implemented: Deep Neural Network (DNN) and Adaptive Neural Fuzzy Interface System (ANFIS). Both MPPT methods have been studied in terms of efficiency and developed in the MATLAB/Simulink environment. The advantage of this method is that it is inexpensive to implement because no additional sensors are required to measure temperature and insolation.
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