Computation of an Effective Hybrid DFA-SVM Approach Aimed at Adaptive PV Power Management

Abstract

Predominantly focussed in environmental conditions that are dynamic in nature the energy harnessed from the photovoltaic systems has to be maintained at high efficiency for which maximum power has to be extracted so a novel hybrid DFA-SVM control has been implemented using SEPIC converter. There are many algorithms to perform this function mentioned but in order to track the power at a faster rate and to avoid oscillations at the settling peak point this new methodology has been implemented. In this paper the novel algorithm used to track the peak power is Dragon Fly Algorithm-Support Vector Machines (SVMs). The algorithm is a combination of optimization and machine learning technique, so that this new methodology can incorporate both instantaneous and steady state features. The benefits of both the optimization and supervised learning technique are used to track most efficiently the maximum power with less oscillations. The DFA-SVM technique is implemented in the controller of the DC-DC converter used to regulate the supply voltage generated by the PV. The suggested MPPT’s performance is demonstrated under demanding experimental conditions including temperature and solar irradiation fluctuations across the panel. To further illustrate the superiority of the suggested approach, its performance is contrasted with that of the P&O method, which is commonly employed in MPPT during difficult exams.