Fast Convergence MPPT for PV Systems Using a Dynamic HHO-P&O Hybrid Controller

Abstract

The energy sector’s fastest-growing trend at the moment is renewable energy. Solar photovoltaic (PV) technology is the most prominent of the renewable energy sources. Because the nonlinearity in the I-V curves of the solar PV system (SPVS) is an inescapable difficulty, it is challenging to estimate the ideal maximum power point (MPP) in a changing climate. The use of MPP tracking (MPPT) controllers increases the output power of the SPVS. In these circumstances, conventional MPPT algorithms are frequently unable to monitor the global maximum power point because they become trapped on local peaks. The Herd Horse Optimizer - Perturbation and Observation (HHOP&O) algorithm, an enhanced hybrid optimization technique that incorporates the abilities of the Herd Horse Optimizer (HHO) and the Perturbation and Observation (P&O) algorithms, has been suggested in this research in order to track the global peak. The suggested method monitors the global MPP in all shading scenarios, improves monitoring speed and efficiency, and produces fewer oscillations. Under Partial Shading Condition (PSC), the suggested system has been modelled and analysed using the MATLAB/Simulink software. Comparing the results from the proposed method to those from HHO-based MPPT, the oscillations in the output power, duty cycle, current and voltage of the PV system are the least.