Indirect adaptive neurofuzzy Hermite wavelet based control of PV in a grid-connected hybrid power system

Abstract

Owing to the evolution of the smart grid, the emergence of hybrid power systems (HPSs), and the proliferation of plug-in-hybrid electric vehicles, the development of efficient and robust maximum power point tracking (MPPT) algorithms for renewable energy sources due to their inherent intermittent nature has overwhelmed the power industry. In this paper, an incremental conductance (IC) based Hermite wavelet incorporated neurofuzzy indirect adaptive MPPT control paradigm for a photovoltaic (PV) system in a grid-connected HPS is proposed. The performance of the proposed adaptive Hermite wavelet incorporated neurofuzzy MPPT control paradigm is validated through a comprehensive grid-connected HPS test-bed developed in MATLAB$\backslash $Simulink by comparison with an IC based adaptive indirect neurofuzzy Takagi-Sugeno-Kang (TSK) control scheme, IC based adaptive direct neurofuzzy TSK control system, IC based adaptive proportional-integral-derivative (adapPID) control scheme, and IC algorithm for PV systems.