This present paper emphasis the improvement of resonant converter (DC –DC converter) for solar energy conversion system (SECS). It accentuations on plan and exhibiting periods of the distinctive constituents of the SECS like the rudimentary model of zero voltage switching (ZVS) converter, inverter, fuzzy logic controller, best maximum power point tracking (MPPT) system utilizing MATLAB/ Simulink. Here, a resonant converter represents the static and dynamic characteristics of an actual solar irradiance have been developed for simulation tests. The irradiance and temperature changes circumstances are performed using the resonant converter, which consist of PV system, whose control is implemented using Fuzzy logic control unit and MATLAB/ Simulink. This PV module can achieve maximum power using Perturb & Obserb algorithm. Under various abnormal conditions like change in irradiance and temperature, step change in irradiance etc. The control system is executed on a fuzzy logic control unit. Speculative outcomes are exhibited utilizing a 200W model of SECS to demonstrate the better aftereffects of the propelled framework and their control under steady- state and dynamic conditions. 

This paper presents the development and performance analysis of Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller for a DC to DC converter. The proposed system consists of 2.0 kW PV array, DC to DC boost converter and load. In this research work presents the development and performance analysis of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) based Levenberg-Marquardt (LM), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms are deployed in maximum power point tracking (MPPT) energy harvesting in solar photovoltaic (PV) system to forge a comparative performance analysis of the four different algorithms. A comparative analysis among the algorithms in terms of the performance of handling the trained dataset is presented. The MATLAB/Simulink environment is used to design the maximum power point tracking energy harvesting system and the artificial neural network toolbox is utilized to analyze the developed model. However, considering the dataset training, the correlation between input-output and error, the Levenberg-Marquardt ANFIS algorithm performs better.