In this paper, A Cascaded Multi level Inverter (CMLI) interconnected with the 10 KW PV System, Boost Converter along with Cascaded Feed Forwarded Neural Network (CFFNN) MPPT Controller is proposed to improve the Power Quality (PQ) for Linear, Non-linear and unbalanced loading conditions and minimize the total Harmonic Distortion (THD). The CMLI Consists of Novel type 9-Level Inverter with Reduced number of switches, and is connected to Bridged type inverter as cascaded, to get the required amount of Output voltage which can be used for grid integration. For controlling the inverter the Current controller is much required to control the current and to synchronize the Phase lock loop (PLL) is important. Here a new Adoptive Neuro Fuzzy Interface System (ANFIS) Control tuned with PI Controller is used to advance the performance of the power quality of the system under various loading conditions and undesired oscillations and THD can be improved compared with Conventional PI Controller and Fuzzy-PI Controller, Load voltage and current waveforms are analyzed under IEEE 519. The system is developed in the MATLAB environment to check the dynamic PV performance with MPPT controller and the results are found satisfactory.

This paper deals with the energy production of photovoltaic (PV) cells in different weather conditions. Today, photovoltaic generation plays an important role in generating electricity and satisfies the demand of the island's consumers. The power generation of the PV cell was completely dependent upon sunlight and temperature, but sunlight and temperature changed forever in nature. The many researchers are working on different MPPT technologies for a PV system. Conventional MPPT controllers cannot withstand a sudden change of weather conditions. The main aim of this article is to compare the various conventional and intelligent controller such as the GA, Fuzzy, KGMO, and CNFF for MPPT of the PV system. The proposed intelligent controller was developed and simulated by the MATLAB environment for MPPT in the PV system. In addition, the above results are evaluated and compared. Based on performance, the optimal smart controller has been recommended as MPPT of the PV system