Power quality improvement ofIntelligent control based 31-Level Asymmetrical Cascaded Multilevel Inverter with DC-DC Flyback converter for hybrid distribution system

Abstract

This paper aims to develop a hybrid renewable energy system under the change in climatic
conditions such as wind speed, radiation of sun and temperature. The main goal of system is to maintain
DC link voltage constant across DC link capacitor or at DC bus. To maintain the same generally for closedloop
control proportional integral (PI) controllers are used. The fixed gain controller encountered the
demerits of the higher probability of failure at the high level of uncertainty, failure to operate in desired
fashion under the change in input conditions, need of retuning of gains for the dynamic condition. To
overcome such disadvantage, some intelligent and optimized tuning methods are adopted. Numbers of
tuning method has been proposed till date to overcome the disadvantages of traditional PI controller. A
GWO (Grey wolf optimizer) based optimized PI controller is proposed for dc-dc converter to realize an
enhanced control and compared with Fuzzy and conventional PI controllers. Similarly the performance of
ANFIS (adaptive neuro-fuzzy inference system) controller which is used to synchronize the grid voltage
and frequency is contrasted with ANN, Fuzzy and conventional PI controllers. The proposed hybrid
renewable system is connected to the grid via a multilevel inverter so that the demand tension of electricity
on the grid is relieved. In this paper, a wind-photovoltaic hybrid power generation system integrated with
grid via converter and inverter is studied and simulated. Finally the proposed system is experimented with
prototype to verify the performance of converter and inverter using DSPIC30F2010 microcontroller