Improved Artificial Neural Network Grid Synchronization Technique for Three-Phase Grid Systems based on Synthesis Segmental Multilevel Converter

Abstract

The solar Photovoltaic (PV) systems have gained a significant attention due to its easy implementation and availability. In which, the proper energy management should be mainly concentrated for a successful PV power utilization. In the traditional works, various controlling techniques have been developed for reactive power compensation. But, it lacks with the issues related to reduced system performance, increased loss, and high harmonics. To solve these issues, this paper aims to develop a new controlling methodology, named as Synthesis Segmental Multilevel Converter (SSMC) for reactive power compensation in a three phase grid systems. Initially, it extracts the maximum power from the solar PV systems by using an Enhanced Perturb and Observe (EPO) method. The panel separation is done and the three phase power input is given to the SSMC converter, where the synchronization and switching pulse generation processes are performed. During synchronization, integrated techniques such as Proportional Integral (PI), Fuzzy Logic Controller (FLC), and Improved Artificial Neural Network (IANN) techniques are utilized to maintain the voltage, magnitude and phase angle in a same level. Consequently, the Inductance Capacitance (LC) filtering technique is applied to reduce the harmonics distortion in the signal. After that, the Park transformation is used to perform the dq0 to abc transformation, which is implemented for reducing the high volume of error. Finally, the error free signal is fed to the three phase grid system with reduced harmonics also both the simulation and analytical results have been taken for analyzing the performance of the proposed technique.