Universal Journal of Electrical and Electronic Engineering 6(4): 223-238, 2019 http://www.hrpub.org DOI: 10.13189/ujeee.2019.060405 Design of an Optimum Single Phase Inverter for a Grid Tie PV System Ibtissem TISS 1,* , Abdulrahman ALAHDAL 2 , Kaiçar AMMOUS 1 , Anis AMMOUS 1,2 1 Power Electronics Group (PEG), Department of Electrical Engineering, National School of Engineers of Sfax, University of Sfax, Tunisia 2 Department of Electrical Engineering, College of Engineering and Islamic Architecture, Umm Al-Qura University, Saudi Arabia Received July 3, 2019; Revised September 3, 2019; Accepted September 16, 2019 Copyright©2019 by authors, all rights reserved. Authors agree that this article remains permanently open access under the terms of the Creative Commons Attribution License 4.0 International License Abstract Power converter optimization by genetic algorithm (GA) is used to provide simpler and more reliable converter design for high efficiency, small size and low cost. This paper presents a Computer-Aided Design Optimization Tool based on GA to determine the optimal structure of single-phase voltage source inverter devoted to grid-connected photovoltaic applications. An accurate non-linear averaged model was used to model the power converter. The hysteresis technique was used to control the output sine wave current of the inverter while the Elitist Non-dominated Sorting Genetic Algorithm NSGA-II was used to search the Pareto optimal front and the best design in terms of efficiency, volume and cost under electrical constraints. The converter model and the NSGA-II algorithm are developed in the MATLAB/Simulink environment. The problem formulation was detailed. It was shown that the optimization of a power converter, working in a given application without the need of tedious and expensive experimental tests classically used to build this converter, is possible by mean of simulation. This will decrease time to market phase for manufacturers. Keywords Genetic Algorithm, DC/AC Converter, Non-Ideal Averaged Model, Hysteresis Control Strategy, NSGA-II 1. Introduction Due to rising prices and environmental impacts of fossil fuels, renewable energy resources have been considered as one of the most efficient solutions to meet global energy demand [1]-[3]. Photovoltaic (PV) solar technology is the fastest growing energy source for renewable energy production at an annual average rate of 8.3% [4]. Grid integrated PV energy sources have been widely used to produce electricity, but their performances need to be improved. To access the grid, the power electronic converter is an extremely important element for photovoltaic systems. To maximize performance, an optimized design of the conversion stage becomes the key to achieve the above goal. A general block scheme of a PV system connected to the grid is shown in Fig. 1. In such a structure, the inverter is a basic component of the grid connected PV systems. Its main task is to convert the direct current to a quasi-sinusoidal form to be injected into electrical grid. The design of power electronic converter or its components by optimization using metaheuristic methods, such as genetic ones, is an effective and widely used approach to obtain an optimal system with better performance [5]. In [6], the authors proposed an optimal design of a DC/DC cascaded converter dedicated to grid- connected photovoltaic systems to maximize efficiency or minimize volume. The obtained results showed that the best solution is a topology with two boosts converters in cascade. References [7]-[9] set out a design methodology based on the multi-objective genetic algorithm of DC/DC converters for grid-connected PV systems to minimize losses, size and cost. Multi-objective optimization was used to obtain the optimal design of a distributed maximum power point tracking synchronous boost converter in [10]. In detail, the efficiency and reliability of the power converter are maximized while its price is minimized. In [11], a study of modelling approaches of several components for power electronic converter was performed. In [12] and [13], the authors adopted a multi-objective GA-based methodology to design and select the appropriate DC/DC stage for a module-integrated inverter. In [14], a systematic methodology using multi-objective evolutionary algorithm and multi-objective covariance matrix adaptation evolution strategy are used in the extraction of power diode parameters.