Modified fuzzy logic and artificial bee colony: An artificial intelligence approach to optimization and power quality improvement in an MPPT-based system

Microgrids are the most efficient method for generating, distributing, and regulating power for consumers using localized distributed energy resources. Nevertheless, achieving optimal economic dispatch and power quality enhancement in microgrids is a significant and challenging topic since solar and wind power generation are inconsistent. In this paper, optimization, and improvement of power quality in a maximum power point tracker (MPPT) based system utilizing artificial intelligence (AI) techniques are studied. The efficiency of a photovoltaic and wind energy system is maximized in this work by using an AI optimization strategy to determine the best scaling parameters for a fuzzy logic-based MPPT controller. This paper presents an improved fuzzy logic and artificial bee colony (FLABC) technique for optimization and power quality enhancement in an MPPT-based system. optimal economic dispatch solution for a microgrid, with the goal of meeting load and balance demand, while reducing the cost of power generation and gas emissions. The FLABC technique is proposed for optimization and power quality enhancement in an MPPT-based hybrid renewable system. The core strength of this study is in the application of AI engineering, particularly in the field of renewable energy as a significant replacement for fossil fuels. The power that goes from the PV wind to the load is directly impacted by the setting of the input and output parameters. Firstly, mathematical model that considers the various traits of distributed generation units and loads was built, with the aim to enhance ABC's global convergence performance to make the model fit. Second, the key stages were outlined for using the modified ABC to solve the optimal power quality enhancement. Thirdly, several scenarios simulations were highlighted the advantages and potency of the suggested strategy for optimal power quality enhancement in microgrids. The study was conducted using MATLAB/Simulink software for simulations. Under various climatic situations, the suggested ABC-based fuzzy controller's performances are compared to those attained using fuzzy logic and ABC controllers. The simulation results demonstrated that the suggested Fuzzy logic-based ABC controller outperformed the performance in terms of the solar energy gained.