Cesar Limones;Nimrod Vázquez;Ricardo Femat;Leonel Estrada;Jeziel Vázquez;Claudia Hernandez;Angel de Castro;Joaquín Vaquero
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Single-Phase Grid-Connected Inverter With Predictive Control Cost Function for Leakage Current Mitigation and FAS Model
Leakage current is one of the main issues for transformerless grid-connected photovoltaic inverters, and its reduction is a primary focus of various studies reported in the literature. This article proposes and validates a finite control set model predictive controller with a novel cost function to mitigate leakage current in a single-phase full-bridge (FB) grid-connected inverter with an LCL filter, making no modifications to the traditional FB topology and without adding any extra components. Furthermore, mathematical modeling is based on the fully actuated system model, allowing a reduction in the number of variables sensed during implementation, resulting in only grid current and voltage being measured.
期刊介绍:
The IEEE Open Journal of the Industrial Electronics Society is dedicated to advancing information-intensive, knowledge-based automation, and digitalization, aiming to enhance various industrial and infrastructural ecosystems including energy, mobility, health, and home/building infrastructure. Encompassing a range of techniques leveraging data and information acquisition, analysis, manipulation, and distribution, the journal strives to achieve greater flexibility, efficiency, effectiveness, reliability, and security within digitalized and networked environments.
Our scope provides a platform for discourse and dissemination of the latest developments in numerous research and innovation areas. These include electrical components and systems, smart grids, industrial cyber-physical systems, motion control, robotics and mechatronics, sensors and actuators, factory and building communication and automation, industrial digitalization, flexible and reconfigurable manufacturing, assistant systems, industrial applications of artificial intelligence and data science, as well as the implementation of machine learning, artificial neural networks, and fuzzy logic. Additionally, we explore human factors in digitalized and networked ecosystems. Join us in exploring and shaping the future of industrial electronics and digitalization.