Project

Model predictive control for two-stage photovoltaic system

Energy is a vital component for human economic development and growth. The extensive use of fossil fuels has damaged our living environment. Consequently, the world has turned its attention to renewable energy sources. Among them, solar energy has attracted people's attention because it has high quality of energy, is safe and reliable, and does not result in polluting emissions. Due to their decreasing costs and flexible configurations, photovoltaic (PV) systems will undoubtedly become a promising green energy technology to achieve sustainable development.
 Two-stage photovoltaic system is widely used. Two-stage PV system consists of three-level boost (TLB) converter and three-level inverter. The boost converter is connected to the PV array and the inverter is connected to the grid. Two-stage system needs to achieve four main control objectives: PV maximum power point tracking (MPPT), DC bus voltage control, AC side current fluctuation suppression and capacitance neutral point voltage balance. Due to the complicated control targets, the traditional two-stage PV system control is complicated and the control accuracy is not good enough. In this project, the research focus on the model predictive control (MPC) for two-stage PV system. In order to achieve the system control strategy optimization and performance improvement.
 The referenced technical papers and books have been the main source of data in this project. Simulations and results are presented using MATLAB.
 The predictive model of TLB converter and NPC inverter are built in this project. Control strategies for TLB converter and NPC inverter based on model predictive control are discussed and proposed. The effectiveness of this control strategy is verified by simulation.

Project (M.S., Electrical and Electronic Engineering)--California State University, Sacramento, 2017.

Energy is a vital component for human economic development and growth. The extensive use of fossil fuels has damaged our living environment. Consequently, the world has turned its attention to renewable energy sources. Among them, solar energy has attracted people's attention because it has high quality of energy, is safe and reliable, and does not result in polluting emissions. Due to their decreasing costs and flexible configurations, photovoltaic (PV) systems will undoubtedly become a promising green energy technology to achieve sustainable development. Two-stage photovoltaic system is widely used. Two-stage PV system consists of three-level boost (TLB) converter and three-level inverter. The boost converter is connected to the PV array and the inverter is connected to the grid. Two-stage system needs to achieve four main control objectives: PV maximum power point tracking (MPPT), DC bus voltage control, AC side current fluctuation suppression and capacitance neutral point voltage balance. Due to the complicated control targets, the traditional two-stage PV system control is complicated and the control accuracy is not good enough. In this project, the research focus on the model predictive control (MPC) for two-stage PV system. In order to achieve the system control strategy optimization and performance improvement. The referenced technical papers and books have been the main source of data in this project. Simulations and results are presented using MATLAB. The predictive model of TLB converter and NPC inverter are built in this project. Control strategies for TLB converter and NPC inverter based on model predictive control are discussed and proposed. The effectiveness of this control strategy is verified by simulation.

Relationships

Items