A New Model Predictive Control Method for Buck-Boost Inverter-Based Photovoltaic Systems

Publikation: Beitrag in Fachzeitschrift › Forschungsartikel › Beigetragen › Begutachtung

Beitragende

  • Saeed Danyali - , Ilam University (Autor:in)
  • Omid Aghaei - , Ilam University (Autor:in)
  • Mohammadamin Shirkhani - , Ilam University (Autor:in)
  • Rahmat Aazami - , Ilam University (Autor:in)
  • Jafar Tavoosi - , Ilam University (Autor:in)
  • Ardashir Mohammadzadeh - , Shenyang University of Technology (Autor:in)
  • Amir Mosavi - , Technische Universität Dresden, Óbuda University, Slovak University of Technology (Autor:in)

Abstract

This study designed a system consisting of a photovoltaic system and a DC-DC boost converter with buck-boost inverter. A multi-error method, based on model predictive control (MPC), is presented for control of the buck-boost inverter. Incremental conductivity and predictive control methods have also been used to track the maximum power of the photovoltaic system. Due to the fact that inverters are in the category of systems with fast dynamics, in this method, by first determining the system state space and its discrete time model, a switching algorithm is proposed to reduce the larger error for the converter. By using this control method, in addition to reducing the total harmonic distortion (THD), the inverter voltage reaches the set reference value at a high speed. To evaluate the performance of the proposed method, the dynamic performance of the converter at the reference voltage given to the system was investigated. The results of system performance in SIMULINK environment were simulated and analyzed by MATLAB software. According to the simulation results, we can point out the advantage of this system in following the reference signal with high speed and accuracy.

Details

OriginalspracheEnglisch
Aufsatznummer11731
FachzeitschriftSustainability (Switzerland)
Jahrgang14
Ausgabenummer18
PublikationsstatusVeröffentlicht - Sept. 2022
Peer-Review-StatusJa

Schlagworte

Ziele für nachhaltige Entwicklung

ASJC Scopus Sachgebiete

  • Informatik (sonstige) - artificial intelligence, buck-boost inverter, DC-DC converter, machine learning, model predictive control (MPC), PV, renewable energy, single-stage inverter
  • Geografie, Planung und Entwicklung - artificial intelligence, buck-boost inverter, DC-DC converter, machine learning, model predictive control (MPC), PV, renewable energy, single-stage inverter
  • Erneuerbare Energien, Nachhaltigkeit und Umwelt - artificial intelligence, buck-boost inverter, DC-DC converter, machine learning, model predictive control (MPC), PV, renewable energy, single-stage inverter
  • Umweltwissenschaften (sonstige) - artificial intelligence, buck-boost inverter, DC-DC converter, machine learning, model predictive control (MPC), PV, renewable energy, single-stage inverter
  • Energieanlagenbau und Kraftwerkstechnik - artificial intelligence, buck-boost inverter, DC-DC converter, machine learning, model predictive control (MPC), PV, renewable energy, single-stage inverter
  • Hardware und Architektur - artificial intelligence, buck-boost inverter, DC-DC converter, machine learning, model predictive control (MPC), PV, renewable energy, single-stage inverter
  • Computernetzwerke und -kommunikation - artificial intelligence, buck-boost inverter, DC-DC converter, machine learning, model predictive control (MPC), PV, renewable energy, single-stage inverter
  • Management, Monitoring, Politik und Recht - artificial intelligence, buck-boost inverter, DC-DC converter, machine learning, model predictive control (MPC), PV, renewable energy, single-stage inverter

Schlagwörter

  • artificial intelligence, buck-boost inverter, DC-DC converter, machine learning, model predictive control (MPC), PV, renewable energy, single-stage inverter