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Predictive direct flux control - A new control method of voltage source inverters in distributed generation applications

  • Jiefeng Hu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Licensee MDPI. Voltage source inverters (VSIs) have been widely utilized in electric drives and distributed generations (DGs), where electromagnetic torque, currents and voltages are usually the control objectives. The inverter flux, defined as the integral of the inverter voltage, however, is seldom studied. Although a conventional flux control approach has been developed, it presents major drawbacks of large flux ripples, leading to distorted inverter output currents and large power ripples. This paper proposes a new control strategy of VSIs by controlling the inverter flux. To improve the system's steady-state and transient performance, a predictive control scheme is adopted. The flux amplitude and flux angle can be well regulated by choosing the optimum inverter control action according to formulated selection criteria. Hence, the inverter flux can be controlled to have a specified magnitude and a specified position relative to the grid flux with less ripples. This results in a satisfactory line current performance with a fast transient response. The proposed predictive direct flux control (PDFC) method is tested in a 3 MW high-power grid-connected VSI system in the MATLAB/Simulink environment, and the results demonstrate its effectiveness.
Original languageEnglish
Article number428
JournalEnergies
Volume10
Issue number4
DOIs
Publication statusPublished - 23 Mar 2017

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Inverter flux
  • Predictive control
  • Voltage source inverter (VSI)

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering

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