Grid Parameter estimation using Model Predictive Direct Power Control

This paper presents a novel Finite Control Set Model Predictive Control (FS-MPC) approach for grid-connected converters. The control performance of such converters may get largely affected by variations in the supply impedance, especially for systems with low Short Circuit Ratio (SCR) values. A nove...

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Main Authors: Arif, Bilal, Tarisciotti, Luca, Zanchetta, Pericle, Clare, Jon C., Degano, Marco
Format: Article
Published: IEEE 2015
Online Access:https://eprints.nottingham.ac.uk/33122/
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author Arif, Bilal
Tarisciotti, Luca
Zanchetta, Pericle
Clare, Jon C.
Degano, Marco
author_facet Arif, Bilal
Tarisciotti, Luca
Zanchetta, Pericle
Clare, Jon C.
Degano, Marco
author_sort Arif, Bilal
building Nottingham Research Data Repository
collection Online Access
description This paper presents a novel Finite Control Set Model Predictive Control (FS-MPC) approach for grid-connected converters. The control performance of such converters may get largely affected by variations in the supply impedance, especially for systems with low Short Circuit Ratio (SCR) values. A novel idea for estimating the supply impedance variation, and hence the grid voltage, using an algorithm embedded in the MPC is presented in this paper. The estimation approach is based on the difference in grid voltage magnitudes at two consecutive sampling instants, calculated on the basis of supply currents and converter voltages directly within the MPC algorithm, achieving a fast estimation and integration between the controller and the impedance estimator. The proposed method has been verified, using simulation and experiments, on a 3-phase 2-level converter.
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institution University of Nottingham Malaysia Campus
institution_category Local University
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publishDate 2015
publisher IEEE
recordtype eprints
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spelling nottingham-331222020-05-04T17:22:15Z https://eprints.nottingham.ac.uk/33122/ Grid Parameter estimation using Model Predictive Direct Power Control Arif, Bilal Tarisciotti, Luca Zanchetta, Pericle Clare, Jon C. Degano, Marco This paper presents a novel Finite Control Set Model Predictive Control (FS-MPC) approach for grid-connected converters. The control performance of such converters may get largely affected by variations in the supply impedance, especially for systems with low Short Circuit Ratio (SCR) values. A novel idea for estimating the supply impedance variation, and hence the grid voltage, using an algorithm embedded in the MPC is presented in this paper. The estimation approach is based on the difference in grid voltage magnitudes at two consecutive sampling instants, calculated on the basis of supply currents and converter voltages directly within the MPC algorithm, achieving a fast estimation and integration between the controller and the impedance estimator. The proposed method has been verified, using simulation and experiments, on a 3-phase 2-level converter. IEEE 2015-11-19 Article PeerReviewed Arif, Bilal, Tarisciotti, Luca, Zanchetta, Pericle, Clare, Jon C. and Degano, Marco (2015) Grid Parameter estimation using Model Predictive Direct Power Control. IEEE Transactions on Industry Applications, 51 (6). 4614 -4622. ISSN 0093-9994 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7151796 doi:10.1109/TIA.2015.2453132 doi:10.1109/TIA.2015.2453132
spellingShingle Arif, Bilal
Tarisciotti, Luca
Zanchetta, Pericle
Clare, Jon C.
Degano, Marco
Grid Parameter estimation using Model Predictive Direct Power Control
title Grid Parameter estimation using Model Predictive Direct Power Control
title_full Grid Parameter estimation using Model Predictive Direct Power Control
title_fullStr Grid Parameter estimation using Model Predictive Direct Power Control
title_full_unstemmed Grid Parameter estimation using Model Predictive Direct Power Control
title_short Grid Parameter estimation using Model Predictive Direct Power Control
title_sort grid parameter estimation using model predictive direct power control
url https://eprints.nottingham.ac.uk/33122/
https://eprints.nottingham.ac.uk/33122/
https://eprints.nottingham.ac.uk/33122/