Centralised model-predictive decoupled active–reactive power control for three-level neutral point clamped photovoltaic inverter with preference selective index-based objective prioritisation

This study presents a single-stage grid-tied three-level neutral point clamped photovoltaic inverter with a centralised model-predictive decoupled active–reactive power control. The proposed centralised model predictive control (CMPC) incorporates the constraints of maximum power extraction, dc-link...

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Bibliographic Details
Published inIET power electronics Vol. 12; no. 4; pp. 840 - 851
Main Authors Bonala, Anil Kumar, Sandepudi, Srinivasa Rao
Format Journal Article
LanguageEnglish
Published The Institution of Engineering and Technology 10.04.2019
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ISSN1755-4535
1755-4543
DOI10.1049/iet-pel.2018.5825

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Summary:This study presents a single-stage grid-tied three-level neutral point clamped photovoltaic inverter with a centralised model-predictive decoupled active–reactive power control. The proposed centralised model predictive control (CMPC) incorporates the constraints of maximum power extraction, dc-link capacitor voltage balancing and active–reactive power tracking in a single objective function. The dc-link voltage of the inverter is regulated to its reference for extracting the maximum power. In order to eliminate the impact of reactive power exchange on floating dc-link voltage regulation, a decoupled active–reactive power control is used in the CMPC. Furthermore, a preference selective index-based dynamic weighting factor selection approach is introduced to maintain the relative importance between the power tracking and dc-link capacitor voltage balancing. The proposed control approach eliminates the outer dc-link voltage control loop and also, the empirical approach required for the selection of weighting factors. As a result, it ensures an optimal control action in each sampling period to improve the steady-state and dynamic tracking performance of the control objectives. The proposed control approach is experimentally verified by using a 1.2 kW laboratory-scale prototype and the results are presented to demonstrate its effectiveness compared to the classical proportional–integral-based model predictive control.
ISSN:1755-4535
1755-4543
DOI:10.1049/iet-pel.2018.5825