Open AccessCentralised model‐predictive decoupled active–reactive power control for three‐level neutral point clamped photovoltaic inverter with preference selective index‐based objective prioritisation
Author(s) -
Bonala Anil Kumar,
Sandepudi Srinivasa Rao
Publication year - 2019
Publication title -
iet power electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.637
H-Index - 77
eISSN - 1755-4543
pISSN - 1755-4535
DOI - 10.1049/iet-pel.2018.5825
Subject(s) - control theory (sociology) , ac power , maximum power point tracking , model predictive control , power factor , engineering , photovoltaic system , power control , inverter , voltage , computer science , power (physics) , control (management) , electrical engineering , quantum mechanics , physics , artificial intelligence
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.