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An_Adaptive_Model_Predictive_Controller_for_Current_Sensorless_MPPT_in_PV_Systems.pdf (3.19 MB)

An Adaptive Model Predictive Controller for Current Sensorless MPPT in PV Systems

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journal contribution
submitted on 2024-07-07, 10:37 and posted on 2024-07-07, 10:38 authored by Morcos Metry, Robert S. Balog

Finite control set model predictive control (MPC) is a model-based control method that can include multi-objective optimization, constrained control, adaptive control, and online auto-tuning of weighting factors all in a single controller that exhibits fast dynamic tracking. This paper utilizes the model-based framework of MPC to develop a sensorless current maximum power point tracking (MPPT) algorithm. Eliminating the current sensor can reduce the cost and improve the reliability of the photovoltaic system. This paper also utilizes constrained control and online auto-tuning of MPC to develop an adaptive perturbation MPPT to reduce steady-state oscillation and improve dynamic performance. This paper builds in a single framework the different layers of the MPPT problem: control, estimation, and MPPT. The proposed adaptive perturbation sensorless current mode MPPT (ASC-MPPT) technique performance is compared to the well-known incremental conductance (InCon) MPPT technique. The EN50530 European industrial test standards were used to demonstrate performance.

Other Information

Published in: IEEE Open Journal of Power Electronics
License: https://creativecommons.org/licenses/by-nc-nd/4.0/
See article on publisher's website: https://dx.doi.org/10.1109/ojpel.2020.3026775

Additional institutions affiliated with: Electrical and Computer Engineering Department - TAMUQ

Funding

Qatar National Research Fund (PDRA5-0422-19004), Solar Roof Tile: Transforming Residential Photovoltaic Systems in Qatar.

History

Language

  • English

Publisher

IEEE

Publication Year

  • 2020

License statement

This Item is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Institution affiliated with

  • Texas A&M University at Qatar