Low-Complexity Power-Balancing-Point Based Optimization for Photovoltaic Differential Power Processing



Chu, Guanying, Wen, Huiqing, Hu, Yihua ORCID: 0000-0002-1007-1617, Jiang, Lin ORCID: 0000-0001-6531-2791, Yang, Yong and Wang, Yiwang
(2020) Low-Complexity Power-Balancing-Point Based Optimization for Photovoltaic Differential Power Processing. IEEE Transactions on Power Electronics, 35 (10). 10306 - 10322.

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Abstract

Differential power processing (DPP) is regarded as a promising architecture in solving mismatching issues among photovoltaic (PV) submodules. Although conventional total-minimum-power-point (TMPP)-based real-time optimization algorithm by using the distributed submodule-level maximum power point tracking and simultaneously the centralized total-minimum-power tracking shows effectiveness in maximizing the power yield. However, uneven power stress among DPP converters, large oscillations, high additional cost for communication among DPP converters, and complicated implementation hinder the practical application. This article proposed a low-complexity power balancing point-based optimization algorithm to reduce the system cost and size, improve the system efficiency, and realize the standardized modular design for DPP converters. Furthermore, simple submodule-level voltage equalization control is implemented to eliminate expensive communication and relieve the control complexity while guaranteeing high maximum-power-point efficiency. The proposed algorithm can reduce the power rating of DPP converters compared with conventional TMPP-based control, which is beneficial to the improvement of system cost, reliability, and lifetime. Both simulation and experimental results under various scenarios are provided to validate the advantages of the proposed algorithm.

Item Type: Article
Uncontrolled Keywords: Maximum power point trackers, Oscillators, Voltage control, Topology
Depositing User: Symplectic Admin
Date Deposited: 02 Mar 2020 11:08
Last Modified: 03 Jun 2021 07:15
DOI: 10.1109/TPEL.2020.2977329
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3077103