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Simplified Predictive Control Strategy for Dual-Input Three-Phase Split-Source Inverter With Minimized Computational Burdens

ملخص البحث

Split-source inverters (SSIs) found vast research
concerns as they utilize lower component numbers and sizes
than other solutions, such as Z-source inverters (ZSIs) and
quasi-ZSIs (qZSIs). Recently, multiple photovoltaic (PV) input
port-based SSI has led to a further reduction of the needed
components compared to single-input topologies. However, controlling
multiple inputs with possible different generated powers,
generating high-quality ac output voltage and current, and
managing SSI’s inductor currents and capacitor voltage control
represent challenging tasks for classical pulse width modulation
(PWM) and other classical control methods. Therefore,
a multiple-objective-based model predictive controller (MPC)
with minimized computational burdens is proposed in this
article based on two novel approaches, namely, the simplified
current-based finite control set model-predictive control (SCFCSMPC)
approach and the simplified voltage-based finite
control set model-predictive control (SV-FCSMPC) approach.
The two proposed approaches ensure effective control of input
sources during partial or complete shading in the case of two
input PV sources. Moreover, the proposed approaches eliminate
the need for weighting factors in the control of the cost function,
simplifying the MPC design. Consequently, the two proposed
MPC approaches avoid cascaded loops for controlling multiple
input topologies, weighting factor adjustment procedures, and
high computation burden problems. Experimental results with
performance evaluations at different expected scenarios are provided
in this article to confirm the superiority and applicability
of the newly proposed weighting factorless MPC approaches.

مؤلف البحث
Mustafa Abu-Zaher, Fang Zhuo, Mokhtar Aly, Jiachen Tian, Mostafa Ahmed
تاريخ البحث
مجلة البحث
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS
مؤلف البحث
صفحات البحث
4703-4715
الناشر
IEEE
تصنيف البحث
Q1
عدد البحث
12
موقع البحث
https://ieeexplore.ieee.org/document/10648756/
سنة البحث
2024