Abstract
This paper discussed an enhanced predictive model control (PMC) strategy based distributed maximum power point tracking DMPPT with a prediction horizon of one sampling time in order to achieve high performances from standalone solar photovoltaic system in the presence of dynamic weather variations and partial shading. In this paper, three PV modules are interfaced to the DC-BUS through three cascaded DC-DC boost power converters used with the enhanced PMC based DMPPT algorithm, the proposed technique calculates all possible switching states before applying to the three converters, and the adequate switching state is selected by minimization of a defined cost function, to regulate the duty cycle of the power converters independently, and to supervise maximum power point of the three cascaded PV modules, in order to avoid mismatching phenomena between modules which is considered the main cause for performance degradation and efficiency drop. The performances of the proposed system and control strategy are verified and confirmed when comparing with other conventional MPPT methods such Perturb and Observe (P&O) algorithm based DMPPT using MATLAB/Simulink interface.
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Ikaouassen, H., Moutaki, K., Raddaoui, A., Rezkallah, M. (2019). Enhanced Predictive Model Control Based DMPPT for Standalone Solar Photovoltaic System. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2018). AI2SD 2018. Advances in Intelligent Systems and Computing, vol 912. Springer, Cham. https://doi.org/10.1007/978-3-030-12065-8_18
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DOI: https://doi.org/10.1007/978-3-030-12065-8_18
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