Sensorless Current Model Predictive Control for Maximum Power Point Tracking of Single-Phase subMultilevel Inverter for Photovoltaic Systems
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abstract
2016 IEEE. Stochastic dynamic behavior of solar energy necessitates the use of robust controllers for photovoltaic (PV) power electronics interfaces to maximize the energy harvest by continuous operation at maximum power point (MPP). This paper proposes a sensorless current model predictive control maximum power point tracking (SC-MPC-MPPT) algorithm. By predicting the future behavior of the power conversion stage, the proposed controller features fast and stable performance under dynamic ambient condition and negligible oscillation around MPP at steady state. Moreover, it does not require expensive sensing and communication equipment and networks to directly measure the changing solar insolation level. The power conversion stage includes an upstream boost dc/dc power conversion to a dc-link capacitor, and a downstream seven-level sub-Multilevel Inverter (sMI) from the dc-link capacitor to the grid. The sMI is using three power arms cascaded with an H-bridge inverter. This topology brings considerable benefits such as reduced number of power switches and their gate drivers when compared to the traditional multilevel inverters. Model Predictive Control (MPC) is employed for current regulation of the sMI, thus eliminating the need of cascaded classical control loops and modulator. The proposed SC-MPC-MPPT technique for a boost converter is implemented experimentally using the dSPACE DS1007 platform.
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2016 IEEE Energy Conversion Congress and Exposition (ECCE)
