A Robust Predictive MPPT Strategy: An Enabler for Improving the Photovoltaic Conversion Source

Abstract

The key drivers for Maximum Power Point Tracking (MPPT) solutions in photovoltaic (PV) power plants are independency from solar panels, fast tracking of irradiance and temperature variations, and efficiency of conversion capacity. In the most available MPPT solutions, some issues such as high implementation burden and performance degradation during abrupt changes have not been sufficiently investigated. To tackle these issues, an optimized scheme for MPPT is implemented in large-scale PV power conversion system, which is designed based on improved circuit of the Modular Multilevel Converter (MMC) configuration. The developed method is featured through an optimized version of Finite-Control-Set Model Predictive Control (FCS-MPC) to be an enabler for steadying the harvesting mechanism of solar power. The estimation design provides the current sensors elimination in PV panels against commonly utilized MPPT algorithms, which is hampered by costly sensing devices used in situ. By designing such control strategy, fast tracking response for the PV system along with an acceptable control bandwidth are implemented to account for solar irradiation transients and PV panels uncertainties. The improved power circuit of conversion topology allows decreased number of solar cells and energy storage sizing by half in comparison to the basic MMC. The effectiveness of the designed MPPT method over the solar irradiance variations is compared with both Ripple-Correlation Control (RCC) and Fractional Open-Circuit Voltage (FOCV) control schemes. The performance of the discussed PV power station is also assessed with respect to different operational scenarios via simulations to validate the developed concept.