Abstract
The energy generation efficiency of photovoltaic (PV) systems is compromised by partial shading
conditions (PSCs) of solar irradiance with many maximum power points (MPPs) while tracking output
power. Addressing this challenge in the PV system, this article proposes an adapted hybrid control
algorithm that tracks the global maximum power point (GMPP) by preventing it from settling at
different local maximum power points (LMPPs). The proposed scheme involves the deployment of a
3 × 3 multi-string PV array with a single modified boost converter model and an adapted perturb and
observe-based model predictive control (APO-MPC) algorithm. In contrast to traditional strategies,
this technique effectively extracts and stabilizes the output power by predicting upcoming future
states through the computation of reference current. The boost converter regulates voltage and
current levels of the whole PV array, while the proposed algorithm dynamically adjusts the converter’s
operation to track the GMPP by minimizing the cost function of MPC. Additionally, it reduces
hardware costs by eliminating the need for an output current sensor, all while ensuring effective
tracking across a variety of climatic profiles. The research illustrates the efficient validation of the
proposed method with accurate and stable convergence towards the GMPP with minimal sensors,
consequently reducing overall hardware expenses. Simulation and hardware-based outcomes
reveal that this approach outperforms classical techniques in terms of both cost-effectiveness and
power extraction efficiency, even under PSCs of constant, rapidly changing, and linearly changing
irradiances.