A hybrid bio-inspired circulatory system optimization integrated with Model Predictive Current Control for global photovoltaic MPPT under partial shading conditions

This paper presents an advanced bio-inspired strategy for Global Maximum Power Point Tracking (GMPPT) in standalone Photovoltaic (PV) systems operating under partial shading conditions (PSC). The proposed approach integrates a novel Circulatory System-Based Optimization (CSBO) algorithm with a Model Predictive Current Control (MPCC) scheme to enhance tracking speed, accuracy and robustness. The CSBO algorithm emulates the dual-path dynamics of the human circulatory-system by segmenting the population into systemic and pulmonary branches, mirroring oxygen-rich and oxygen-poor flows. This bio-inspired architecture enables a dynamic equilibrium between global exploration and local exploitation, significantly enhancing convergence reliability and robustness. Such a strategy ensures consistent tracking of the GMPP under multi-modal Power-Voltage (P-V) characteristics. Meanwhile, the MPCC leverages real-time inductor current estimation to sharpen transient performance and suppress steady-state oscillations, thereby improving overall tracking efficiency and system stability. The combined CSBO-MPCC approach is implemented on a standalone PV system, and its performance is validated through detailed simulations and experimental results under various irradiance profiles. Comparative analysis with other GMPPT techniques demonstrates significant improvements in convergence speed, tracking efficiency, and stability, particularly under PSC. This work confirms the potential of hybrid bio-inspired MPCC methods in advancing intelligent energy harvesting strategies for standalone renewable energy systems.