Quantum Grover Search-Inspired Global Maximum Power Point Tracking for Photovoltaic Systems Under Partial Shading Conditions

Tracking the global maximum power point (GMPP) of photovoltaic (PV) arrays under partial shading conditions (PSCs) is a critical research area. This work proposes a quantum-inspired algorithm called Grover search-inspired global maximum power point tracking (GGMPPT) that combines the ideas of variable boundary and cutting with quantum probabilistic sampling to track the GMPP efficiently. The first stage of GGMPPT adopts adaptive sampling to quickly locate the duty cycle range of main peaks, improving search efficiency. In the second stage, a cutting idea combined with quantum probability sampling is proposed for designing Oracle to rapidly track the GMPP. The acceleration effect becomes more evident with an increase in the number of peaks. Finally, GGMPPT is compared with the other five algorithms in various aspects, and it is found that GGMPPT has significant advantages in terms of tracking speed and overall performance.