Spotted Hyena Optimization Method for Harvesting Maximum PV Power under Uniform and Partial-Shade Conditions

Maximum power-point-tracking techniques applied for partially shaded photovoltaic array yield maximum power output via operating the panel at its most efficient voltage. Considering the noticeable issues existing with the available methods, including steady-state oscillations, poor tracking capability and complex procedures, a new bioinspired Spotted-Hyena Optimizer (SHO) is proposed. It follows simple implementation steps, and does not require additional controller-parameter tuning to track the optimal power point. To validate the versatility of the proposed method, the SHO algorithm is applied to track the maximum power of different string arrangements under six partial-shade conditions. Further, to authenticate SHO's methods, its results are compared with perturb-and-observe (P&O), and particle-swarm-optimization (PSO) methods. As a result of its implementation, it is observed that the tracking speed of SHO towards the global convergence for four patterns under 4S2P are 0.34 s, 0.24 s, 0.2 s, and 0.3 s, which is far less than the PSO and P&O methods. Further, to demonstrate its suitability, a hardware prototype is built and tested for various operating conditions. The experimental results are in good agreement with the simulated values.

Automated summary

A new bioinspired optimization algorithm, called Spotted-Hyena Optimizer (SHO), is proposed for maximum power-point tracking of partially shaded photovoltaic arrays. The algorithm is easy to implement and does not require additional parameter tuning. The results show that SHO achieves faster global convergence compared to traditional perturb-and-observe (P&O) and particle-swarm-optimization (PSO) methods.