An integrated high-gain boost converter and Grasshopper Optimization Framework for fast global MPPT and high-efficiency solar PV under dynamic irradiance

Abstract

The research work proposes a novel high-gain boost converter (HGBC) with a Grasshopper Optimization Algorithm (GHO) Maximum Power Point Tracking (MPPT) for a solar photovoltaic (SPV)system. Traditionally used boost converters have low voltage gain, high switching stress, and poor dynamic response under partial shading and fast varying irradiance conditions. Further, traditional MPPT methods exhibit slow convergence behavior and are unable to achieve accurate global MPPT under nonlinear operating conditions. The HGBC utilizes a multi-inductor and multicapacitor topology, which establishes high voltage conversion gain in addition to low switching stress with enhanced output stability. The GHO algorithm dynamically varies the duty cycle to facilitate rapid and precise global MPPT. The proposed system is modeled and analyzed using MATLAB simulation under steady-state and dynamic irradiance (500–1000 W/m 2 ). Results exhibit that the proposed HGBC–GHO achieves a peak efficiency of 93.6%, which is greater than that of Whale Optimization Algorithm (92.4%), Adaptive Neuro Fuzzy Inference Systems (91.4%), Marine Predator Algorithm (MPA) (91.2%), Fuzzy Logic Control (90.4%), and Artificial Neural Network (ANN) (89.8%). It shows a fast convergence time of 0.17 s, low output ripple at 1.2%, and reduced computational effort with only 65 iterations, compared with MPA's 150 and ANN's 125 iterations. At dynamic irradiance (1000, 700, and 500 W/m 2 ), the output power levels of the system are stable at 204.5, 108.1, and 56.02 W, respectively, with a settling time of less than 36 ms. These results demonstrate improved tracking accuracy, reduced oscillations, and enhanced dynamic responses, thus confirming the proposed HGBC–GHO's performance potential for SPV applications.