Maximum power tracking of grid integrated PV system with power electronic converters: A hybrid COA-QNN approach

To optimize solar PV power connected to the grid, a new power electronic setup with maximum power point tracking is necessary. This technology minimizes losses and maximizes solar output by employing direct energy transfer to the grid for rated power, while surplus power is directed to a converter for DC loads. So this manuscript proposes a hybrid approach for extracting the maximum power of Photovoltaic with high gain converter. The proposed topology is quantum neural network (QNN) and the combination of Cheetah Optimization Algorithm (COA), known as COA-QNN. This main goal of COA-QNN method is to regulate the active-power needs of loads using solar power generated, and after satisfying the load-demand, surplus power is supplied to the grid. COA is used to optimize the MPPT and QNN is used to forecast the optimal control signal of the converter. After that, the COA-QNN methodology is completed in the MATLAB platform and contrasted with other current approaches. The COA-QNN technique offers superior performance in terms of power-quality (PQ), stability, and settling time compared to other controllers. Simulation analysis shows low total harmonic distortion (THD) at 1.7%, high efficiency at 99.52%, and minimal error at 0.01%, demonstrating its effectiveness over existing methods.