Integration of modular multilevel converters and soft computing for efficient grid-connected PV systems

An innovative approach that leverages Multilevel Inverter (MLI) advanced control methodologies to improve grid-connected Photovoltaic (PV) system performance is introduced in this work. This Modular Multilevel Converter (MMC) consists of both high-frequency converter and low-frequency inverter, facilitating efficient power conversion. To maximize PV power extraction, novel Chaotic Prairie Dog Optimization (PDO) based Fuzzy Maximum Power Point Tracking (MPPT) algorithm is implemented. The optimized Fuzzy MPPT output, combined with reactive power information, serves as inputs to dq theory-based Proportional-Integral (PI) controller. The obtained reference current undergoes pre-processing, Discrete Cosine Transform (DCT) based segmentation, Self-adapting genetic algorithm (GA)-Probabilistic Neural Networks (PNN) based classification. Furthermore, validation through Matlab simulations and hardware implementation underscores the proposed approach’s effectiveness in optimizing grid-connected PV system operation. With a total harmonic distortion (THD) of 1.25% in simulation and 1.6% in experiments, the system exhibits a high level of precision in generating sinusoidal current waveforms.