Bayesian Optimization-Based Tuning of the Proportional-Integral Controller for Grid-Connected Three-Level NPC Converter

Power converters provide energy interface in various applications; in photovoltaics, they interconnect solar panels to the grid, with two‑level inverters being the most common across virtually all power ranges, followed by three‑level neutral point clamped (3LNPC) converters. The proportional-integral (PI) controller is the most commonly employed controller for these converters. This paper proposes the use of Bayesian Optimization (BO) to tune the PI controller for a 3LNPC converter connected to the grid. The optimization algorithm was used to determine the controller’s tuning gains through computational simulation. Subsequently, the PI controller tuned using BO was implemented on an experimental test bench to validate the concept. The performance of the controller tuned with the proposed method was compared to PI controllers adjusted using classical methods widely found in the literature. The test results demonstrated that Bayesian Optimization is straightforward to implement and, when compared to the Genetic Algorithm (GA), it exhibited a more effective and targeted exploration of the search space. This led to superior PI controller tuning, with improved dynamic response and reduced total harmonic distortion relative to the benchmark methods.