Abstract

In order to improve the performance of three-phase NPC inverters under unbalanced working conditions, a quasi-proportional complex integral controller (QPCI) three-phase inverter control method based on an adaptive fractional-order algorithm is proposed. Firstly, the reasons for the poor performance of conventional control methods under unbalanced load conditions are analyzed. Subsequently, a fractional-order quasi-proportional complex integral (FO-QPCI) controller is proposed, and the effects of its control parameters, including proportional gain (KP), integral gain (KI), resonant frequency (ωc), and fractional order (μ), are systematically investigated. Furthermore, the optimal control parameter dataset is utilized to train a Generalized Regression Neural Network (GRNN), through which an adaptive parameter-tuning model is established. As a result, the proposed FO-QPCI controller can dynamically adjust its control parameters according to different voltage references and load unbalanced levels. Finally, to verify the effectiveness of the proposed control strategy, both simulation models and an experimental platform based on a three-level inverter are developed. The results show that the proposed control method has good control ability for the output voltage and harmonics under unbalanced working conditions.

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