ADALINE Network-based Adaptive Harmonic Suppression Strategy for Totem-pole Bridgeless PFC Converter

In the GaN totem-pole bridgeless power factor correction (PFC) converter, the grid-side voltage, load line surges, and LC resonance can cause grid-side current harmonics, thereby degrading the quality of the power grid. Meanwhile, the traditional harmonic suppression methods have unsatisfactory adaptability under a wide load range. To this end, this paper proposes an adaptive harmonic suppression strategy for the totem-pole bridgeless PFC converter based on the Adaptive linear neuron (ADALINE) network. Firstly, the input impedance model of the converter is established, and the numerical variation law of the input impedance under different load conditions is analyzed. Furthermore, the working principle of the ADALINE network is clarified, and the LMS algorithm is adopted to implement the adaptive update of weights, which greatly improves adaptability under a wide load range. Finally, the effectiveness and adaptability of the proposed strategy are verified on the totem-pole bridgeless PFC converter platform.