基于拓扑可迁移网络的双有源桥变换器家族通用建模方法

The emerging gray-box modeling for power converters effectively mitigates model discrepancies seen in traditional physics-based white-box models while offering a data-light, explainable alternative to data-driven black-box models. However, a significant challenge remains existing gray-box modeling approaches suffer from poor generalization to out-of-domain topologies. This limitation necessitates rebuilding or retraining the model when a new topology is encountered, hindering widespread adoption. Catering for these challenges, this article proposes a generic gray-box modeling approach tailored for the dual-active-bridge (DAB) converter topology family, which is based on a proposed topology transferrable physics-in-architecture mixture density network (T2PA-MDN). As the core part, the T2PA network retrofits recurrent neurons to embed circuit physics seamlessly via discretized numeric methods, enabling efficient topology transfer. Moreover, a probabilistic mixture density network (MDN) quantifies ambient fluctuations using a mixture of Gaussian distributions, mitigating model discrepancies. The proposed modeling methodology is demonstrated with three topology transfer design cases, in which the model is trained on a nonresonant DAB with merely a five-time series and is easily transferred to resonant, multilevel, and multiport topologies with no extra data or training. Algorithm analysis and 2-kW hardware experiments have verified the feasibility and the superiority of T2PA-MDN. This research aims to pioneer a new direction for the future gray-box modeling of power converters, toward generalization across diverse topologies but effectiveness.