【目的】 随着双馈风电场经串补线路大规模并网，机组与电网之间相互作用引发的次同步振荡（sub-synchronous oscillation，SSO）问题日益突出。现有的次同步阻尼控制器（sub-synchronous damping controller，SSDC）在适应性、鲁棒性方面存在局限，难以应对复杂运行工况的变化。为提升系统对宽频带SSO的抑制效果，提出了一种兼具高可靠性与强自适应能力的模型-数据融合阻尼控制方法。【方法】 提出一种基于储能的模型-数据融合阻尼控制架构，该架构将机理清晰的传统SSDC与基于无模型自适应控制（model-free adaptive control，MFAC）的SSDC相结合，并借助同一网侧储能系统注入阻尼信号。传统SSDC提供基频附近的基础阻尼支撑，基于MFAC的SSDC则实时跟踪系统动态并适应频率偏移，从而避免多设备协调与复杂参数整定。【结果】 在不同参数变化与大扰动故障等多种工况下，相较于其构成单元，所提控制器均能快速、平稳地抑制不同频率的SSO，且表现出更优的带宽适应性与动态调节性能。【结论】 所构建的融合阻尼控制架构有效结合了固定结构的稳定性与数据驱动的灵活性，结构简洁、易于实现，在提升系统动态性能与鲁棒性方面具有显著效果。

[Objective] With the large-scale integration of doubly-fed induction generator （DFIG） based wind farms via series-compensated transmission lines， the issue of sub-synchronous oscillation （SSO） induced by the interaction between units and the grid has become increasingly prominent. Existing sub-synchronous damping controllers （SSDC） face limitations in adaptability and robustness， making it difficult to cope with complex and varying operating conditions. To enhance the suppression of wide-band SSO， this paper proposes a model-data fusion damping control method featuring high reliability and strong adaptability. [Method] A model-data fusion damping control architecture based on energy storage is proposed. This architecture combines a mechanism-based traditional SSDC with a model-free adaptive control （MFAC） based SSDC， injecting damping signals via the same grid-side energy storage system. The traditional SSDC provides fundamental damping support near the fundamental frequency， while the MFAC-based SSDC tracks system dynamics in real-time and adapts to frequency deviations， thereby avoiding multi-device coordination and complex parameter tuning. [Results] Under various operating conditions， including parameter variations and large disturbance faults， the proposed controller can rapidly and smoothly suppress SSO at different frequencies compared to its constituent units. It demonstrates superior bandwidth adaptability and dynamic regulation performance. [Conclusions] The constructed fusion damping control architecture effectively combines the stability of fixed structures with the flexibility of data-driven approaches. It features a concise structure and ease of implementation， showing significant effectiveness in improving system dynamic performance and robustness.