基于油中溶解气体动力学分析的电力变压器潜伏性故障监测技术研究

Research on Incipient Fault Monitoring Technology for Oil-immersed Power Transformers Based on Dissolved Gas Dynamics Analysis

  • 摘要: 为提升变压器潜伏性故障监测的准确性,基于绝缘油中溶解气体扩散的动力学分析,提出了一种DGA多点监测的布点选取方法。首先,通过分子动力学模拟,获得了七类故障气体在绝缘油中随温度变化的扩散系数拟合式,揭示了微观扩散机制。进而基于换流变压器二维结构,耦合Fick扩散定律与修正Navier-Stokes方程,构建油流-气体输运动力学模型,仿真分析了不同故障位置下氢气扩散的受影响影响规律。结果表明,高流速区气体以对流扩散主导呈现广域分布,而低流速区因Fick扩散效应显著导致气体局部富集。最终,通过叠加分析六组预设故障源释放氢气的稳态浓度空间分布,识别出综合气体浓度最高的区域,据此确定了5个最优监测点(P1~P5)。本研究从气体动态输运角度为变压器DGA多点协同监测布点提供了理论支撑,对提升潜伏性故障的早期识别能力具有重要意义。

     

    Abstract: To enhance the accuracy of latent fault monitoring in transformers, a multi-point DGA monitoring location selection method based on kinetic analysis of dissolved gas diffusion in insulating oil is proposed. First, molecular dynamics simulations are performed to derive fitting equations for the diffusion coefficients of seven types of fault gases in insulating oil under varying temperatures, revealing microscopic diffusion mechanisms. Subsequently, a fluid-gas transport dynamics model is developed by coupling Fick's diffusion law with modified Navier-Stokes equations within a two-dimensional converter transformer structure. This model simulated hydrogen diffusion patterns under different fault locations. Results demonstrate that high-flow-rate zones exhibit wide-range gas distribution dominated by convective diffusion, while low-flow-rate areas show localized gas accumulation due to prominent Fick diffusion effects. Ultimately, by superimposing steady-state spatial concentration distributions of hydrogen released from six preset fault sources, regions with peak integrated gas concentrations are identified. This analysis determined five optimal monitoring points(P1~P5). This research provides theoretical support for multi-point collaborative DGA monitoring in transformers from the perspective of dynamic gas transport, significantly contributing to early detection capabilities for latent faults.

     

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