基于RVM的双特征融合变压器铁心剩磁辨识方法研究

彭乃勇; 孙振兴; 翟雪松; 马涛; 杨海涛

doi:10.14044/j.1674-1757.pcrpc.2026.03.007

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基于RVM的双特征融合变压器铁心剩磁辨识方法研究

电力电容器与无功补偿 | 系统应用研究

基于RVM的双特征融合变压器铁心剩磁辨识方法研究
Research on Identification Method of Transformer Core Remanence Based on Dual-feature Fusion Using SVM
国网安徽电力有限公司淮北供电公司，安徽淮北 235000
国网安徽电力有限公司电力科学研究院，合肥 230092
2026年47卷第3期页码：67-74
DOI：10.14044/j.1674-1757.pcrpc.2026.03.007
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彭乃勇, 孙振兴, 翟雪松, 等. 基于RVM的双特征融合变压器铁心剩磁辨识方法研究[J]. 电力电容器与无功补偿, 2026,47(3):67-74.

PENG Naiyong, SUN Zhenxing, ZHAI Xuesong, et al. Research on Identification Method of Transformer Core Remanence Based on Dual-feature Fusion Using SVM[J]. Power Capacitor & Reactive Power Compensation, 2026, 47(3): 67-74.
彭乃勇, 孙振兴, 翟雪松, 等. 基于RVM的双特征融合变压器铁心剩磁辨识方法研究[J]. 电力电容器与无功补偿, 2026,47(3):67-74. DOI： 10.14044/j.1674-1757.pcrpc.2026.03.007.

PENG Naiyong, SUN Zhenxing, ZHAI Xuesong, et al. Research on Identification Method of Transformer Core Remanence Based on Dual-feature Fusion Using SVM[J]. Power Capacitor & Reactive Power Compensation, 2026, 47(3): 67-74. DOI： 10.14044/j.1674-1757.pcrpc.2026.03.007.

摘要

铁心剩磁作为诱发变压器励磁涌流的关键内因，工程中受制于非线性磁滞与合闸相位的强耦合作用面临在线识别精度不足的难题。考虑到传统方法在特征表征维度上的欠缺，本文选取合闸相位与励磁涌流峰双特征量，提出一种基于相关向量机（relevance vector machine，RVM）的剩磁辨识方案。研究以Jiles-Atherton（J-A）动态磁滞理论为基础，在COMSOL多物理场平台中建立单相变压器场路耦合模型，通过直流脉冲注入法产生不同初始剩磁状态，并在铁心中心区域设置磁通探针获取稳态剩磁数据，构建剩磁与双特征变量之间的非线性映射并验证模型的物理一致性。在此基础上构建稀疏化RVM回归模型，依托贝叶斯框架下的自动相关性判别机制实现参数自适应调整，减少模型对人工参数调节的依赖。仿真结果表明该方法在剩磁反演中具有较高辨识精度，模型决定系数

达到0.998，表明其在复杂非线性关系建模与剩磁精确辨识方面具备良好的有效性。

Abstract

Strong coupling between nonlinear hystersis and the closing phase，together with the core fremanence as a key internal cause of transformer magnetizing inrush current，leads to the challenge of insufficient online identification accuracy in engineering. Condiering the shortcomings of traditional method in feature characterization，in this paper the dual features of closing phase angle and inrush current peak are selected，and a remanence identification scheme based on Relevance Vector Machine（RVM）is proposed. Based on Jiles-Atherton dynamic hysteresis theory，a single-phase transformer field-circuit coupled model is set up in COMSOL Multiphysics. Different initial remanence states are generated through DC pulse injection，and steady-state remanence data are obtained by setting a flux probe in the core center. The nonlinear mapping between remanence and dual features is constructed with verified physical consistency. On this basis，a sparse RVM regression model is constructed，realizing adaptive parameter adjustment via Bayesian automatic relevance determination（ARD）to reduce manual tuning dependency. Simulation results show that this method exhibits high accuracy in remanence inversion，achieving a coefficient of determination

is up to 0.998，which confirms its excellent effectiveness in capturing complex nonlinear relationships and enabling precise remanence identification.

关键词

Keywords

references

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