A fault cause identification method for HVDC transmission systems based on control response decoupling and adaptive multi-source information fusion

SUN Kezhen; XIAO Taiyu; YU Bing; OUYANG Jinxin; SUN Jiaqi; CHEN Qidi; YANG Yaming

doi:10.19783/j.cnki.pspc.260248

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A fault cause identification method for HVDC transmission systems based on control response decoupling and adaptive multi-source information fusion

更新时间：2026-09-28

- A fault cause identification method for HVDC transmission systems based on control response decoupling and adaptive multi-source information fusion
- Power System Protection and Control Vol. 54, Issue 19, (2026)
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- DOI：10.19783/j.cnki.pspc.260248
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- Published：2026
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SUN Kezhen, XIAO Taiyu, YU Bing, et al. A fault cause identification method for HVDC transmission systems based on control response decoupling and adaptive multi-source information fusion[J]. Power System Protection and Control, 2026, 54(19).
DOI：

SUN Kezhen, XIAO Taiyu, YU Bing, et al. A fault cause identification method for HVDC transmission systems based on control response decoupling and adaptive multi-source information fusion[J]. Power System Protection and Control, 2026, 54(19). DOI： 10.19783/j.cnki.pspc.260248.

摘要

针对高压直流输电线路故障电气量受换流站控制系统非线性重塑影响，导致暂态特征呈现同质化且难以精确辨识的难题，提出了一种基于控制响应解耦与多源信息自适应融合的故障原因辨识方法。首先，分析了雷击、山火、风偏、异物诱因下直流线路故障暂态特征的演化差异，揭示了故障特征同质化的成因。其次，通过构建考虑触发角动态演变的解析模型，提出了计及控制响应的直流电压和电流故障固有特征量提取方法。进一步，构建了融合卷积神经网络(convolutional neural network

CNN)、双向长短期记忆(bidirectional long short-term memory

BiLSTM)网络与空间注意力机制(spatial attention mechanism

SAM)的辨识模型，用以挖掘故障固有电气量的多尺度空间细节与双向全局时序演化特征。在此基础上，引入基于气象环境先验的贝叶斯概率模型，并提出基于JS散度的多源信息动态融合策略，实现了数据驱动特征与外部物理约束的自适应权重协同。算例分析表明，所提方法能够针对各种故障原因实现高精度辨识，成功解决了特征同质化严重的辨识难题。

Abstract

To address the difficulty of accurately identifying the causes of HVDC transmission line faults due to the homogenization of transient features caused by the nonlinear reshaping of electrical quantities by converter-station control systems

a fault cause identification method is proposed based on control response decoupling and adaptive multi-source information fusion. First

the differences in the evolution transient characteristics of DC line faults caused by lightning strikes

wildfires

wind-induced conductor deviations

and foreign objects are analyzed

and the underlying causes of fault characteristic homogenization are revealed. Second

an analytical model is constructed considering the dynamic evolution of the firing angle. An extraction method for inherent fault characteristic quantities of DC voltage and current while considering the control response is proposed. Furthermore

an identification model is constructed by fusing a convolutional neural network (CNN)

a bidirectional long short-term memory (BiLSTM) network

and a spatial attention mechanism (SAM). Multi-scale spatial details and bidirectional global temporal evolution characteristics of the inherent fault electrical quantities are mined by the constructed model. On this basis

a Bayesian probability model is introduced based on meteorological environment priors

and a dynamic fusion strategy of multi-source information is proposed based on Jensen-Shannon divergence to achieve adaptive weight coordination between data-driven features and external physical constraints. Case studies demonstrate that the proposed method achieves high-precision identification for different fault causes

effectively addressing the challenge of fault cause identification under severe feature homogenization.

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references

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