Interpretable Knowledge Graph Reasoning Method for Fault Traceability in Converter Transformer

WU Wanhao; WANG Jiawen; ZHANG Yonghao; WU Hongbin; LI Qingquan; REN Fuqiang

doi:10.13296/j.1001-1609.hva.2026.09.018

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Interpretable Knowledge Graph Reasoning Method for Fault Traceability in Converter Transformer

Technology for Fault Identification, Prediction and Protection of Power Equipment | 更新时间：2026-09-10

- Interpretable Knowledge Graph Reasoning Method for Fault Traceability in Converter Transformer
- High Voltage Apparatus Vol. 62, Issue 9, Pages: 179-185,198(2026)
- 作者机构：
  
  山东大学特高压输变电技术与装备山东省重点实验室，济南 250061
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2026.09.018
  CLC：
- Received：02 April 2026，
  
  Revised：2026-06-28，
  
  Published：16 September 2026
- 稿件说明：
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WU Wanhao, WANG Jiawen, ZHANG Yonghao, et al. Interpretable Knowledge Graph Reasoning Method for Fault Traceability in Converter Transformer[J]. High Voltage Apparatus, 2026, 62(9): 179-185,198.
DOI：

WU Wanhao, WANG Jiawen, ZHANG Yonghao, et al. Interpretable Knowledge Graph Reasoning Method for Fault Traceability in Converter Transformer[J]. High Voltage Apparatus, 2026, 62(9): 179-185,198. DOI： 10.13296/j.1001-1609.hva.2026.09.018.

摘要

换流变压器是高压直流输电系统的关键设备，其故障演化过程具有非线性和强耦合特征。针对运维文本数据利用不足和诊断过程缺乏可解释性的问题，提出一种可解释知识图谱推理方法（IKGR）。首先，采用ALBERT-BiLSTM-CRF从检修记录和故障报告等非结构化文本中抽取实体，并依据图谱模式层和物理因果约束组织关系三元组，构建换流变压器故障溯源知识图谱。其次，设计双智能体强化学习推理模型，引入软奖励机制，并利用短期同策略轨迹缓冲区同步更新双智能体参数，实现多跳故障溯源。实验结果表明， IKGR的根因排序指标

Hits

@10达到94.8%，并可生成包含设备部件、故障状态、保护动作和试验方法的推理路径，为换流变压器故障处置和运维决策提供路径级解释。

Abstract

Converter transformer is a critical component of high-voltage direct-current(HVDC)transmission systems

and its fault evolution exhibits nonlinear and strongly coupled characteristics. To address the problems of underutilized operation and maintenance text data and the lack of interpretability in the diagnostic process

an interpretable knowledge graph reasoning method(IKGR)is proposed. First

an ALBERT-BiLSTM-CRF model is adopted to extract entities from such unstructured text as maintenance records and fault reports. The relational triples are organized in accordance with the schema layer of the knowledge graph and physical causal constraints

thereby constructing a converter transformer fault traceability knowledge graph. Then

a dual-agent reinforcement learning reasoning model is designed

in which a soft reward mechanism is introduced and a short-term on-policy trajectory buffer is utilized to synchronously update the par

ameters of the dual agents

thereby achieving multi-hop fault traceability. Experimental results show that IKGR achieves 94.8% in terms of the root cause ranking meric

Hits

@10

and is capable of generating reasoning paths comprising equipment components

fault status

protective actions and testing methods

thereby providing path-level explanations for converter transformer fault handling and O

M decision-making.

关键词

Keywords

references

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