上海交通大学学报

上海交通大学学报

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基于知识推理的换流变压器可解释性故障诊断方法研究

  

  1. 1. 西安交通大学 电气工程学院，西安710049；

    2. 广西大学 电气工程学院，南宁530004；

    3. 国网江苏省电力公司 电力科学研究院，南京211100；

    4. 国家电网有限公司，北京，100031

  • 作者简介:李元（1984—），副教授，博士生导师，从事电力设备状态感知与智能预警方面研究
  • 基金资助:
    国家自然科学基金（52477159）资助项目

Study on Interpretable Fault Diagnostic Method of Converter Transformer Based on Knowledge-based Reasoning Approach

  1. 1. School of Electrical Engineering, Xi’an Jiaotong University, 710049, Shaanxi, China；

    2. School of Electrical Engineering, GuangXi University, 530004, Guangxi, China；

    3. Electric Power Research Institute, State Grid Jiangsu Electric Power Co., Ltd., 211100, Jiangsu, China；

    4. State Grid Corporation of China, 100031, Beijing, China

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摘要： 针对换流变压器常用故障诊断和评估技术在故障机理不明、故障数据缺失条件下应用效果不佳的问题，提出一种基于知识推理的换流变压器可解释性故障诊断方法。首先，基于扎根理论建立故障特征框架，将历史故障案例的文本信息有机重构为结构化数据；然后，利用灰色关联分析实现换流变压器关键故障特征优选，采用因果特征分离的先验算法提取换流变压器故障诊断知识规则，并构建知识图谱，提高换流变压器故障诊断机理与过程的可解释性。采用实际换流变压器故障数据检验方法的有效性，结果表明，该方法诊断准确率达78.26%，能够为换流变压器故障诊断和状态评价提供准确可靠且具有可解释性的决策支持，对换流变压器的现场试验与检修具有指导意义。

关键词: 换流变压器, 规则提取, 扎根理论, 知识推理, 故障诊断

Abstract: To address the limitations of conventional fault diagnosis and evaluation techniques for converter transformers under conditions of unclear fault mechanisms and insufficient fault data, this paper proposes an interpretable fault diagnosis method for converter transformers based on knowledge reasoning. Firstly, grounded theory is employed to establish a fault characteristic framework, thereby recon-structing textual information from historical fault cases into structured data. Subsequently, grey relational analysis is utilized to optimize the selection of key fault characteristics, and a causal feature separation-based Apriori algorithm is applied to extract fault diagnosis knowledge rules. A fault diagnosis knowledge graph is constructed to enhance the interpretability of the fault diagnosis mechanism and process. The effectiveness of the proposed method is validated using actual converter transformer fault data. The results demonstrate that the method achieves a diagnostic accuracy of 78.26%, providing accurate, reliable, and interpretable decision support for converter transformer fault diagnosis and condition assessment. This approach holds significant practical value for guiding field testing and maintenance of converter transformers.

Key words: Converter transformer, Rule extraction, Grounded Theory, Multi-source information characteristics, Fault diagnosis

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