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In practical engineering, there are few samples of arrester failures, and it is difficult for intelligent algorithms such as neural networks to make accurate judgments. To this end, a fault diagnosis method of arrester based on Bayesian network was proposed. Firstly, the principal component analysis was used to extract 21 characteristic parameters that affect the operation of arrester. And then the extracted characteristic parameters was chosen to establish two-layer information architecture defect diagnosis model. The classification probability of different categories was calculated according to the existing real-time data. If the first classification result indicated that the arrester is abnormal, new detection evidence was added for the second diagnosis. Finally, 6 arresters under the same voltage level in a certain area were selected to analyze and verify the validity and correctness of the proposed method.

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在实际工程中，避雷器缺陷的样本较少，利用神经网络之类的智能算法难以得出精准的判断。为此提出一种基于贝叶斯网络的避雷器缺陷诊断方法，该方法首先通过主成分分析法提取了21个影响避雷器运行的特征参量，随后考虑提取后的特征参量建立两层信息架构的缺陷诊断模型，根据已有的实时数据计算不同类别下的分类概率，若第一次分类结果表明避雷器异常，则补充新的检测证据进行二次诊断。最后选用某地区同一电压等级下的6台避雷器，分析和验证了所提方法的有效性和正确性。

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田雨鑫（1998-），男，硕士研究生，研究方向为电力设备智能运维，E-mail：1779276100@qq.com

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田雨鑫（1998-），男，硕士研究生，研究方向为电力设备智能运维，E-mail：1779276100@qq.com

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