Improving the accuracy of transformer DGA diagnosis in the presence of conflicting evidence
Improving the accuracy of transformer DGA diagnosis in the presence of conflicting evidence
复制标题
在存在相互矛盾的证据的情况下提高变压器 DGA 诊断的准确性
DOI：
10.1109/eic.2017.8004698
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
J. Cross
中科院分区：
文献类型：
--
作者：
J. Aizpurua;V. Catterson;B. Stewart;S. Mcarthur;B. Lambert;Bismark Ampofo;Gavin Pereira;J. Cross
变压器是电网可靠、经济运行的关键资产。如果状态监测不能及时发现劣化状态，变压器可能会发生故障。溶解气体分析(DGA)是对变压器油中溶解气体的检测，根据分析变压器油中溶解气体含量的不同，存在不同的变压器故障诊断方法。然而，这些方法可能会给出相互矛盾的结果，而且在给定的情况下，并不总是清楚哪种模型最准确。提出了一种基于贝叶斯网络的分布式遗传算法证据组合框架。贝叶斯网络模型嵌入了专家知识以及学习的数据模式和证据组合，这有助于分析的一致性。利用IEC TC 10数据集验证了该框架的有效性，最高诊断正确率为88.3%。
Transformers are critical assets for the reliable and cost-effective operation of the power grid. Transformers may fail if condition monitoring does not identify degraded conditions in time. Dissolved Gas Analysis (DGA) focuses on the examination of the dissolved gasses in the transformer oil and there exist different methods for transformer fault diagnosis based on different analyses of the gassing levels. However, these methods can give conflicting results, and it is not always clear which model is most accurate in a given situation. This paper presents a novel evidence combination framework for DGA based on Bayesian networks. Bayesian network models embed expert knowledge along with learned data patterns and evidence combination which aids in the consistency of analysis. The effectiveness of the proposed framework is validated using the IEC TC 10 dataset with a maximum diagnosis accuracy of 88.3%.