Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing
Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing
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DOI：
10.1109/tdei.2018.006766
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发表时间：
2018-04
影响因子：
3.1
通讯作者：
J. Aizpurua;V. Catterson;B. Stewart;S. Mcarthur;B. Lambert;Bismark Ampofo;Gavin Pereira;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
电力变压器的准确诊断对于电网的可靠和经济运行至关重要。目前，基于油中溶解气体分析的Transformer故障诊断方法和分析模型很多。然而，这些方法给出相互矛盾的结果，并且它们不能生成与诊断结果相关联的不确定性信息。在这种情况下，并不总是清楚哪个模型是最准确的。提出了一种基于贝叶斯网络和假设检验的溶解气体多类概率诊断框架。贝叶斯网络模型嵌入专家知识，从数据中学习模式，并推断与诊断结果相关的不确定性，假设检验有助于数据选择过程。使用IEC TC 10数据集验证了所提出的框架的有效性，并显示出具有88.9%的最大诊断准确率。
Accurate diagnosis of power transformers is critical for the reliable and cost-effective operation of the power grid. Presently there are a range of methods and analytical models for transformer fault diagnosis based on dissolved gas analysis. However, these methods give conflicting results and they are not able to generate uncertainty information associated with the diagnostics outcome. In this situation it is not always clear which model is the most accurate. This paper presents a novel multiclass probabilistic diagnosis framework for dissolved gas analysis based on Bayesian networks and hypothesis testing. Bayesian network models embed expert knowledge, learn patterns from data and infer the uncertainty associated with the diagnostics outcome, and hypothesis testing aids in the data selection process. The effectiveness of the proposed framework is validated using the IEC TC 10 dataset and is shown to have a maximum diagnosis accuracy of 88.9%.