Abstract Dissolved gas analysis (DGA) is used to diagnose power transformer fault based on the concentration of dissolved gases and the ratios between them. These gases are generated in oils as a result of electrical and thermal stresses, but these DGA techniques cannot identify the severity of the fault types. In IEEE Standard C57.104, the maintenance action is taken based on the total dissolved combustible gases, which is not sufficient because it ignores the importance of the gas type and its change rate. Thermodynamic theory using different starting decomposing materials, namely, n-octane (C8H18) and eicosane (C20H42), is used to estimate the severity of transformer fault types. Two scenarios are suggested with different data transformation techniques to enhance neural pattern-recognition (NPR) method accuracy for predicting transformer fault types and their severity classes. The proposed scenarios are built based on 446 samples collected from the laboratory and literature. Results refer to the role of the starting decomposing material on the severity of the transformer fault and illustrate that the proposed model has a higher accuracy (92.8%) compared with other DGA methods for diagnosing transformer fault types and superior accuracy (99.1%) to predict fault severity class.

中文翻译：

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基于神经模式识别技术的变压器故障类型和严重性等级预测

摘要 溶解气体分析(DGA)是根据溶解气体的浓度及其比值来诊断电力变压器故障的方法。由于电应力和热应力，油中会产生这些气体，但这些 DGA 技术无法确定故障类型的严重程度。在 IEEE 标准 C57.104 中，根据总溶解的可燃气体采取维护措施，这是不够的，因为它忽略了气体类型及其变化率的重要性。使用不同起始分解材料的热力学理论，即正辛烷 (C8H18) 和二十烷 (C20H42)，用于估计变压器故障类型的严重程度。建议使用不同的数据转换技术来提高神经模式识别 (NPR) 方法的准确性，以预测变压器故障类型及其严重性等级的两种情况。提议的情景是基于从实验室和文献中收集的 446 个样本构建的。结果参考了起始分解材料对变压器故障严重程度的作用，并说明与其他 DGA 方法相比，所提出的模型具有更高的准确度（92.8%），用于诊断变压器故障类型和预测准确度（99.1%）故障严重等级。