Transformer defects are defined by their severity which is the intrinsic property of the transformer. Several approaches for identifying the severity of Power Transformer (PT) problems have previously been proposed; however, most published research does not incorporate Gas Level (GL), Gas Rate (GR), and DGA interpretation into a unified strategy. A novel technique in the form of fuzzy logic (FL) has been offered as a new way to assess faults’ severity by utilizing the combination of GL, GR, and DGA interpretation from the Duval Pentagon Method (DPM) to increase the reliability of the faults’ severity evaluation of PT. Based on the local population, a four-level typical concentration and rate were created. A Deep Learning (DL) oriented Convolutional Neural Network (CNN) based DPM and Harris Hawks Optimization (HHO) method with a high agreement to that same graphical DPM has also been devised to enable the evaluation of hundreds of PT information easy. The proposed method was applied to 448 PTs, and it was then used to assess the severity of problems in PTs using historical DGA data. Due to the integration of GL, GR, and DGA interpretation results in one technique, this novel strategy yields good agreement with earlier methods, but with better sensitivity.

变压器缺陷是根据其严重程度来定义的，这是变压器的固有属性。之前已经提出了几种确定电力变压器 (PT) 问题严重程度的方法；然而，大多数已发表的研究并未将 Gas Level (GL)、Gas Rate (GR) 和 DGA 解释纳入统一的策略中。模糊逻辑 (FL) 形式的新技术作为评估故障严重性的新方法，通过结合杜瓦尔五角大楼方法 (DPM) 中的 GL、GR 和 DGA 解释来提高故障的可靠性。 PT的故障严重程度评价。根据当地人口，制定了四级典型浓度和比率。还设计了一种基于深度学习 (DL) 的卷积神经网络 (CNN) 的 DPM 和 Harris Hawks 优化 (HHO) 方法，与相同的图形 DPM 高度一致，可以轻松评估数百个 PT 信息。所提出的方法应用于 448 个 PT，然后使用历史 DGA 数据评估 PT 问题的严重性。由于将 GL、GR 和 DGA 解释结果集成在一种技术中，这种新颖的策略与早期方法具有良好的一致性，但具有更好的灵敏度。