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English(EN) Optimized Fuzzy Logic Approach with the IEEE Key Gas Method for Diagnosing Power Transformer Faults Using Dissolved Gas Analysis

新型模糊逻辑模型提升变压器故障诊断准确性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种结合模糊逻辑和IEEE关键气体法（FL-KGM）的增强模型，以提高通过溶解气体分析诊断电力变压器故障的准确性。这种新方法改进了隶属函数，优化了模糊规则集，并分离了CO和CO2，以解决传统IEEE关键气体法中发现的不一致之处。实际数据验证显示，FL-KGM的准确率高达98.6%，显著优于现有方法，并为先进的变压器监测和预测性维护提供了潜力。 AI

影响 这项研究可能通过改进AI驱动的故障检测，从而提高电网维护的可靠性。

排序理由 详细介绍新方法和实验验证的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

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新型模糊逻辑模型提升变压器故障诊断准确性

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详细介绍新方法和实验验证的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kim-Anh Nguyen, Huy Hoang Le, Ba Tu Phung ·

    基于IEEE关键气体法的优化模糊逻辑方法用于溶解气体分析诊断电力变压器故障

    arXiv:2608.18133v1 Announce Type: new Abstract: Reliable transformer fault diagnosis is essential for maintaining power system stability. The IEEE Key Gas Method (KGM), a widely utilized approach in Dissolved Gas Analysis (DGA), exhibits limitations in addressing ambiguous data a…

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