结合IEEE关键气体法的优化模糊逻辑方法，用于通过溶解气体分析诊断电力变压器故障
Optimized Fuzzy Logic Approach with the IEEE Key Gas Method for Diagnosing Power Transformer Faults Using Dissolved Gas Analysis
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中文总结 AI 辅助
本研究提出结合模糊逻辑与IEEE关键气体法的FL-KGM模型，优化隶属度函数与规则集并分离CO和CO₂，经真实数据集验证准确率达98.6%，可提升电力变压器故障诊断效果。
中文摘要 AI 辅助
可靠的变压器故障诊断对维持电力系统稳定性至关重要。溶解气体分析（DGA）中广泛应用的IEEE关键气体法（KGM）在处理模糊数据和确保高诊断准确率方面存在局限。本研究提出一种结合模糊逻辑与IEEE关键气体法的增强模型（FL-KGM），该模型引入了优化的隶属度函数、经优化的模糊规则集，并对CO和CO₂进行了新颖的分离以消除诊断不一致性。FL-KGM利用多维气体比率分析和自适应分类框架，实现了更优的故障识别与分类。基于真实世界数据集的实验验证表明，FL-KGM的准确率高达98.6%，显著优于KGM及其他基于FL的方法。这些研究结果阐明了FL-KGM在推进变压器监测、实现智能故障检测以及增强现代电力系统预测性维护策略方面的潜力。
英文摘要
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 and ensuring high diagnostic accuracy. This study presents An enhanced model combining Fuzzy Logic with the IEEE Key Gas Method (FL-KGM) that introduces refined membership functions, optimized fuzzy rule sets, and a novel separation of CO and CO2 to eliminate diagnostic inconsistencies. By leveraging multidimensional gas ratio analysis and an adaptive classification framework, FL-KGM delivers superior fault identification and classification. Experimental validation utilizing real-world datasets demonstrates that FL-KGM achieves up to 98.6% accuracy, significantly outperforming KGM and other FL-based approaches. These findings elucidate the potential of FL-KGM in advancing transformer monitoring, enabling intelligent fault detection, and enhancing predictive maintenance strategies in modern power systems.
