用优化模糊逻辑提升变压器故障诊断准确率，最高达98.6%。
Optimized Fuzzy Logic Approach with the IEEE Key Gas Method for Diagnosing Power Transformer Faults Using Dissolved Gas Analysis
- 融合模糊逻辑与IEEE关键气体法，优化隶属函数和规则集
- 在真实数据上实现98.6%诊断准确率，优于传统方法
- 适合电力系统智能运维与预测性维护场景
可靠的变压器故障诊断对维持电力系统稳定至关重要。广泛使用的溶解气体分析（DGA）中的IEEE关键气体法（KGM）存在数据模糊和诊断精度不足的问题。本文提出一种增强模型——模糊逻辑结合的IEEE关键气体法（FL-KGM），引入优化的隶属函数、改进的模糊规则集，并创新性地分离CO与CO2以消除诊断矛盾。通过多维气体比值分析与自适应分类框架，FL-KGM显著提升故障识别与分类能力。基于真实世界数据集的实验验证表明，FL-KGM最高可达98.6%的准确率，显著优于KGM及其他基于模糊逻辑的方法。结果表明，FL-KGM在推动变压器监测智能化、实现故障预警与提升现代电力系统预测性维护策略方面具有重要潜力。
原文摘要 · Abstract (English)
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.
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