动态调整权重的模糊模型提升变压器故障诊断准确率
An adaptive multi-fuzzy logic model for diagnosing transformer faults using dynamic weight optimization
- 融合多种气体分析法，通过动态权重优化实现自适应诊断
- 在复杂故障场景下准确率显著提升，较传统方法更稳定可靠
- 适合电力系统运维人员用于设备状态监测与决策支持
溶解气体分析（DGA）对早期发现变压器故障至关重要。传统DGA解读方法如杜瓦三角、IEC比值、罗杰比值、多宁堡比值和关键气体法存在不一致且准确率波动大，尤其在多重故障条件下表现不佳。本文提出一种自适应多模糊逻辑（AMFL）模型，集成多种DGA方法，结合模糊逻辑与动态权重调整机制。不同于固定权重方法，该系统可迭代评估各方法诊断性能，识别多重故障类型，并根据预测准确率动态调整权重。基于反馈的优化机制在每轮后重新校准权重，确保最优解收敛。模型在MATLAB/Simulink中实现，并在含已知误差条件的DGA数据集上验证。结果表明，AMFL在复杂误诊场景下诊断准确率显著提高，且对新数据集具有更强适应性。对比分析显示，该方法在准确性、一致性与可靠性方面均优于传统固定权重多模糊系统。本研究为变压器状态监测提供了一种鲁棒、灵活的诊断工具，有助于提升资产管理水平。
原文摘要 · Abstract (English)
Dissolved gas analysis (DGA) is crucial for diagnosing early power transformer failures. Traditional DGA interpretation methods like Duval Triangle, IEC ratio, Roger ratio, Doernenburg ratio and Key Gas are inconsistent and vary in accuracy, especially for multiple fault conditions. We propose an Adaptive Multi-Fuzzy Logic (AMFL) model integrating multiple DGA methods with fuzzy logic and a dynamic weight adjustment mechanism. Unlike existing approaches with fixed weights, this system iteratively evaluates each method's diagnostic performance, identifies multiple fault types, and adjusts weights based on fault prediction accuracy. A feedback-based optimization recalibrates weights after each cycle to ensure optimal solution convergence. The model, implemented in MATLAB/Simulink, is validated against DGA datasets with known error conditions. Results show the AMFL model significantly improves diagnostic accuracy, especially in complex error scenarios, and enhances adaptability to new datasets. Comparative analysis demonstrates the proposed method outperforms traditional fixed weight multi-fuzzy systems in accuracy, consistency, and reliability of error detection. This work provides a robust, flexible diagnostic tool for transformer condition monitoring and supports more accurate asset management decisions.
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