一种基于动态权重优化的自适应多模糊逻辑变压器故障诊断模型
An adaptive multi-fuzzy logic model for diagnosing transformer faults using dynamic weight optimization
AI总结：
针对传统DGA方法诊断变压器故障存在的问题，提出自适应多模糊逻辑模型，结合多种DGA方法、模糊逻辑与动态权重调整机制，经MATLAB/Simulink验证，该模型显著提升诊断准确率，为变压器状态监测提供有力工具。
AI中文摘要：
溶解气体分析（DGA）对早期电力变压器故障诊断至关重要。传统DGA解释方法，如杜瓦尔三角、IEC比值、罗杰比值、多恩伯格比值和关键气体法，存在不一致性且准确率各异，尤其在多故障条件下。本文提出一种自适应多模糊逻辑（AMFL）模型，将多种DGA方法与模糊逻辑及动态权重调整机制相结合。该模型能迭代评估各方法诊断性能，识别多种故障类型，并根据故障预测准确率调整权重。基于反馈的优化在每个周期后重新校准权重以确保最优解收敛。在MATLAB/Simulink中实现该模型，并针对已知误差条件的DGA数据集进行验证。结果表明，AMFL模型显著提高了诊断准确率，尤其在复杂误差场景中，并增强了对新数据集的适应性。对比分析表明，该方法在误差检测的准确性、一致性和可靠性方面优于传统固定权重多模糊系统。这项工作为变压器状态监测提供了一个强大、灵活的诊断工具，并支持更准确的资产管理决策。
英文摘要：
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.
