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电动汽车驱动 ★ 5.0

一种考虑逆变器非线性的牵引电机匝间短路故障混合模型磁链估计器

A Hybrid Model Flux Estimator for Diagnosis of Inter-Turn Short Circuit Fault in Traction Motors Considering Inverter Nonlinearity

作者 Lisong He · Jinsong Kang · Dongliang Ke · Fengxiang Wang
期刊 IEEE Transactions on Industry Applications
出版日期 2025年4月
技术分类 电动汽车驱动
相关度评分 ★★★★★ 5.0 / 5.0
关键词 定子匝间短路故障 牵引电机 故障诊断方法 电压 - 电流混合模型磁通估计器 仿真与实验
语言:

中文摘要

定子匝间短路(ITSC)故障是牵引电机遇到的最严重故障之一,会显著降低高速列车的功率输出。本文提出一种利用电压 - 电流混合模型磁链估计器检测牵引电机ITSC故障的有效故障诊断方法。故障特征信号通过磁链估计器内的PI模块获得,该模块专门用于补偿与纯积分以及低速时定子电阻测量相关的磁链误差。首先,建立了ITSC故障下感应电机的数学模型,并提出了一种基于无差拍定子电流观测器的高精度电压 - 电流混合磁链估计器。其次,分析了ITSC故障和逆变器非线性特性对故障特征信号的影响。此外,评估了电机中各种参数不匹配对故障特征信号的影响。最后,通过在不同运行条件下进行的仿真和实验验证了所提出的故障诊断方法的有效性。

English Abstract

Stator inter-turn short circuit (ITSC) faults are among the most severe malfunctions encountered in traction motors, significantly compromising the power output of high-speed trains. This paper presents an effective fault diagnosis method for detecting ITSC faults in traction motors, utilizing a voltage-current hybrid model flux estimator. The fault characterization signal is obtained using the PI module within the flux estimator, specifically designed to compensate for flux errors associated with pure integration and stator resistance measurements at low speeds. Firstly, a mathematical model of the induction motor under ITSC faults is established, and a high-precision voltage-current hybrid flux estimator based on deadbeat stator current observer is proposed. Secondly, the impact of ITSC faults and the characteristics of the inverter nonlinearity on the fault characterization signal is analyzed. Furthermore, an assessment is conducted on the influence of various parameter mismatches in the motor on the fault characterization signal. Finally, the efficacy of the proposed fault diagnosis method is validated through simulations and experiments conducted under diverse operating conditions.
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SunView 深度解读

该混合模型磁链估计技术对阳光电源电动汽车驱动系统及储能变流器产品具有重要应用价值。文中提出的逆变器非线性补偿方法可直接应用于车载OBC充电机和电机驱动控制器,通过改进磁链观测器提升死区效应、开关延迟等非理想因素下的故障诊断精度。该技术可集成至ST系列储能变流器的智能运维模块,实现电机类负载的匝间短路早期预警,增强系统可靠性。结合iSolarCloud云平台的预测性维护功能,可构建基于混合模型的电机健康管理系统,降低突发故障风险,提升新能源汽车动力系统与储能系统的运行安全性与服务寿命,具有显著的工程应用价值。