【目的】永磁同步电机(PMSM)因其功率因数高、结构简单和动态性能好等优势在风力发电、电动汽车领域得到广泛应用。然而，PMSM在运行过程中由于驱动器损坏、定子绕组接线松动等原因可能会导致缺相故障，PMSM缺相运行时会产生噪声和振动，导致输出功率降低，并且长时间缺相运行会损坏电气设备，因此对其进行准确的故障诊断对于保障设备的正常运行至关重要。【方法】本文提出了一种基于改进经验小波变换(IEWT)和分类提升(CatBoost)算法的故障诊断策略，并将其应用于六相PMSM缺相故障诊断。首先，介绍了IEWT算法的基本原理，IEWT算法在Welch功率谱曲线上进行频谱分割，相较于经验小波变换(EWT)算法，能有效抑制模态混叠；然后，对PMSM故障信号进行IEWT分解得到各个模态分量，利用各个模态的能量矩表征故障信号，构建故障分类数据集；最后，基于算数优化算法，取数据集的80%作为训练集对CatBoost算法进行超参数调优，选择合适的超参数构建CatBoost故障分类模型，并与其他传统分类模型进行对比。【结果】试验结果表明，本文所提基于IEWT和CatBoost算法的故障诊断策略有效抑制了传统EWT算法中因主频附近旁瓣过大导致的错误分段现象，提高了故障分类的准确率。与传统分类模型相比，CatBoost多分类模型能够更加准确地识别故障类别，且在每种故障类别上的分类性能表现均衡，泛化能力更强，在不同的故障工况下均有良好表现。【结论】试验结果验证了本文所提故障诊断策略在六相PMSM缺相故障诊断上的可行性和有效性，为后续六相PMSM容错控制以及综合故障检测技术提供了支撑。

[Objective] Permanent magnet synchronous motor (PMSM) is widely used in wind power generation and electric vehicles due to its high power factor, simple structure and good dynamic performance. However, PMSM may experience phase loss faults during operation due to reasons such as drive failure or loose stator winding connections. When operating with phase loss, the PMSM generates noise and vibration, leading to a reduction in output power. Prolonged phase loss operation can also damage electrical equipment, making accurate fault diagnosis crucial for ensuring the normal operation of the equipment. [Methods] This paper proposed a fault diagnosis strategy based on an improved empirical wavelet transform (IEWT) and categorical boosting (CatBoost) algorithm, and applied it to the phase loss fault diagnosis of six-phase PMSM. First, the basic principle of the IEWT algorithm was introduced. The IEWT algorithm performed spectral segmentation on the Welch power spectrum, effectively suppressing modal aliasing compared

国网上海市电力公司科技项目资助(5209KZ230005)