Acoustic behavior of electric drives is an important factor for their application in areas where humans expect certain comfort, such as in electric vehicles. The main problem addressed in this article is high tonality of noise emitted by conventional pulsewidth modulation (PWM) controlled drives. We propose to use an explicit model of the transfer function from the electric variables to the sound pressure with experimentally identified parameters. This model is then used to include acoustic quality into the cost function of the finite control set model predictive control (FCS-MPC). Specifically, we propose to penalize the sound pressure resonant frequencies in the spectrum of the direct flux component of the stator current vector. We study the impact of this penalization on acoustic properties of the drive measured by the sound pressure level and spectral flatness measure on three experimental induction machine (IM) drives of different power and acoustic environment. In comparison with the conventional PWM, random PWM, and FCS-MPC techniques, the proposed approach improves the tonality of the noise in terms of spectral flatness and in high-speed regimes it also improves total sound pressure level by several decibel.

中文翻译：

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IM驱动声噪声的预测控制

电驱动器的声学特性是其在人们期望一定舒适度的领域（例如电动汽车）中应用的重要因素。本文解决的主要问题是传统脉宽调制 (PWM) 控制的驱动器发出的高音调噪声。我们建议使用从电变量到具有实验确定参数的声压的传递函数的显式模型。然后使用该模型将声学质量包括到有限控制集模型预测控制 (FCS-MPC) 的成本函数中。具体来说，我们建议惩罚定子电流矢量的直接磁通分量的频谱中的声压共振频率。我们研究了这种惩罚对不同功率和声学环境的三个实验感应电机 (IM) 驱动器上的声压级和频谱平坦度测量的驱动器声学特性的影响。与传统的 PWM、随机 PWM 和 FCS-MPC 技术相比，所提出的方法在频谱平坦度方面改善了噪声的音调，并且在高速状态下，它还将总声压级提高了几个分贝。