Lee Hojin, Jeong Hyeyun, Kim Seongyun, Kim Sang Woo
Department of Electrical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang 37673, Korea.
Sensors (Basel). 2022 Jun 20;22(12):4639. doi: 10.3390/s22124639.
This study presents a novel interturn short-circuit fault (ISCF) and demagnetization fault (DF) diagnosis strategy based on a self-attention-based severity estimation network (SASEN). We analyze the effects of the ISCF and DF in a permanent-magnet synchronous machine and select appropriate inputs for estimating the fault severities, i.e., a positive-sequence voltage and current and negative-sequence voltage and current. The chosen inputs are fed into the SASEN to estimate fault indicators for quantifying the fault severities of the ISCF and DF. The SASEN comprises an encoder and decoder based on a self-attention module. The self-attention mechanism enhances the high-dimensional feature extraction and regression ability of the network by concentrating on specific sequence representations, thereby supporting the estimation of the fault severities. The proposed strategy can diagnose a hybrid fault in which the ISCF and DF occur simultaneously and does not require the exact model and parameters essential for the existing method for estimating the fault severity. The effectiveness and feasibility of the proposed fault diagnosis strategy are demonstrated through experimental results based on various fault cases and load torque conditions.
本研究提出了一种基于自注意力严重度估计网络（SASEN）的新型匝间短路故障（ISCF）和去磁故障（DF）诊断策略。我们分析了永磁同步电机中ISCF和DF的影响，并选择合适的输入量来估计故障严重程度，即正序电压和电流以及负序电压和电流。将所选输入量输入到SASEN中，以估计用于量化ISCF和DF故障严重程度的故障指标。SASEN由基于自注意力模块的编码器和解码器组成。自注意力机制通过专注于特定的序列表示来增强网络的高维特征提取和回归能力，从而支持对故障严重程度的估计。所提出的策略可以诊断ISCF和DF同时发生的混合故障，并且不需要现有故障严重程度估计方法所必需的精确模型和参数。通过基于各种故障情况和负载转矩条件的实验结果，证明了所提出的故障诊断策略的有效性和可行性。
