Department of Electrical Power Engineering, Faculty of Engineering, Cairo University, Giza 12613, Egypt.
Electronic and Electrical Engineering Department, College of Engineering, Design and Physical Sciences, Brunel University London, London UB8 3PH, UK.
Sensors (Basel). 2022 Jan 4;22(1):365. doi: 10.3390/s22010365.
This paper introduces a novel online adaptive protection scheme to detect and diagnose broken bar faults (BBFs) in induction motors during steady-state conditions based on an analytical approach. The proposed scheme can detect precisely adjacent and non-adjacent BBFs in their incipient phases under different inertia, variable loading conditions, and noisy environments. The main idea of the proposed scheme is monitoring the variation in the phase angle of the main sideband frequency components by applying Fast Fourier Transform to only one phase of the stator current. The scheme does not need any predetermined settings but only one of the stator current signals during the commissioning phase. The threshold value is calculated adaptively to discriminate between healthy and faulty cases. Besides, an index is proposed to designate the fault severity. The performance of this scheme is verified using two simulated motors with different designs by applying the finite element method in addition to a real experimental dataset. The results show that the proposed scheme can effectively detect half, one, two, or three broken bars in adjacent/non-adjacent versions and also estimate their severity under different operating conditions and in a noisy environment, with accuracy reaching 100% independently from motor parameters.
本文提出了一种新颖的在线自适应保护方案，用于在稳态条件下基于解析方法检测和诊断感应电动机中的断条故障（BBF）。该方案可以在不同惯性、变负载条件和噪声环境下精确检测初始阶段的相邻和非相邻 BBF。该方案的主要思想是通过对定子电流的一个相施加快速傅里叶变换来监测主边带频率分量的相角变化。该方案不需要任何预定的设置，只需要在调试阶段的一个定子电流信号。阈值是自适应计算的，以区分正常和故障情况。此外，还提出了一个指标来指定故障严重程度。该方案的性能通过应用有限元法对两个具有不同设计的模拟电机进行了验证，此外还使用了真实的实验数据集。结果表明，该方案可以有效地检测相邻/非相邻版本中的半根、一根、两根或三根断条，并在不同的运行条件和噪声环境下估计其严重程度，准确性达到 100%，与电机参数无关。
