An MPC-Based Online Interturn Fault Diagnosis Method for Induction Motors With Fault Localization
Zhen Jia, Wensheng Song, Chenwei Ma, Baojie Zhang, Na Sun
Abstract
Inter-turn fault (ITF) is considered as an early state of all motor stator faults. It can cause further harm to the whole winding if not detected and mitigated in time. Model predictive control (MPC) has become a popular control method for motor drives due to its fast dynamic response and flexible structure. However, ITF detection method in the framework of MPC still lacks investigations. In this article, the MPC principle is fully exploited, and a novel method of ITF detection and location under MPC is proposed. In the proposed method, the effect of different fault locations is taken into account in the faulty machine model. Then, the current residual information of αβ-axis is obtained by MPC paradigm, and the fault indicators are constructed by using the difference and ratio of current residual. Due to the further processing of residuals, the method has excellent robustness and low parameter sensitivity. In addition, ITF fault detection and location can be efficiently implemented online with simple operation and superior real-time performance. The proposed method does not require any additional observers, hardware or changes in the control structure. The proposed scheme can be easily embedded into the existing control system, which is suitable for industrial applications. The accuracy and robustness of the proposed method are verified by experimental results.