Open‐switch fault diagnosis in voltage source inverters of PMSM drives using predictive current errors and fuzzy logic approach

Abstract

In critical industrial applications fault diagnosis and fault tolerance are considered key features, in order to ensure the required reliability and availability levels. In this context, this paper proposes a new and effective diagnostic algorithm for power semiconductors opencircuit faults, in three-phase, two-level, voltage-source inverter-fed permanent magnet synchronous machine (PMSM). The proposed method is based on the analysis of the errors between the reference currents and the PMSM stator predictive currents. Hence, for each phase of the PMSM two fault diagnostic variables have been defined, which allow the diagnosis of both single and multiple open-circuit faults. The fuzzy logic approach is applied to the fault diagnostic variables in order to identify the faulty power switches. The method is experimentally validated on a model predictive controlled (MPC) permanent magnet synchronous motor (PMSM) drive, which shows the effectiveness of the fault diagnosis algorithm with a high robustness regarding the operating point and parameter variations of the PMSM drive system. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

ASMADO‐based refined anti‐disturbance low‐speed control of PMSM

“…Combining Equations ( 3) and ( 2), flux harmonics cause distortions in the current, which cause electromagnetic torque pulsation. And Equation (1) shows that torque pulsation causes speed pulsation. At this point, the magnetic flux linkage generated by the three-phase current is represented in the d -q coordinate system as [10]:…”

“…Permanent magnet synchronous motor (PMSM) has the advantages of a simple structure, high power factor, high energy utilization, fast dynamic response and especially great low-speed performance. It has been widely used in high-precision machine tools, robotics, electric vehicles, and aerospace [1]. However, some PMSMs are complex controlled objects with multivariable, strong coupling, nonlinearity and variable parameters [2,3].…”

A Robust Technique for Detection, Diagnosis, and Localization of Switching Faults in Electric Drives Using Discrete Wavelet Transform

“…The discrepancy between the predicted and the estimated fluxes are used for detecting SO faults [9]. Artificial intelligence-based techniques are also widely used for fault detection, such as neural networks [10], support vector machines [11], fuzzy logic [12], hybrid techniques such as wavelet neural networks [13], and multi-sensory control with wavelet analysis [14].…”

A Diagnosis Method of Semiconductor Power Switch Open-Circuit Fault in the PMSM Drive System With the MPCC Method

“…The MPCC method is relatively widely applied in PMSM drive system. The fault diagnosis research of PMSM drive system with the FCS-MPC method is getting more and more attention [37][38][39][40]. In [37], the DC component and second harmonic component in the cost function are used for the open-phase fault diagnosis, and the initial phase angle differences are defined to locate the faulty phase.…”

“…In [38], the wavelet transform is applied to extract the fault feature from the cost function existing in the MPC system, and the interturn fault is diagnosed by monitoring the normalized energy-related feature vector calculated from the wavelet transform coefficients. In [39], the errors between the reference currents and the PMSM stator predictive currents are used to generate appropriate diagnostic variables, which is combined with the fuzzy logic approach to identify the faulty power switches. In [40], the detection is implemented through the normalized average value of cost function variation over a fundamental period.…”