Abstract
Taking advantage of the high reliability, multiphase permanent magnet synchronous motors (PMSMs), such as five-phase PMSM and six-phase PMSM, are widely used in fault-tolerant control applications. And one of the important fault-tolerant control problems is fault diagnosis. In most existing literatures, the fault diagnosis problem focuses on the three-phase PMSM. In this paper, compared to the most existing fault diagnosis approaches, a fault diagnosis method for Interturn short circuit (ITSC) fault of five-phase PMSM based on the trust region algorithm is presented. This paper has two contributions. (1) Analyzing the physical parameters of the motor, such as resistances and inductances, a novel mathematic model for ITSC fault of five-phase PMSM is established. (2) Introducing an object function related to the Interturn short circuit ratio, the fault parameters identification problem is reformulated as the extreme seeking problem. A trust region algorithm based parameter estimation method is proposed for tracking the actual Interturn short circuit ratio. The simulation and experimental results have validated the effectiveness of the proposed parameter estimation method.
Highlights
Owing to high torque-to-current ratio, large power-to-weight ratio, high efficiency, high-power factor, high fault tolerance, robustness, and so forth, multiphase permanent magnet synchronous motors (PMSMs) have been paid more attention in high-power and high-reliability applications [1,2,3]
A novel fault diagnosis method of Interturn short circuit (ITSC) based on the trust region algorithm is proposed for five-phase PMSM
With the aid of the trust region algorithm which is global convergence, the interturn short circuit ratio μ is estimated with a short time transient
Summary
Owing to high torque-to-current ratio, large power-to-weight ratio, high efficiency, high-power factor, high fault tolerance, robustness, and so forth, multiphase PMSMs have been paid more attention in high-power and high-reliability applications [1,2,3]. In some AI fault detection and diagnosis methods, such as literature [11], in order to detect and diagnose the severity of the stator winding interturn short circuit fault of PMSM, a mathematical model that can describe both healthy and fault conditions is needed first. Literature [12] built power losses model of five-phase PMSM with ITSC fault and analyzed the changes in power losses due to faults occurrence by finite elements simulations This fault model is not suitable for AI fault diagnosis based on parameter. For the complex distribution of the parameters in the fault model, the identification problem is extremely difficult for nonlinear identification techniques To overcome this difficulty, the fault diagnosis problem is transformed into a corresponding optimization problem and solved by intelligent algorithm [15]. The simulation and experimental results have validated both the correction of the established models and the effectiveness of the proposed parameter estimation method
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