Modeling and Fault Diagnosis of Interturn Short Circuit for Five-Phase Permanent Magnet Synchronous Motor

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.

A Comprehensive Review of Winding Short Circuit Fault and Irreversible Demagnetization Fault Detection in PM Type Machines

“…The parameters of the PM motors, such as self and mutual inductance induced BEMF, and rotor saliency, are extremely sensitive to different unwanted variations caused by various types of faults. These parameters are observed during operation to diagnose the occurrence and severity of the fault [86]. Mostly, in these techniques, the threshold values for the parameter to be monitored are initially defined through FEM simulations or by experiments.…”

Detection of Inter-Turn Short Circuits in Induction Motors under the Start-Up Transient by Means of an Empirical Wavelet Transform and Self-Organizing Map

“…Certainly, the corresponding BMU associated with each input sample of the input feature space 𝑋 can be determined by measuring the Euclidean distance following Equation (18), where 𝑥 = (𝑥 , … , 𝑥 , … , 𝑥 ) is a sample that belongs to the training input feature space 𝑋 . To quantify the quality of adaptation achieved with the mapping through the SOM, the deviation between the BMUs and the input feature vectors is measured with two metrics, the quantization error (𝐸 ) and the topological error (𝐸 ), described with Equations ( 19) and (20), respectively. This is where each 𝑥 represents each one of the features of the input feature space 𝑋 , whereas each vector jth in the output neuron grid is represented by 𝑚 , and 𝑢(𝑥 ) is equal to 1 when the two first-evaluated BMUs for 𝑥 are adjacent (are not close between them); otherwise, 𝑢(𝑥 ) is equal to 0.…”

“…In relation to the diagnostics of ITSC faults, there exist several classic methodologies based on stator current, stator voltage, mechanical output, parameter estimation, and even magnetic flux [17]. Many works were initially model-based, for example, the physical parameters of a motor (resistances and inductances) were analyzed on a mathematical model for a five-phase permanent-magnet synchronous motor (PMSM) in [20]; posteriorly, a trust region algorithm uses an objective function for indicating the ratio of the ITSC. In another work [21], the modelling of a stator end winding of a six-pole synchronous generator is developed based on a qualitative theoretical analysis, a finite element analysis (FEA), and experimental validation.…”

Analysis and control of Twin Wind Turbine subject to asymmetric fault

“…The objective of control strategies applicated to the twin wind turbine is to control the whole structure position by keeping it in front of the wind direction at all times, in order to optimize energy production whereas the second objective is to control the electrical generator. Knowing that, electrical machines can be inevitably be damaged or afflicted by numerous failures as well as eccentricities, short winding fault, sensor faults and permanent magnet demagnetization fault [2], [3], an active fault tolerant control should be considered. Indeed, as shown in [4], the fault tolerance of the healthy control strategy developed in [4] is very questionable when considering some electrical faults.…”