Name
Title | ||
|---|---|---|
Modeling and detecting the stator winding fault of permanent magnet synchronous motors. |
Abstract | ||
|---|---|---|
Because of its high efficiency, robustness, and high power density, a permanent magnet synchronous machine (PMSM) is a desirable choice for high-performance applications, such as naval shipboard power systems. The stator winding fault is the most common electrical fault in PMSM; thus, detection of this type of fault is very important. The objective of this paper is to model and detect the location and severity of the stator winding fault of PMSM. To achieve this objective, a mathematical model that can describe both healthy and fault conditions is developed. Simulation results match the observations of this type of fault in the literature. According to the fault model, two parameters associated with fault location and fault severity must be identified in order to detect the fault. Because of the complex distribution of these two parameters in the fault model, the identification problem is extremely difficult for nonlinear identification techniques. To overcome this difficulty, the detection/identification problem is first transformed into a corresponding optimization problem and then solved using particle swarm optimization (PSO). By simulating a modified model instead of the original model, the PSO-based identification algorithm is able to identify the fault in real time. The real-time PSO-based identification algorithm also can be applied to many other identification problems. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.simpat.2012.04.007 | Simulation Modelling Practice and Theory |
Keywords | Field | DocType |
Fault diagnosis,Permanent magnet synchronous motors,Stator winding fault,Particle swarm optimization,Parameter identification,Real-time implementation | Particle swarm optimization,Control theory,Computer science,Robustness (computer science),Control engineering,Fault (power engineering),Stator,Optimization problem,Parameter identification problem,Fault model,Fault indicator | Journal |
Volume | ISSN | Citations |
27 | 1569-190X | 3 |
PageRank | References | Authors |
0.59 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenxin Liu | 1 | 96 | 8.39 |
| Li Liu | 2 | 46 | 8.81 |
| Il-Yop Chung | 3 | 13 | 1.95 |
| David A. Cartes | 4 | 64 | 11.09 |
| Wei Zhang | 5 | 10 | 1.84 |