Abstract
Permanent magnet machines are widely applied in motor drive systems. Therefore, condition monitoring of permanent magnet machines has great significance to assist maintenance. High temperatures are accountable for lots of typical malfunctions and faults, such as demagnetization of the permanent magnet (PM) and inter-turn short circuit of stator windings. Therefore, temperature monitoring of the PM and stator windings is essential for reliable operation. In this paper, an overview introducing and evaluating existing thermal monitoring methods is presented. First, the mechanism of thermal-caused failures for the PM and stator windings is introduced. Then, the design procedure and principles of existing temperature monitoring methods are introduced and summarized. Next, the evaluations and recommendations of application feasibility are demonstrated. Finally, the potential future challenges and opportunities for temperature monitoring of the PM and stator windings are discussed.
Highlights
Permanent magnet synchronous machines (PMSMs) have attracted more and more attention, especially in recent years
High temperatures cause the demagnetization of the permanent magnet (PM) and intensive thermal stress will lead to insulation aging of the stator windings
Except for the overheating caused by the normal operation, faults such as interturn short circuit insulation aging can lead to temperature rising of stator windings
Summary
Permanent magnet synchronous machines (PMSMs) have attracted more and more attention, especially in recent years. Whether for the PM or stator windings, from the implementation level, the temperature monitoring methods can be categorized into contact direct measuring methods and non-contact estimation methods. Non-contact estimation methods are usually based on the identification of thermalrelevant parameters or the temperature derivation of intelligence algorithm. For the temperature estimation of stator windings, similar to the PM, there are two main methods: observation model-based methods and signal injection-based methods. The AI algorithm-based methods used for temperature monitoring are attracting more and more attention and research. A prospective analysis for challenges and opportunities of temperature monitoring and investigation of the research trend is required.
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