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Kan Liu received his B. Sc. degree in electrical engineering from Xi'an Jiaotong University in 2015, and the M. Sc. degree in electrical engineering from the University of Melbourne in 2018. He is currently a Research Assistant in the AC Transmission Development Department at Beijing Zongheng Electro-Mechanical Technology Co., Ltd. His current research interests include sensorless rail transit traction control, and advanced chopper control technologies, and their applications in modern traction drive systems.
Outline
This study aims to implement condition monitoring for urban rail train permanent magnet synchronous motors and inverter systems. Through the construction of a digital twin model, it performs fault diagnosis of potential system failures, enabling rapid fault localization and protection.
This research begins with a brief introduction to the structure and classification of permanent magnet synchronous motors (PMSMs), followed by a detailed analysis of their mathematical model. Subsequently, it thoroughly investigates the working principle of three-phase two-level inverters and the distribution of space voltage vectors. Based on the analysis of the main circuit topology, a digital twin model matching the external characteristics of the physical circuit is established using the model predictive control method, achieving accurate system simulation. Furthermore, through theoretical analysis and simulation verification of phase current characteristics under inverter switch tube faults, general patterns of phase currents under fault conditions are summarized. The established digital twin model is then employed to validate these patterns, confirming the model's effectiveness in fault diagnosis.
This study proposes a fault diagnosis method based on digital twins. Experimental and simulation results demonstrate that the established digital twin model can accurately simulate the external characteristics of the actual physical circuit, validating its effectiveness in inverter fault diagnosis. This approach offers practical value for condition monitoring in actual urban rail train systems.
The study innovatively starts from a mathematical model and simulates the actual physical model through a virtual model, requiring only external characteristics to achieve system fault diagnosis, thereby enhancing diagnostic efficiency.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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