以超級電容為儲能元件之內藏式永磁同步馬達控制;A Supercapacitor Based IPMSM drive using intelligent control

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题名:	以超級電容為儲能元件之內藏式永磁同步馬達控制;A Supercapacitor Based IPMSM drive using intelligent control
作者:	張恩維;Chang, En-Wei
贡献者:	電機工程學系
关键词:	內藏式永磁同步馬達;超級電容;切比雪夫模糊類神經網路;城市輕軌車;Interior permanent magnet synchronous motor;Light rail vehicle;Supercapacitor
日期:	2020-08-17
上传时间:	2020-09-02 18:28:37 (UTC+8)
出版者:	國立中央大學
摘要:	在本研究中，開發了一種基於超級電容的內藏式永磁同步馬達驅動，以模擬城市輕軌車輛的運行，包括特定速度曲線的速度追隨和超級電容的充電。在基於超級電容的內藏式永磁同步馬達驅動中，設計了用於模擬輕軌車輛速度控制的驅動模式和用於超級電容充電的充電模式。在驅動模式下，開發了磁場導向控制內藏式永磁同步馬達驅動系統來模擬輕軌車輛的速度控制。在充電模式下，為超級電容的充電開發了恆流恆壓充電策略。此外，以上兩種模式使用相同的變頻器和坐標軸轉換以降低設計複雜度。而為了測試超級電容的性能，使用特定的測試行駛週期來獲得仿真輕軌車輛的速度命令。設計目標是對超級電容進行快速充電，使其能夠為模擬的輕軌車輛提供足夠的能量，以運行完整的測試行駛週期。另外，為提高仿真輕軌車輛暫態速度響應的控制性能，提出了一種切比雪夫模糊類神經網絡速度控制器，並詳細推導了所提出的切比雪夫模糊類神經網路的網絡架構，線上學習法則和收斂性分析。最後，展示一些實驗結果，以證明所開發之針對超級電容的恆流恆壓充電策略以及所提出的切比雪夫模糊類神經網路速度控制器對於仿真輕軌車輛的有效性。;A supercapacitor (SC) based interior permanent magnet synchronous motor (IPMSM) drive is developed in this study to emulate the operation of an urban light rail vehicle (LRV) including the speed tracking of a specific velocity profile and the charging of the SC. In the SC based IPMSM drive, the motoring mode to emulate the LRV speed tracking control and the charging mode for the charging of the SC are both designed. In the motoring mode, a field-oriented controlled (FOC) IPMSM drive system is developed to emulate the speed control of a LRV. In the charging mode, the constant current and constant voltage (CC-CV) charging strategy is developed for the charging of the SC. Moreover, the above two modes use the same inverter and coordinate transformations to reduce the design complexity. Furthermore, in order to test the performance of SC, the speed command of the emulated LRV is obtained using a specific testing driving cycle. The design objective is a quick charge of SC being able to provide enough energy for the emulated LRV to operate a full testing driving cycle. In addition, to improve the control performance of the transient speed of the emulated LRV, a Chebyshev fuzzy neural network (CheFNN) speed controller is proposed. The network structure, online learning algorithm and the convergence analysis of the proposed CheFNN are derived in detail. Finally, some experimental results are given to demonstrate the effectiveness of the developed CC-CV charging strategy for the SC and the proposed CheFNN speed controller for the emulated LRV.
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