- 學位論文
以智慧型凸極式反電動勢估測器為基礎之無感測變流器饋接型壓縮機驅動系統開發
Development of Sensorless Inverter-Fed Compressor Drive System using Back-EMF Based Intelligent Estimator with Maximum Torque Per Ampere Control
摘要
本論文提出一個利用小波模糊類神經網路估測器之以凸極式反電動勢 為基礎之速度估測法，結合每安培最大轉矩控制，以改善應用在變頻壓縮 機驅動系統上內藏式永磁同步馬達之性能。文中首先說明內藏式永磁同步 電動機及其凸極式反電動勢之特性與數學模型，並分析了以凸極式反電動 勢為基礎的無感測控制及啟動策略，且同時提出適用於數位訊號處理器之 新型每安培最大轉矩控制；其次提出一新型無感測技術，利用小波模糊類 神經網路估測器之以凸極式反電動勢為基礎之速度估測法做為馬達控制策 略，以達到快速的暫態響應及節能效益。此外小波模糊類神經網路的網路 架構、線上學習法則將在本文被詳細的討論，並將以PSIM 搭配C 撰寫之 DLL 檔為模擬軟體進行模擬。最後利用微芯公司所生產之數位訊號處理器 實現變頻壓縮機驅動系統，並且以實驗結果驗證所提出方法之可行性。
並列摘要
A saliency back-EMF based wavelet fuzzy neural network (WFNN) torque observer combining with a new-type maximum torque per ampere (MTPA) control is proposed in this thesis to improve the speed estimating performance of a sensorless interior permanent magnet synchronous motor (IPMSM) used in inverter-fed compressor drive systems. First, the structure, characteristics and mathematical model of the IPMSM and the saliency back-EMF estimator are discussed, and the start-up strategy based on saliency back EMF is added. Then, a new saliency back EMF based MTPA control suitable for the implementation using digital signal processor (DSP) is introduced. Moreover, a back-EMF based speed estimation method using WFNN torque observer is proposed. Furthermore, the network structure and the online learning algorithms of WFNN are discussed in detail. In addition, a Microchip DSP is adopted to develop the sensorless inverter-fed compressor drive system. Finally, some experimental results are given to verify the feasibility of the proposed control schemes.