內藏式永磁同步馬達之電流注入線上參數估測與智慧型電流迴路控制;Current Injection Based Online Parameters Estimation for IPMSM Drive System with Intelligent Current Loop Control

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題名:	內藏式永磁同步馬達之電流注入線上參數估測與智慧型電流迴路控制;Current Injection Based Online Parameters Estimation for IPMSM Drive System with Intelligent Current Loop Control
作者:	林威廷;Lin, Wei-Ting
貢獻者:	電機工程學系
關鍵詞:	線上參數估測;d軸電流注入;內藏式永磁同步馬達;遞迴最小平方法;每安培最大轉矩;比例積分微分類神經網路;Online parameter estimation;d-axis current injection, Current-controlled voltage source inverter (CCVSI);interior permanent magnet synchronous motor (IPMSM);recursive least square (RLS);maximum torque per ampere (MTPA) control;proportional–integral–derivative neural network (PIDNN)
日期:	2021-08-18
上傳時間:	2021-12-07 13:09:20 (UTC+8)
出版者:	國立中央大學
摘要:	本文提出一利用d軸電流注入線上參數估測，該方法可以於內藏式永磁同步馬達驅動系統在不同的運行條件下估測由電流控制電壓源變流器引發的失真電壓、由磁飽和現象引發的dq軸電感變化以及轉子磁通量。首先，利用dq軸電壓、電流和電源轉速的平滑值來估算電流控制電壓源變流器失真電壓和內藏式永磁同步馬達參數。此方法可以有效消除電壓、電流和轉子位置的測量誤差對估測的影響。然後，使用簡化的線性模型對電流注入其間dq軸電感的交互飽和及自飽和現象進行建模，此外，線性模型的參數式通過使用遞迴最小平方法估算出的。由於僅使用遞迴最小平方法難以獲得d軸未飽和電感，因此提出了一種新的d軸未飽和電感的調變方式，該方式為利用每安培最大轉矩理論結合遞迴最小平方法。接著在電流迴路的解耦控制中使用估測出的dq軸電感及轉子磁通量以確保在不同的運行條件下都能正確解耦。此外，為了提高電流迴路的控制性能和頻寬，採用一比利積分微分類神經網路控制器，並採用改進的線上學習法則來取代傳統的比例積分控制器。最後，由內藏式永磁同步馬達在不同運行條件下的實驗結果，證明所提出的估算和控制策略的有效性。 ;An online parameter estimation method using d-axis current injection, which can estimate the distorted voltage of the current-controlled voltage source inverter (CCVSI), the varying dq-axis inductances and the rotor flux, is proposed in this study for interior permanent magnet synchronous motor (IPMSM) drives under different operating conditions. First, a d-axis current injection-based parameter estimation method considering the nonlinearity of a CCVSI is proposed. Then, a simplified linear model is employed to model the cross- and self-saturation of the dq-axis inductances during current injection. Since the d-axis unsaturated inductance is difficult to obtain by merely using the recursive least square (RLS) method, a novel tuning method for the d-axis unsaturated inductance is proposed by using the theory of the maximum torque per ampere (MTPA) with the combination of the RLS method. Moreover, the estimated dq-axis inductances and magnetic flux of the rotor are then adopted in the decoupled control of the current loops. Furthermore, to improve the bandwidth of the current loop, a proportional–integral–derivative neural network (PIDNN) controller is adopted with improved online learning algorithm to replace the traditional PI controller. Finally, the experimental results at various operating conditions of the IPMSM are given.
顯示於類別:	[電機工程研究所] 博碩士論文

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