應用於內藏式永磁同步馬達之智慧型慣量估測及共振頻率偵測

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：19

、訪客IP：18.220.249.203

姓名

周孝澤(Hsiao-Tse Chou) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

應用於內藏式永磁同步馬達之智慧型慣量估測及共振頻率偵測
(Intelligent Control with Inertia Estimation for IPMSM Drive System and the Detection of Resonance Frequency)

相關論文

★ 機場地面燈光更新工程 -以桃園國際機場南邊跑滑道為例	★ 多功能太陽能微型逆變器之研製
★ 應用於儲能系統之智慧型太陽光電功率平滑化控制	★ 利用智慧型控制之三相主動式電力濾波器的研製
★ 應用於內藏式永磁同步馬達之智慧型速度控制及最佳伺服控制頻寬研製	★ 新型每安培最大轉矩控制同步磁阻馬達驅動系統之開發
★ 同步磁阻馬達驅動系統之智慧型每安培最大轉矩追蹤控制	★ 利用適應性互補式滑動模態控制於同步磁阻馬達之寬速度控制
★ 具智慧型太陽光電功率平滑化控制之微電網電能管理系統	★ 高性能同步磁阻馬達驅動系統之寬速度範圍控制器發展
★ 智慧型互補式滑動模態控制系統實現於X-Y-θ三軸線性超音波馬達運動平台	★ 智慧型同動控制之龍門式定位平台及應用
★ 利用智慧型滑動模式控制之五軸主動式磁浮軸承控制系統	★ 智慧型控制雙饋式感應風力發電系統之研製
★ 無感測器直流變頻壓縮機驅動系統之研製	★ 應用於模組化輕型電動車之類神經網路控制六相永磁同步馬達驅動系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

本論文提出一非對稱歸屬函數之派翠機率模糊類神經網路對內藏式永磁同步馬達驅動系統即時慣量鑑別之技術，估測出的慣量將應用於內藏式永磁同步馬達驅動系統IP速度控制器之增益設計，並線上自動調變。本論文首先研究了具有IP速度控制器的磁場導向控制內藏式永磁同步馬達伺服驅動系統的動態分析，然後提出了非對稱歸屬函數之派翠機率模糊類神經網路用於即時鑑別內藏式永磁同步馬達伺服驅動系統的慣量。此外，還介紹了非對稱歸屬函數之派翠機率模糊類神經網路的網路結構和收斂性分析。根據實驗結果，可以在不同的操作條件下有效地線上調變IP速度控制器的增益。本論文亦發展一種基於小波濾波器的共振頻率偵測，以獲取轉子速度中共振頻率的特徵，並利用快速傅立葉轉換找出內藏式永磁同步馬達伺服驅動器的機械共振頻率。實驗所使用之硬體為應用德州儀器公司生產之浮點數數位訊號處理器TMS320F28075之內藏式永磁同步馬達驅動系統。

摘要(英)

A real-time moment of inertia identification technique using Petri probabilistic fuzzy neural network with an asymmetric membership function (PPFNN-AMF) for an interior permanent magnet synchronous motor (IPMSM) servo drive is proposed in this thesis. The estimated moment of inertia will be used in the online design of an integral-proportional (IP) speed controller to achieve the gains auto-tuning of the IPMSM servo drive. In this thesis, first, the dynamic analysis of a field-oriented control (FOC) IPMSM servo drive system with an IP speed controller is studied. Then, a heuristic approach using the PPFNN-AMF is proposed for the real-time identification of the moment of inertia of the IPMSM servo drive system. Moreover, the network structure and the convergence analysis of the PPFNN-AMF are introduced. From the experimental results, the gains of the IP speed controller can be effectively tuned online at different operating conditions. Furthermore, a wavelet filter-based scheme for the detection of mechanical resonance frequency is also developed in this thesis. The wavelet Daubechies 4 (db4) is adopted to extract the features of the resonant frequencies embedded in the rotor speed. In addition, fast Fourier transform (FFT) is applied to perform the detection of the mechanical resonant frequencies for IPMSM servo drives. The experimentation using the IPMSM servo drive based on Texas Instruments′ floating point digital signal processor (DSP) TMS320F28075 carried out.

關鍵字(中)

★ 內藏式永磁同步馬達
★ 線上增益調變
★ 派翠機率模糊類神經網路
★ 非對稱歸屬函數
★ 機械共振頻率

關鍵字(英)

★ Interior permanent magnet synchronous motor
★ online gain auto-tuning
★ Petri probabilistic fuzzy neural network
★ asymmetric membership function
★ mechanical resonant frequencies

論文目次

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VII
表目錄 XII
第一章緒論 1
1.1 研究動機 1
1.2 文獻回顧與簡介 2
1.3 本文貢獻 6
1.4 論文大綱 6
第二章內藏式永磁同步馬達變頻驅動器硬體介紹 8
2.1 簡介 8
2.2 變頻器 8
2.3 磁粉式煞車 9
2.4 數位訊號處理器 10
2.5 驅動控制電路板 14
第三章內藏式永磁同步馬達數學模型及電磁轉矩方程式 18
3.1 前言 18
3.2 三相座標轉換 20
3.3 內藏式永磁同步馬達在abc座標系下之數學模型 23
3.4 內藏式永磁同步馬達在αβ座標系下之數學模型 25
3.5 內藏式永磁同步馬達在d-q座標系下之數學模型 29
3.6 凸極式反電動勢定義 31
第四章非對稱歸屬函數之派翠機率模糊類神經網路 35
4.1 前言 35
4.2 非對稱歸屬函數之派翠機率模糊類神經網路架構 35
4.3 線上學習法則 39
4.4 網路收斂性 41
第五章具線上增益調變與慣量估測之馬達伺服驅動器 43
5.1 前言 43
5.2 內藏式永磁同步馬達伺服驅動系統 43
5.3 智慧型慣量估測 46
第六章小波轉換 48
6.1 簡介 48
6.2 連續小波轉換 49
6.3 離散小波轉換 50
6.4 小波轉換與多重解析度分析 50
6.4.1 尺度函數(Scaling Function) 51
6.4.2 多重解析度分析 52
6.4.3 多貝西小波(Daubechies Wavelet)函數 53
第七章共振頻率偵測 56
7.1 前言 56
7.2 小波共振頻率偵測 56
7.3 線上共振頻率偵測流程 58
7.4 動態訊號分析儀 61
第八章模擬與實驗結果 63
8.1 前言 63
8.2 智慧型慣量估測之馬達驅動器 64
8.2.1 模擬結果 64
8.2.2 實驗結果 85
8.3 共振頻率偵測結果 99
第九章結論與未來研究方向 110
參考文獻 111
作者簡歷 118

參考文獻

[1] 彭文陽，「先進馬達與驅動技術專輯」，機械工業雜誌，434期，2019年5月號
[2] P. Pillay, and R. Krishnan, “Application Characteristics of Permanent Magnet Synchronous and Brushless dc Motors for Servo Drives,” IEEE Trans. Ind. Appl., vol. 27, no. 5, pp. 986–996, Sep./Oct. 1991.
[3] H. Kim, J. Son, and J. Lee, “A high-speed sliding-mode observer for the sensorless speed control of a PMSM,” IEEE Trans. Ind. Electron., vol. 58, no. 9, pp. 4069–4077, Sep. 2011.
[4] S. Kim, Y. D. Yoon, S. K. Sul, and K. Ide, “Maximum torque per ampere (MTPA) control of an IPM machine based on signal injection considering inductance saturation,” IEEE Trans. Power Electron., vol. 28, no. 1, pp. 488–497, Jan. 2013.
[5] F. J. Lin, Y. C. Hung, J. M. Chen, and C. M. Yeh, “Sensorless IPMSM drive system using saliency back-EMF-based intelligent torque observer with MTPA control,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1226–1241, May 2014.
[6] J. Lemmens, P. Vanassche, and J. Driesen, “PMSM drive current and voltage limiting as a constraint optimal control problem,” IEEE J. Emerg. Sel. Topics Power Electron., vol. 3, no. 2, pp. 326–338, Jun. 2014.
[7] T. Sun, J. Wang, and X. Chen, “Maximum torque per ampere (MTPA) control for interior permanent magnet synchronous machine drives based on virtual signal injection,” IEEE Trans. Power Electron., vol. 30, no. 9, pp. 5036–5045, Sep. 2015.
[8] K. Liu, and Z. Zhu, “Fast determination of moment of inertia of permanent magnet synchronous machine drives for design of speed loop regulator, ” IEEE Trans. Control Syst. Technol., vol. 25, no. 5, pp. 1816–1824 Sep. 2017.
[9] C. J. Hsu, and Y. S. Lai, “Novel on-line optimal bandwidth search and auto tuning techniques for servo motor drives,” Proc. IEEE Energy Conversion Congress and Expo. (ECCE), pp. 1-8, 2016.
[10] F. Andoh, “Moment of inertia identification using the time average of the product of torque reference input and motor position,” IEEE Trans. Power Electron., vol. 22, no. 6, pp. 2534–2542, Nov. 2007.
[11] S. Li, and Z. Liu, “Adaptive speed control for permanent-magnet synchronous motor system with variations of load inertia, ” IEEE Trans. Ind. Electron., vol. 56, no. 8, pp. 3050–3059, Aug. 2009.
[12] J. W. Choi, S. C. Lee, H. G. Kim, “Inertia identification algorithm for high-performance speed control of electric motors”, IEE Proc. Electric Power Appl., vol. 153, no. 3, pp. 379–386, May 2006.
[13] L. Niu, D. Xu, M. Yang, X. Gui, and Z. Liu, “On-line inertia identification algorithm for PI parameters optimization in speed loop,” IEEE Trans. Power Electron., vol. 30, no. 2, pp. 849–859, Feb. 2015.
[14] K. Liu and Z. Zhu, “Mechanical parameter estimation of permanent magnet synchronous machines with aiding from estimation of rotor pm flux linkage,” IEEE Trans. Ind. Appl., vol. 51, no. 4, pp. 3115–3125, Jul./Aug. 2015.
[15] W. Yu, and X. Li, “Fuzzy identification using fuzzy neural networks with stable learning algorithms,” IEEE Trans. Fuzzy Syst., vol. 12, no. 3, pp. 411–420, Jun. 2004.
[16] F. J. Lin, H. J. Shieh, P. K. Huang, and L. T. Teng, “Adaptive control with hysteresis estimation and compensation using RFNN for piezo-actuator,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 53, no. 9, pp. 1649–1661, Sep. 2006.
[17] F. J. Lin, P. H. Chou, C. S. Chen, and Y. S. Lin, “DSP-based cross-coupled synchronous control for dual linear motors via intelligent complementary slidingmode control,” IEEE Trans. Ind. Electron., vol. 59, no. 2, pp. 1061–1073, Feb. 2012.
[18] H. Chaoui, and P. Sicard, “Adaptive fuzzy logic control of permanent magnet synchronous machines with nonlinear friction,” IEEE Trans. Ind. Electron., vol. 59, no. 2, pp. 1123–1133, Feb. 2012.
[19] M. A. Khanesar, E. Kayacan, M. Teshnehlab, and O. Kaynak, “Extended Kalman filter based learning algorithm for type-2 fuzzy logic systems and its experimental evaluation,” IEEE Trans. Ind. Electron., vol. 59, no. 11, pp. 4443–4455, Nov. 2012.
[20] S. J. Hart, R. E. Shaffer, S. L. Rose-Pehrsson, and J. R. McDonald, “Using physics-based modeler outputs to train probabilistic neural networks for unexploded ordnance (UXO) classification in magnetometry surveys,”IEEE Trans. Geosci. Remote Sensing, vol. 39, pp. 797–804, Jun. 2001.
[21] X. Jin, D. Srinivasan, and R. L. Cheu, “Classification of freeway traffic patterns for incident detection using constructive probabilistic neural networks,” IEEE Trans. Neural Networks, vol. 12, no. 5, pp. 1173-1187,
September 2001.
[22] F. J. Lin, K. C. Lu, T. H. Ke, and H. Y. Li, “Reactive power control of three-phase PV system during grids faults using Takagi-Sugeno-Kang probabilistic fuzzy neural network control,” IEEE Trans. Ind. Electron., vol. 62, no. 9, pp. 5561–5528, Sep. 2015.
[23] R. J. Wai and R. Muthusamy, “Design of fuzzy-neural-network inherited backstepping control for robot manipulator including actuator dynamics,” IEEE Trans. Fuzzy Syst., vol. 22, no. 4, pp. 709–722, Aug. 2014.
[24] F. J. Lin, M. S. Huang, P. Y. Yeh, H. C. Tsai, and C. H. Kuan, “DSP-based probabilistic fuzzy neural network control for li-ion battery charger,” IEEE Trans. Power Electron., vol. 27, no. 8, pp. 3782–3794, Aug. 2012.
[25] K. H. Tan, “Squirrel-cage induction generator system using wavelet Petri fuzzy neural network control for wind power applications,” IEEE Trans. Power Electron., vol. 31, no. 7, pp. 5242-5254, Jul. 2016.
[26] K. Kaur, R. Rana, N. Kumar, M. Singh, and S. Mishra, “A colored petri net based frequency support scheme using fleet of electric vehicles in smart grid environment,” IEEE Trans. Power Syst., vol. 31, no. 6, pp. 4638–4649, Nov. 2016.
[27] P. Wang, L. Ma, R. M. P. Goverde, and Q. Wang, “Rescheduling trains using Petri nets and heuristic search,” IEEE Trans. Intell. Transp. Syst., vol. 17, no. 3, pp.726–735, Mar. 2016.
[28] Y. C. Hung, F. J. Lin, J. C. Hwang, J. K. Chang, and K. C. Ruan, “Wavelet fuzzy neural network with asymmetric membership function controller for electric power steering system via improved differential evolution,” IEEE Trans. Power Electron., vol. 30, no. 4, pp. 2350-2362, Apr. 2015.
[29] T. Sreekumar and K. S. Jiji, “Comparison of Proportional-Integral (P-I) and Integral-Proportional (I-P) controllers for speed control in vector controlled induction Motor drive,” 2012 2nd International Conference on Power, Control and Embedded Systems, Allahabad, 2012, pp. 1-6.
[30] S. M. Yang, and S. C. Wang, “The detection of resonance frequency in motion control systems,” IEEE Trans. Ind. Appl., vol. 50, no. 5, pp. 3423–3427, Sep./Oct. 2014..
[31] S. H. Kia, H. Henao, and G. A. Capolino, “Torsional vibration assessment using induction machine electromagnetic torque estimation,” IEEE Trans. Ind. Electron., vol. 57, no. 1, pp. 209–219, Jan. 2010.
[32] A. R. Mohanty, and C. Kar, “Fault detection in a multistage gearbox by demodulation of motor current waveform,” IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1285–1297, Aug. 2006.
[33] Y. Wang, Q. Zheng, and H. Zhang, “Adaptive control and predictive control for torsional vibration suppression in helicopter/engine system,” IEEE Access, vol. 6, pp. 23896–23906, Apr. 2018.
[34] C. I. Chen, Y. C. Chen, “Intelligent identification of voltage variation events based on IEEE Std 1159-2009 for SCADA of distributed energy System,” IEEE Trans. Ind. Electron., vol. 62, no. 4, pp. 2604-2611, Apr. 2015.
[35] C.I. Chen, “Virtual multifunction power quality analyzer based on adaptive linear neural network,” IEEE Trans. Ind. Electron., vol. 59, no. 8, pp. 3321-3329, Aug. 2012.
[36] Z. Zhao, C. Liu, Y. Li, Y. Li, J. Wang, and B. S. Lin, J. Li, “Noise Rejection for Wearable ECGs Using Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks,” IEEE Access., vol. 7, no. 4, pp. 34060-34067, Feb. 2019.
[37] P. Sangeetha, and S. Hemamalini, “Dyadic wavelet transform-based acoustic signal analysis for torque prediction of a three-phase induction motor,” IET Signal Process., vol. 11, no. 5, pp. 604-612, Jul. 2017.
[38] S. Mallat, “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 11, No. 7, Jul. 1989, pp. 674-693.
[39] M.T. Hanna, and S.A. Mansoori, “The discrete time wavelet transform: its discrete time fourier transform and filter bank implementation,” IEEE Trans. Circuits Systems II: Analog Digital Signal Process, vol. 2, pp. 180-183, 2001.
[40] J. A. R. Macias, and A. G. Exposito, “Efficient computation of the running discrete Haar transform,” IEEE Trans. Power Del., vol. 21, no. 1, pp. 504-5, 2006
[41] Y. Y. Hong, and C. W. Wang, “Switching detection/classification using discrete wavelet transform and self-organizing mapping network,” IEEE Trans. Power Del., vol. 20, no. 2, pp. 1662-1668, Apr. 2005.
[42] 盛暘科技股份有限公司，數位訊號處理，2007。
[43] Texas Instruments，TMS320F28075 datasheet.
[44] MCP4922 datasheet.
[45] 黃泰寅，「新型每安培最大轉矩控制同步磁阻馬達驅動系統之開發」，中央大學電機工程系，碩士論文，民國106年6月。
[46] 俞韋安，「應用於內藏式永磁同步馬達之智慧型最佳伺服頻寬調整及慣量估測」，中央大學電機工程系，碩士論文，民國107年7月。
[47] 陳家銘，「以單一直流鏈電流感測器結合低轉速轉矩補償之無轉軸位置感測器變頻壓縮機驅動系統開發」，中央大學電機工程系，碩士論文，民國102年6月。
[48] K. Ahsanullah, R. Dutta and M. F. Rahman, “Analysis of Low-Speed IPMMs With Distributed and Fractional Slot Concentrated Windings for Wind Energy Applications,” IEEE Transactions on Magnetics, vol. 53, no. 11, pp. 1-10, Nov. 2017
[49] 陳航生，「內藏式永磁同步馬達之特性分析及其電動機車之應用」，中央大學電機工程系，碩士論文，民國93年5月。
[50] 劉昌煥，「交流電機控制」，東華書局，民國92年。
[51] 高子胤，「以反電動勢為基礎之比例積分微分類神經網路估測器之無感測器變頻壓縮機驅動系統開發」，中央大學電機工程系，碩士論文，民國100年7月。
[52] Z. Chen, M. Tomita, S. Ichikawa, S. Doki, and S. Okuma, “Sensorless control of interior permanent magnet synchronous motor by estimator of an extented electromotive force,” in Proc. IECON 00., 2000, pp. 1814-1819.
[53] F. J. Lin and R. J. Wai, “Hybrid controller using a neural network for a PM synchronous servo-motor drive,” IEE Proc.-Elect. Power Appl., vol. 145, no. 3, pp. 223–230, May 1998.
[54] L. Guo and L. Parsa, “Model reference adaptive control of five-phase IPM motors based on neural network,” IEEE Trans. Ind. Electron., vol. 59, no. 3, pp. 1500–1508, Mar. 2012.
[55] 王政偉，「結合小波轉換與類神經網路辨別電力開關之切換」，中原大學電機工程系，碩士論文，民國92年7月。
[56] S. Mallat, “Multifrequency channel decompositions of images and wavelet models,” IEEE Trans. Acoust. Speech Signal Processing, vol. 37, no. 12, pp. 2091-2110, Dec. 1987.
[57] A. Houari, A. Bouabdallah, A. Djerioui, M. Machmoum, F. Auger, A. Darkawi, J. C. Olivier, and M. F. Benkhoris, “An Effective Compensation Technique for Speed Smoothness at Low-Speed Operation of PMSM Drives,” IEEE Trans. Ind. Appl., vol. 54, no. 1, pp. 647–654, Apr. 2018.
[58] Keysight 35670A, datasheet.
[59] H. Sediki, A. Bechouche, D. O. Abdeslam, and S. Haddad, “ADALINE approach for induction motor mechanical parameters identification,” Mathematics and Computers in Simulation, Elsevier, pp. 86-97, 2013.
[60] N. Leonard, “An investigation of control strategies for friction compensation,” Master’s Thesis, University of Maryland, 1991.
[61] N. V. Nenkov, E. Zdravkova, “Implementation of a Neural Network Using Simulator and Petri Nets,” (IJACSA) International Journal of Advanced Computer Science and Applications., vol. 7, no. 1, pp. 412–417, Sep. 2001.
[62] W. M. Lin, C. H. Lin, Z. C. Sun, “Adaptive multiple fault detection and alarm processing for loop system with probabilistic network,” IEEE Trans. Power Del., vol. 19, no. 1, pp. 64-69, Jan. 2004.

指導教授

林法正(Faa-Jeng Lin)

審核日期

2019-8-1

推文