Responsive image
博碩士論文 etd-0803111-230837 詳細資訊
Title page for etd-0803111-230837
論文名稱
Title
感應機參數估測之研究
A Study on Parameter Identification of Induction Machine
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
105
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2011-06-23
繳交日期
Date of Submission
2011-08-03
關鍵字
Keywords
參數辨識、粒子群演算法、信號分析、感應機、最小均方根法
least mean square method, parameter identification, induction machine, signal analysis, particle swarm optimization
統計
Statistics
本論文已被瀏覽 5664 次,被下載 1983
The thesis/dissertation has been browsed 5664 times, has been downloaded 1983 times.
中文摘要
感應機的參數辨識對於在工業領域上是相當重要的,如應用在設計控制架構與效能分析。參數辨識的根本為輸入-輸出信號與模型,在辨識的過程中許多研究皆偏好於使用變流器來控制其激勵信號。本文提出一個方法在低壓無載起動測試下,估測出感應機的完整參數,此方法具有簡單的架構無需額外增加硬體,可以大大簡化程序及降低成本。本方法在電阻和電抗曲線下可求得感應機等效電路上的相關參數,配合輸入電壓與轉子轉速可以獲取其轉矩,最後其機械相關參數可由轉矩與轉速求得。在求解上述問題上本文使用最小均根方法搭配粒子群演算法來搜尋其最佳解。經過模擬與實測皆可印證此方法的實用性和準確性。另外,本文也提供一個方法,可以快速分析電力信號,藉由兩個資料點計算基頻電壓或電流的參數。電壓或電流的參數包括頻率、振幅及相位。由於在變動參數下頻率與相位是相依的,因此本文在頻率為固定的條件下計算振幅及相位,可以得到穩定的計算結果。本文提出一個方法在低壓無載起動測試下,可以估測出感應機的完整參數,此方法具有簡單的架構無需額外增加硬體,可以大大簡化程序及降低成本。
Abstract
Parameter identification of an induction machine is of great importance in numerous industrial applications, including the assessment of machine performance and design of control schemes. Parameter identification is based on the input-output signals and the model used. Many researches have applied the inverter drive to control the exciting signal of the induction machine in the identifying process. This study proposed a method to identify all parameter of the induction machine with a no-load low-voltage starting test. The method has a simple structure without needing extra hardware, which could significantly simplify the procedures and save cost. Based on the curves of resistance and reactance, the user can obtain the machine’s equivalent circuit parameters. With the identified parameters of the equivalent circuit, input voltage, and rotor speed, the user can find the torque. From the torque and rotor speed, the user can find the mechanical parameters. A least mean square (LMS) method was used with a particle swarm optimization (PSO) method to solve the aforementioned problem. From various tests, the practicability and accuracy of this method can been proven. This study also proposes a method to rapidly analyze power parameters. This method uses two adjacent data to compute the fundamental frequency component of voltage or current. The parameters of fundamental frequency component include frequency, amplitude, and phase. Under the condition of varied parameters, the frequency and phase are dependent. This method fixes the frequency and computes the amplitude and phase, and then stable results will be obtained.
目次 Table of Contents
誌謝.......................................................i
中文摘要...............................................ii
英文摘要...............................................iii
目錄.......................................................iv
圖目錄...................................................viii
表目錄...................................................x

第一章 緒論.......................................1
1.1 研究動機.........................................1
1.2 研究背景.........................................2
1.2.1 離線估測方法..............................3
1.2.2 線上估測方法..............................4
1.3 研究方法.........................................5
1.4 主要貢獻.........................................6
1.5 論文內容概要.................................7
第二章 時變信號的參數分析............8
2.1 前言..................................................8
2.2 電力基頻信號分析..........................9
2.2.1 濾波器設計..................................10
2.2.2 參數計算......................................13
2.2.3 初值設定......................................15
2.2.4 程序..............................................16
2.3 結果與討論 ...................................18
2.3.1 演算法的評估..............................18
2.3.2 實際應用......................................24
2.4 本章結論.........................................28
第三章 感應機的模型........................29
3.1 前言.................................................29
3.2 感應機的等效模型.........................30
3.2.1 穩態模型......................................30
3.2.2 動態模型......................................31
3.2.3 參數關係......................................35
3.3 感應機的模擬.................................36
3.3.1 動態模擬......................................36
3.3.2 穩態模擬......................................41
3.4 本章結論.........................................42
第四章 感應機參數估測方法............43
4.1 前言.................................................43
4.2 理論基礎.........................................46
4.2.1 感應機的數學模型…..................46
4.2.2 感應機等效阻抗曲線..................46
4.3 參數最佳化搜尋.............................47
4.3.1 粒子群演算法..............................47
4.3.2 最小均方根法..............................49
4.3.3 改良式粒子群演算法..................51
4.4 參數計算.........................................54
4.4.1 定子電阻直流測試......................54
4.4.2 初值設定......................................54
4.4.3 機械參數的計算..........................57
4.5 本章結論.........................................58
第五章 模擬分析與實驗....................59
5.1 前言.................................................59
5.2 模擬分析.........................................60
5.2.1 時變的參數...... ...........................60
5.2.2 初值設定......................................63
5.2.3 等效電路參數估測......................63
5.2.4 機械參數的估測..........................64
5.3 結果與討論.....................................66
5.3.1 精確度測試..................................68
5.3.2 不同額定電壓下的估測分析......73
5.3.3 雜訊的干擾..................................75
5.3.4 諧波的干擾..................................77
5.4 實驗分析.........................................80
5.4.1 測試過程......................................80
5.4.2 結果與討論..................................84
5.5 本章結論.........................................86
第六章 結論及未來研究方向............87
6.1 結論.................................................87
6.2 未來研究方向.................................88
參考文獻................................................89
參考文獻 References
[1] Nicola Tesla, “A New System of Alternating Current Motors and Transformers,” American Institute of Electrical Engineers, May 1888.
[2] R. Krishnan, Electric Motor Drives: Modeling Analysis and Control. Englewood Cliffs, NJ: Prentice-Hall, 2001.
[3] P. Vas, Sensorless Vector and Direct Torque Control. London, U.K.:Oxford Univ. Press, 1998.
[4] L. Ljung, System Identification: Theory for the Users, 2nd ed. Upper Saddle River, NJ: Prentice-Hall, 1999.
[5] J. He and Z.F. Fu, Modal Analysis, Boston: Butterworth- Heinemann, 2003.
[6] R. Pintelon and J. Schoukens, System Identification: A Frequency Domain Approach Piscataway, NJ: IEEE Press, 2001.
[7] T. Soderstorm and P. Stoica, System Identification, New-Jersey: Prentice Hall, 1989.
[8] E. D. Eyman, Modeling, Simulation, and Control, St. Paul, West Publication Company, 1988.
[9] T. A. Lipo, “The Analysis of Induction Motors with Voltage Control by Symmetrical Triggered thyristors,” IEEE Trans. On Power Apparatus and Systems, vol. 90, pp. 515-525, Mar./Apr. 1971.
[10] W. Shepherd, “On the Analysis of the Three-phase Induction Motor with Voltage Control by Thyristror Switching,” IEEE Trans. On Industry and General Applications, vol. 4, no. 3, pp. 304-311, May/Jun. 1968.
[11] T. G. Habetler, F. Profumo, M. Pastorelli, and L. M. Tolbert, “Direct Torque Control of Induction Machines using Space Vector Modulation,” IEEE. Trans. on Industry Applications, vol. 28, no. 5, pp. 1045-1053, Sept./Oct. 1992.
[12] Y. S. Lai, J. C. Lin, and J. J. Wang, “Direct Torque Control Induction Motor Drives with Self-commissioning Based on Taguchi Methodology,” IEEE Trans. on Power Electron., vol. 15, pp. 1065–1071, Nov. 2000.
[13] E. Bim, “Fuzzy Optimization for Rotor Constant Identification of an Indirect FOC Induction Motor Drive,” IEEE Trans. on Ind. Electron., vol. 48, pp. 1293–1295, Dec. 2001.
[14] R. Krishnan and A. S. Bharadwaj, “A Review of Parameter Sensitivity and Adaptation in Indirect Vector Controlled Induction Motor Systems,” IEEE Trans. on Power Electron., vol. 6, no. 4, pp. 623-635, Oct. 1991.
[15] H. A. Toliyat, E. Levi, and M. Raina, “A Review of RFO Induction Motor Parameter Estimation Techniques,” IEEE Trans. on Energy Convers., vol. 18, pp. 271-283, Jun. 2003.
[16] IEEE Standard Test Procedure for Polyphase Induction Motors and Generators, IEEE Standard 112-2004, Nov. 2004.
[17] S. I. Moon and A. Keyhani, “Estimation of Induction Machine Parameters from Standstill Time-domain Data,” IEEE Trans. on Ind. Applicat., vol. 30, pp. 1606–1615, Nov./Dec. 1994.
[18] M. Ruff, A. Bünte, and H. Grotstollen, “A New Self-commissioning Scheme for an Asynchronous Motor Drive System,” in Proc. IEEE Ind. Applicat. Soc. Annu. Meeting, pp. 612–623, 1994.
[19] A. M. Khambadkone and J. Holtz, “Vector-controlled Induction Notor Drive with a Self-commissioning Scheme,” IEEE Trans. on Ind. Electron., vol. 38, pp. 322–327, Oct. 1991.
[20] E. Levi, “Method of Magnetizing Curve Identification in Vector Controlled Induction Machines,” Europe. Trans. on Elect. Power, vol. 2, no. 5, pp. 309–314, 1992.
[21] E. Levi and S. N. Vukosavic, “Identification of the Magnetizing Curve During Commissioning of a Rotor Flux Oriented Induction Machine,” Proc. Inst. Elect. Eng.—Elect. Power Applicat., vol. 146, no. 6, pp. 685–693, 1999.
[22] L. A. Ribeiro, C. B. Jacobina, “Real-Time Estimation of the Electrical Parameters of an Induction Machine Using Sinusoidal PWM Voltage Waveforms,” IEEE Trans. on Ind. Appl., vol. 36, no. 3, pp. 743-754, May/Jun. 2000.
[23] J. Holtz, “Sensorless Control of Induction Machines—With or Without Signal Injection?,” IEEE Trans. on Ind. Electron., vol. 53, no. 1,pp. 7-30, Feb. 2006.
[24] Y. Wu, and H. Gao, “Induction-Motor Stator and Rotor Winding Temperature Estimation Using Signal Injection Method,” IEEE Trans. on Ind. Appl., vol. 42, no. 4, pp. 1038-1044, Jul./Aug. 2006.
[25] Y. S. Kwon, J. H. Lee, S. H. Moon, B. K. Kwon, C. H. Choi, and J. K. Seok, “Standstill Parameter Identification of Vector-Controlled Induction Motors Using the Frequency Characteristics of Rotor Bars,” IEEE Trans. on Ind. Appl., vol. 45, no. 5, pp. 1610-1618, Sep./Oct. 2009.
[26] P. Zhang, Y. Du, J. Dai, T. G. Habetler, and B. Lu, “Impaired-Cooling-Condition Detection Using DC-Signal Injection for Soft-Starter-Connected Induction Motors,” IEEE Trans. on Ind. Electron., vol. 56, no. 11, pp. 4642-4650, Nov. 2009.
[27] R. M. Moraes, L. A. S. Ribeiro, C.B. Jacobina, and A. M. N. Lima, “Parameter Estimation of Induction Machines by Using its Steady-state Model and Transfer Function,” IEEE International Electric Machines and Drives Conference, IEMDC'03, vol. 3, pp. 1965-1971, Jun. 2003.
[28] D. Telford, and M. W. Dunnigam, and Barry W. Williams, “Online Identification of Induction Machine Electrical Parameters for Vector Control Loop Tuning,” IEEE Trans. on Ind. Electron., vol. 50, no. 2, pp. 253-261, Apr. 2003.
[29] D. P. Marčetič, and S. N. Vukosavić, “Speed-Sensorless AC Drives With the Rotor Time Constant Parameter Update,” IEEE Trans. Ind. Electron., vol. 54, no. 5, pp. 2618-2625, Oct. 2007.
[30] S. Maiti, C. Chakraborty, Y. Hori, and M.C. Ta, “Model Reference Adaptive Controller-Based Rotor Resistance and Speed Estimation Techniques for Vector Controlled Induction Motor Drive Utilizing Reactive Power,” IEEE Trans. on Ind. Electron., vol. 55, no. 2, pp. 594-601, Feb. 2008.
[31] B. Abdelhadi, A. Benoudjit, and N. Nait-Said, “Application of Genetic Algorithm with a Novel Adaptive Scheme for the Identification of Induction Machine Parameters,” IEEE Trans. on Energy Convers., vol. 20, pp. 284-291, Jun. 2005.
[32] B. Karanayil, M. F. Rahman, and C. Grantham, “Online Stator and Rotor Resistance Estimation Scheme Using Artificial Neural Networks for Vector Controlled Speed Sensorless Induction Motor Drive,” IEEE Trans. on Ind. Electron., vol. 54, no. 1, pp. 167- 176, Feb. 2007.
[33] T. Orlowska-Kowalska and K. Szabat, “Neural-networks Application for Mechanical Variables Estimation of Two-mass Drive System,” IEEE Trans. on Ind. Electron., vol. 54, no. 3, pp. 1352-1364, Jun. 2007.
[34] M. Wlas, Z. Krzemi´nski, and H. A. Toliyat, “Neural-Network-Based Parameter Estimations of Induction Motors,” IEEE Trans. on Ind. Electron., vol. 55, no. 4, pp. 1783- 1794, Apr. 2008.
[35] A. Trentin, P. Zanchetta, C. Gerada, J. Clare, and P. W. Wheeler, “Optimized Commissioning Method for Enhanced Vector Control of High-Power Induction Motor Drived,” IEEE Trans. on Ind. Electron., vol. 56, no. 5, pp. 1708- 1717, May 2009.
[36] M. T. Wishart, and R. G. Harley, “Identification and Control of Induction Machines using Artificial Neural Networks,” IEEE Trans. on Ind. Application, vol. 31, no. 3, pp. 612-619, May-June 1995.
[37] C. Cecati and N. Rotondale, “On-line Identification of Electrical Parameters of the Induction Motor using RLS Estimation,” in Proc. IEEE IECON’98, vol. 4, Aug.-Sep. 1998, pp. 2263–2268.
[38] R. Babau, I. Boldea, T. J. E. Miller, and N. Muntean, “Complete Parameter Identification of Large Induction Machines From No-Load Acceleration-Deceleration Tests,” IEEE Trans. on Ind. Electron., vol. 54, no. 4, pp. 1962-1972, Aug. 2007.
[39] E. Laroche, and M.Boutayeb, “Identification of the Induction Motor in Sinusoidal Model,” IEEE Trans. on Energy Convers., vol. 25, no. 1, pp. 11-19, May. 2010.
[40] R. Bracewell, The Fourier transform and its application, New York: McGraw-Hill, 1965.
[41] A.V. Oppenheim and R.W. Schafer, Discreate-time signal processing, New Jersey: Prentic-Hall, 1989.
[42] J.Z. Yang and C.W. Liu, “A Precise Calculation of Power System Frequency,” IEEE Trans. on Power Delivery, vol. 16, no. 3, pp. 361-371, 2001.
[43] C.S. Moo, Y.N. Chang, and P.P. Mok, “A Digital Measurement Scheme for Time-varying Transient Harmonics,” IEEE Trans. on Power Delivery, vol. 10, No. 2, April 1995, pp. 588-594.
[44] V.K. Jain, W.L. Collins, Jr., and D.C. Davis, “High-accuracy Analog Measurements via Interpolated FFT,” IEEE Trans. on Instrument Measurement, vol. IM-28, No. 2, June 1979, pp. 113-122.
[45] H. C. So and Y. T. Chan “Analysis of an LMS Algorithm for Unbiased Impulse Response Estimation,” IEEE Trans. on Signal Processing, vol. 51,pp. 2008-2013, July 2003.
[46] N. Bose, Digital Filters: Theory and Applications, New York: North-Holland, 1985.
[47] A. G. Phadke and B. Kasztenny, “Synchronized Phasor and Frequency Measurement Under Transient Conditions,” IEEE. Trans. on Power Delivery, Vol. 24, No. 1, pp. 89- 95, 2009.
[48] Y.H. Lin, C.W. Liu and C.S. Chen, “An New PMU-Based Fault Detection/Location Technique for Transmission Lines With Consideration of Arcing Fault Discrimination, Part I : Theory and Algorithms,” IEEE Trans. on Power Delivery, vol. 19, no.4, pp. 1587-1593, Oct. 2004.
[49] W. M. Lin, T. J. Su, R. C. Wu, and C. T. Chiang, “Fast Analysis for Power Parameters by the Newton Method” 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 1868-1872, Jul. 2009.
[50] V. V. Terzija, M. B. Djuric, and B. D. Kovacevic, “Voltage Phasor and Local System Frequency Estimation Using Newton-Type Algorithms, ” IEEE. Trans. on Power Delivery, vol. 4, no. 3, pp. 1368- 1374, 1994.
[51] R. C. Wu and T. P. Tsao, “Theorem and Application of Adjustable Spectrum,” IEEE Trans. on Power Delivery, vol. 18, no. 2, pp. 372-376, 2003.04
[52] R. C. Wu and T. P. Tsao, , “The optimization of spectrum analysis for digital signal, ” IEEE Trans. on Power Delivery, vol. 18, no. 2, pp. 398-405, 2003.
[53] J. Chapman, Electric Machinery Fundamentals 4th. McGraw-Hill, 2005.
[54] J.J. Cathey, R. K. Calvin, III, and A.K. Ayoub, “Transient Load Model of an Induction Machine,” IEEE Trans. on Power Apparatus and Systems, vol. 92, pp. 1399-1406, Jul./Aug. 1973.
[55] F. Ding, L. Qiu, and T. Chen, “Reconstruction of Continuous-time Systems from their Non-uniformly Sampled Discrete-time Systems,” Automatica, vol. 45, no. 2, pp. 324-332, 2009.
[56] F. Ding, G. Liu, and P. X. Liu, “Partially Coupled Stochastic Gradient Identification Methods for Non-Uniformly Sampled Systems,” IEEE Trans. Aotom. Control., vol. 55, no. 8, pp. 1976-1981, Aug. 2010.
[57] F. Ding and T. Chen, “Performance Analysis of Multi-innovation Gradient Type Identification Methods,” Automatica, vol. 43, no. 1, pp. 1-14, 2007.
[58] F. Ding, and J. Ding, “Least-squares Parameter Estimation for System with Irregularly Missing Data,” International Journal of Adaptive Control And Signal Processing, vol. 24, no. 7, pp. 540-553, Jul. 2010.
[59] M. Cirrincione, M. Pucci, G. Girrincione, and G.A. Capolino, “Constrained Minimization for Parameter Estimation of Induction Motors in Saturated and Unsaturated Conditions,” IEEE Trans. on Ind. Electron., vol. 52, no. 5, pp. 1391-1402, Oct. 2005.
[60] K. E. Parsopoulos and M. N. Vrahatis, “On the Computation of All Global Minimizers Through Particle Swarm Optimization,” IEEE Trans. on Evol. Comput., vol. 8, no. 3, pp.211-224, 2004.
[61] F.J. Lin, L.T. Teng, J.W. Lin, and S.Y. Chen, “Recurrent Functional-Link-Based Fuzzy-Network-Controlled Induction-Generator System Using Improved Particle Swarm Optimization,” IEEE Trans. on Ind. Electron., vol. 56, no. 5, pp. 1557-1577, May 2009.
[62] J. Kennedy, and R. Eberhart, “Particle Swarm Optimization,” Proc. of IEEE International Conference on Neural Network, pp. 1942-1948, 1995.
[63] J. A. Anderson and E. Rosenfeld, Neurocomputing: Foundations of Research, MIT Press, Cambridge, MA 1988.
[64] P. C. Krause, O. Wasynczuk, and S. D. Sudhoff, Analysis of Electric Machinery and Driver System, 2nd ed., Wiley-IEEE Press, New York, 2002.
電子全文 Fulltext
本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。
論文使用權限 Thesis access permission:自定論文開放時間 user define
開放時間 Available:
校內 Campus: 已公開 available
校外 Off-campus: 已公開 available


紙本論文 Printed copies
紙本論文的公開資訊在102學年度以後相對較為完整。如果需要查詢101學年度以前的紙本論文公開資訊,請聯繫圖資處紙本論文服務櫃台。如有不便之處敬請見諒。
開放時間 available 已公開 available

QR Code