
系統識別號 
U00260812200915040081 
論文名稱(中文) 
藉基因演算法之遞迴式類神經網路應用於電力系統穩定器之研究 
論文名稱(英文) 
Study of the Power System Stabilizer Using Recurrent Neural Networks based on Genetic Algorithm 
校院名稱 
成功大學 
系所名稱(中) 
工程科學系碩博士班 
系所名稱(英) 
Department of Engineering Science 
學年度 
97 
學期 
1 
出版年 
98 
研究生(中文) 
陳俊榮 
研究生(英文) 
ChunJung Chen 
電子信箱 
n9893104@ccmail.ncku.edu.tw 
學號 
N9893104 
學位類別 
博士 
語文別 
英文 
論文頁數 
120頁 
口試委員 
召集委員周榮華 口試委員卓明遠 指導教授陳添智 口試委員黎碧煌 口試委員蕭瑛星

中文關鍵字 
遞迴型類神經網路
適應類神經網路
基因演算法
電力系統穩定器

英文關鍵字 
power system stabilizer
adaptive neural network control
Recurrent neural network
genetic algorithm

學科別分類 

中文摘要 
none

英文摘要 
The study and design of power system stabilizer (PSS) using a recurrent neural network based on genetic algorithm is investigated in this dissertation. Since the power system is extremely nonlinear, the PSS is used to damp the oscillations for power generators. The conventional PSSs are used to damp the oscillations base on linear cases and nominal operating point, hence the operating point are limited. In recent years, the artificial intelligent PSS were widely discussed and published. The feedforward neural networks (FNN) and recurrent neural networks (RNN) were proposed to be applied on the PSS, called FNNPSS and RNNPSS. In this dissertation, a new two layer RNNPSS was proposed which consist of input layer and output layer. The neurons on the input layer are connected to itself and other neurons on the same layer and then connect to output layer. The RNNPSS includes an identifier and a controller. The function of identifier is to track the characteristics of power generator. It can effectively identify the system without knowing its structure or mathematic models of the power systems. The function of the controller is used to supply an adaptive signal to the exciter or governor according the information of identifier. Thus, the oscillations of the power system can be effectively damped by the RNNPSS. Since the learning rates are used to be found by trial and error in the past. The RNNPSS based on genetic algorithm is proposed in this dissertation. The function of genetic algorithm is to find the optimal learning rates for RNNPSS. In order to damp the oscillations of the power systems effectively, the RNNPSS based on genetic algorithm is first trained off line. After the optimal learning rates are determined, the RNNPSS based on genetic algorithm can be guaranteed to be convergent for power system to damp the oscillations. Simulation results demonstrate the performance is effective on damping jobs.
The new two layers RNN is proposed to be applied on an adaptive control PSS which includes a recurrent neural network controller (RNNC) and a compensator. The function of RNNC and compensator are to supply an adaptive control signal to the exciter or governor with the adaptive law. The principle analysis and equation derivation of the adaptive neural network control PSS is introduced and deduced. Simulations for the power system are demonstrated their performance and compare with the conventional PSS does.

論文目次 
Abstract I
Acknowledgements III
Contents V
List of Figures and Table VII
Nomenclature XII
Chapter 1 Introduction 1
1.1 Background and motivation 1
1.2 The proposed recurrent neural network 9
Chapter 2 Modeling of the Power System 13
2.1 Motivation 13
2.2 The simple power system model 14
2.3 The Nbuses power system model 16
2.4 The peripheral equipments and power system stabilizer 22
Chapter 3 RNNPSS for Power Systems 25
3.1 Motivation 25
3.2 RNNPSS for threemachine power system 27
3.3 The proposed RNNPSS 30
3.3.1 The RNNI 30
3.3.2 The RNNC 33
3.4 Convergence of the RNNI 36
3.5 Convergence of the RNNC 39
3.6 Computer simulations 40
3.7 The RNNPSS based on GA 50
3.8 Computer simulations 52
3.9 Changing the operating point 62
Chapter 4 PSS Design Using Adaptive Control 70
4.1 Motivation 70
4.2 The adaptive control scheme 71
4.3 Computer simulations 78
4.4 Changing the operating point for adaptive RNNC PSS 87
Chapter 5 Conclusions 95
References 98
Appendix I The Tracking Effect of Proposed TwoLayer RNNI 110
Appendix II The Parameters of State Equation 113
Publish List 117
Curriculum Vitae 119

參考文獻 
[1] Larsen, E. V. and Swann, D. A., “Applying power system stabilizers part I: General concepts,” IEEE Trans. Power Apparatus and System, vol. PAS100, No. 6, pp. 30173024, June 1981.
[2] Kao, W., “Effect of Load Models on Unstable LowFrequency Oscillation Damping in Taipower System Experience with and Without Power System Stabilizers,” IEEE Power Engineering Review. pp. 72, June 2001.
[3] Hamouda, R. M., Iravani, M. R., and Hackam, R., “Coordinated static VAR and power system stabilizers for damping power system oscillations,” IEEE Trans. Power Systems. vol. 2, No. 4, pp. 10591067, November 1987.
[4] Bera, P., Das, D and Basu, T. K., “Design of PID power system stabilizer for multimachine system,” IEEE India annual conference Indicon 2004, pp. 446450, 2004.
[5] Vitthal, B., and Bandyopadhyay, B., “Relay free sliding mode control technique based power system stabilizer for single machine infinite bus system,” IEEE Proceeding of 2007 American Control Conference, pp. 59225927, July 2007.
[6] Joe, H. C., George, E. B., and Alexander, M., “Power system stabilizer as undergraduate control design projects,” IEEE Tran. Power Systems, vol. 19, No. 1, pp. 144151, February 2004.
[7] Aldeen, M and Crusca, F., “Multimachine power system stabiliser design based on new LQR approach,” IEE Proceeding Gener. Trasm. Distrib., vol. 142, No. 5, pp. 494502, September 1995.
[8] DeMello, F. P. and Laskowski, T. F., “Concepts of power system dynamic stability,” IEEE Trans. Power Apparatus and System, vol. PAS94, pp. 827833, May 1975.
[9] AbdelMagid, Y. L., Abido, M. A., and Mantawy, A. H., “Robust tuning of power system stabilizers in multimachine power systems,” IEEE Trans. Power Systems, vol. 15, No. 2, pp. 735740, May 2000.
[10] Chaturvedi, D. K., Malik, O. P., and Kalra, P. K., “Experimental studies with a generalized neuronbased power system stabilizer,” IEEE Trans. Power Systems, vol. 19, No. 3, pp. 14451453, August 2004
[11] Hoang, P. and Tomsovic, K., “Design and analysis of an adaptive fuzzy power system stabilizers,” IEEE Trans. Energy Conversion, vol. 11, pp. 455481, June 1996.
[12] Chan, W. C. and Hsu, Y. Y., “An optimal variable structure stabilizer for power system stabilization,” IEEE Trans. Power Apparatus and Systems, vol. PAS102, No. 6, pp. 17381746, June 1983.
[13] Fleming, R. J., Mohan, M. A., and Paratisam, K., “Selection of parameters in mutimachine power systems,” IEEE Trans. Apparatus and Systems, vol. PAS100, No. 5, pp. 23292333, May 1981.
[14] Yu, Y. N., and Siggers, C., “Stabilization and optimal control signals for a power system,” IEEE Trans. Power Apparatus and Systems, vol. PAS90, pp.14691481, July/August 1971.
[15] Yu, Y. N., and Moussa, H. A. M., “Optimal stabilization of a multimachine system,” IEEE Trans. Power Apparatus and Systems, vol. PAS91, pp. 11741182. May/June 1972.
[16] Chen, T. C., and ChangChien, L. R., “Optimal pole assignment to stabilize a power system, Journal of Control Systems and Technology,” vol. 4, No. 2, pp. 119126, 1996.
[17] Nomikos, B. M. and Vournas, C. D., “Investigation of induction machine contribution to power system oscillations,” IEEE Trans. Power Systems, vol. 20, pp. 916925, May 2005.
[18] Hosseinzadeh, N. and Kalam, A., A “direct adaptive fuzzy power system stabilizer,” IEEE Trans. Energy Conversion, vol. 14, pp. 15641571, Dec.1999.
[19] Wenxin, L., Venaygamoorthy, G. K. and Wunsch, D. C., “Adaptive neural network based power system stabilizer design,” IEEE Trans. Neural Networks, vol. 4, pp. 29702975, July 2003.
[20] Giles, G. L., Kuhn, G. M. and Williams, R. J., “Dynamic recurrent neural networks: theory and applications,” IEEE Trans. Neural Networks, vol. 5, No. 2, pp. 153155, Mar. 1994.
[21] You, R., Eghbali, H. J. and Nehrir, M. H., “An online adaptive neurofuzzy power system stabilizer for multimachine systems,” IEEE Trans. Power Systems, vol. 18, No.1, pp.128135, Feb. 2003.
[22] Gupta, R., Bandyopadhyay, B. and Kulkarni, A. M., “Design of power system stabilizer for singlemachine system using robust periodic output feedback controller,” IEE Proc. Generation, Transmission and Distribution, vol. 150, issue 2, pp. 211216, Mar. 2003.
[23] Xu, D. and He, R. M., “ANN based multiple power system stabilizers adaptive and coordinates control,” PowerCon 2002. International Conference Proceeding, vol. 1, pp. 361364, Oct. 2002.
[24] Ramakrishna, G. and Malik, O. P., “Radial basis function identifier and poleshifting controller for power system stabilizer application,” IEEE Trans. Energy Conversion, vol. 19, No. 4, pp. 663670, Dec. 2004.
[25] Salem, M. M., “Studies on a multimachine power system with a neural network based excitation controller,” Power Engineering Society Meeting, 2000 IEEE, vol. 1, pp. 105110, July 2000,
[26] Chen, C. J. and Chen, T. C., “Design of power system stabilizer using a new recurrent neural network,” IEEE proceedings, First International Conference on Innovative Computing, Information and Control, vol. I, pp. 39~42, Aug. 2006.
[27] .Chen, G.. P. and Malik, O. P., “An adaptive power system stabilizer based on the selfoptimizing pole shifting control strategy,” IEEE Trans. Energy Conversion, vol. 8, pp. 639645, Dec. 1993.
[28] Chaturvedi, D. K., Malik, O. P. and Kalra, P. K., “Performance of a generalized neuronbased PSS in a multimachine power system,” IEEE Trans. Energy Conversion, vol. 19, pp.625632, Sept. 2004.
[29] Lee, K. C., and Hydro, B. C., “Analysis of power system stabilizers application for controlling poorly damped oscillations in the alcan/B. C. hydro power systems,” IEEE Trans. Power Systems, vol. 8, No. 1, pp. 255263, February 1993.
[30] Zhang, Y., Chen, G. P., Malik, O. P., and Hope, G. S., “An artificial neural network based adaptive power system stabilizer,” IEEE Trans. Energy Conversion, vol. 8, No. 1, pp. 7177, March 1993.
[31] Boukarim, G. E., Wang, S. Chow, J. H. Taranto, G. N., and Martins, N., “A comparison of classical, robust, and decentralized control designs for multiple power system stabilizers,” IEEE Trans. Power Systems, vol. 15, No. 4, pp. 12871292, November 2000.
[32] Hsu, Y. Y., and Hsu, C. Y., “Design of a proportionalintegral power system stabilizer,” IEEE Trans. Power Systems, vol. PWRS1, No. 2, pp. 4652, May 1986.
[33] Hosaeinzadeh, N., and Kalam, A., “A direct adaptive fuzzy power system stabilizer,” IEEE Trans. Energy Conversion, vol. 14, No. 4, pp. 15641571, December 1999.
[34] Hosseinzadeh, N., and Kalam, A., “A rulebased fuzzy power system stabilizer tuned by a neural network,” IEEE Trans. Energy Conversion, vol. 14, No. 3, pp. 773779, September 1999.
[35] Liu, W., Venayagamoorthy, G. K., and Wunsch, D. C., “A heuristicdynamicprogrammingbased power system stabilizer for a turbogenerator in a singlemachine power system,” IEEE Trans. Industry Applications, vol. 41, No. 5, pp. 13771385, September 2005.
[36] Kamwa, I., Grondin, R., and Trudel, G., “IEEE PSS2B versus PSS4B: The limits of performance of modern power system stabilizers,” IEEE Trans. Power Systems, vol. 20, No. 2, pp. 903915, May 2005.
[37] Abido, M. A., and AbdelMagid, Y. L., “Hybridizing rulebased power system stabilizer with genetic algorithm,” IEEE Trans. Power Systems, vol. 14, No. 2, pp. 600607, May 1999.
[38] Chaturvedi, D. K., and Malik, O. P., “Neurofuzzy Power System Stabilizer,” IEEE Trans. Energy Conversion, vol. 23, No. 3, pp. 887894, September 2008.
[39] Liu, S., Messina, A. R., and Vittal, V., “A normal form analysis approach to siting power system stabilizer (PSSs) and assessing power system nonlinear behavior,” IEEE Trans. Power Systems, vol. 21, No. 4, pp. 17551762, November 2006.
[40] Yue, M., and Schlueter, R. A., “μsynthesis power system stabilizer design using a bifurcation subsystem based methodology,” IEEE Trans. Power Systems, vol. 18, No. 4, pp. 14971506, November 2003.
[41] Rogers, G. J., “The application of power system stabilizer to a muligenerator plant,” IEEE Trans. Power Systems, vol. 15, No. 1, pp. 350355, February 2000.
[42] Nambu, M., and Ohsawa, Y., “Development of an advanced power system stabilizer using a strict linearization approach,” IEEE Trans. Power Systems, vol. 11, No. 2, pp. 813818, May 1996.
[43] Chen, C. L., and Hsu, Y. Y., “Coordinated synthesis of multimachine power system stabilizer using an efficient decentralized modal control (DMC) algorithm,” IEEE Trans. Power Systems, vol. PWRS2, No. 3, pp. 543550, August 1987.
[44] AbdelMagid, Y. L., and Abido, M. A., “Optimal multiobjective design of robust power system stabilizers using genetic algorithm,” IEEE Trans. Power Systems, vol. 18, No. 3, pp. 11251132, August 2003.
[45] Ao, Z., Sidhu, T. S., and Fleming, R. J., “Stability investigation of a longitudinal power system and its stabilization by a coordinated application of power system stabilizer,” IEEE Trans. Energy Conversion, vol. 9, No. 3, pp. 466474, September 1994.
[46] Rogers, G. J., “The application of power system stabilizers to a multigenerator plant,” IEEE Trans. Power Systems, vol. 15, No. 1, pp. 350355, February 2000.
[47] Huda, R. M., Iravani, M. R., and Hackarn, R., “Coordinated static VAR compensators and power system stabilizer for damping power system oscillations,” IEEE Trans. Power Systems, vol. PWRS2, No. 4, pp. 10591067, November 1987.
[48] Hughes, F. M., AnayaLara, O., and Jenkins, N., “A power system stabilizer for DFIGbased wind generation,” IEEE Trans. Power Systems, vol. 21, No. 2, pp. 763772, May 2006.
[49] Lee, K. C., “Analysis of power system stabilizers application for controlling poorly damped oscillations in the ALCAN/B. C. hydro power systems,” IEEE Trans. Power Systems, vol. 8, No. 1, pp. 255263, February 1993.
[50] Hagengren, B., and Sandberg, U., “A control center laboratory realistic power system model,” IEEE Trans. Power System, vol. PWRS1, No. 3, pp. 292297, August 1986.
[51] Mingguo, H., Liu, C. C. and Madeleine, G. “Complete controllability of an Nbus dynamic power system model,” IEEE Trans. Circuits and System—I: Fundamental Theory and Applications, vol. 46, No. 6, pp. 700713, June 1999
[52] Zhihua, Q., Dorsey, J. B., and James, D. M. “Application of robust to sustained oscillations in power system,” IEEE Trans. Circuits and System—I: Fundamental Theory and Applications, vol. 39, No. 6, pp. 470476, June 1992.
[53] Stavrakakis, G. S. and Kariniotakis, G. N., “ A general simulation algorithm for the accurate assessment of isolate dieselwind turbines systems interaction, part I: a general multimachine power system model,” IEEE Trans. Energy Conversion, vol. 10, No. 3, pp. 577583, September 1995.
[54] Mingguo, H., and Liu, C. C., ”Complete controllability of a simple dynamic power system model,” IEEE Trans. Circuits and System—I: Fundamental Theory and Applications, vol. 42, No. 8, pp. 491494, June 1995.
[55] Chen, S., and Malik, O. P.,”Power system stabilizer design using μ synthesis,” IEEE Trans. Energy Conversion, vol. 10, No. 1, pp. 175181, March 1995.
[56] H. Okamoto, A. Kurita, J.J. SanchezGasca, K. Clark, N.W. Miller, and J. H. Chow., “Identification of low order linear power system models from EMTP simulations using the steiglitzMcbride algorithm,” IEEE Trans. Power Systems, vol. 13, No. 2, pp. 422427, May 1998.
[57] George Opoku, “Optimal power system VAR planning,” IEEE Trans. Power Systems, vol. 5, No. 1, pp. 5360, February 1990.
[58] Noroosian, M. and Andersson, G., “Damping of power system oscillations by controllable components,” IEEE Trans. Power Delivery, vol. 9, No. 4, pp. 20462054, October 1994.
[59] Carlos, T. C. Max, R. N., Walter, J., and Jose , A. L. B., “Using local models identification to power system stabilizers design at tucurui’s hydroelectric power plant,” IEEE/PES transmission & Distribution conference & Exposition, pp. 16, 2004.
[60] Malik, O. P., Stroev, V. A., Shtrobel, V. A., Hancock, G. C. and Beim, R. S., “Experimental studies with power system stabilizer on a physical model of a multimachine power system,” IEEE Trans. Power Systems, vol. 11, No. 2, pp. 807812, May 1996.
[61] He, J. and Malik, O. P., “An adaptive power system stabilizer based on recurrent neural networks,” IEEE Trans. Energy Conversion, vol. 12, No. 4, pp. 413419, Dec. 1997.
[62] Shamsollahi, P. and Malik, O. P., “Application of neural adaptive power system stabilizer in a multimachine power system,” IEEE Trans. on Energy Conversion, vol. 14, No. 3, pp. 731736, Sept. 1999.
[63] Miguel, R. G., and Malik, O. P., “Power system stabilizer design using an online adaptive neurofuzzy controller with adaptive input link weights,” IEEE Trans. Energy Conversion, vol. 23, No. 3, pp. 914922, September 2008.
[64] Lin, C. H., “Adaptive recurrent fuzzy neural network control for synchronous reluctance motor servo drive,” IEE Proc. Power Applications, vol. 151, No. 6, pp. 711724, November 2004.
[65] You, R., Eghbali, H. J., and Nehrir, M. H., “An online adaptive neurofuzzy power system stabilizer for multimachine systems,” IEEE Trans. Power Systems, vol. 18, No. 1, pp. 128135, February 2003.
[66] Hasanovic, A., and Feliachi, A., “Practical robust PSS design through identification of loworder transfer functions,” IEEE Trans. Power Systems, vol. 19, No. 3, pp. 14921500, August 2004.
[67] Barton, Z., “Robust control in a multimachine power system using adaptive neurofuzzy stabilizers,” IEE Proc. Generating, Transmission, and Distributing, vol. 151, No. 2, pp. 261267, March 2004.
[68] Lukasz A. Machowski and Tshilidzi Marwala., “Using an object oriented calculation process framework and neural networks for classification of image shapes,” International Journal of Innovative Computing, Information and Control ( IJICIC ), vol. 1, No. 4. pp. 609623, Dec. 2005.
[69] Toshihiko Yasuda, Kaazushi Nakamura, Akihro Kawahara and Katsuyuki Tanaka., “Neural network with variable type connection weights for autonomous obstacle avoidance on a prototype of sixwheel type intelligent wheelchair, “ International Journal of Innovative Computing, Information and Control ( IJICIC ), vol. 2, No. 5. pp. 11651177, Oct. 2006.

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