International Journal of Energy and Power Engineering

| Peer-Reviewed |

Online Tuning of Power System Stabilizers Using Fuzzy Logic Network with Fuzzy C-Means Clustering Prediction: A Case Study

Received: 26 August 2014    Accepted: 16 September 2014    Published: 30 September 2014
Views:       Downloads:

Share This Article

Abstract

Power system stabilizers (PSS) has been widely used to enhance damping due to the electromechanical low frequency oscillations occurrence in power systems. In this paper, a new method is used for the online tuning of parameters of conventional power system stabilizers (CPSS) using fuzzy logic. Fuzzy logic enables mathematical modeling and computation of some nonlinear parameters of the system, which are usually derived empirically by utilization of expert knowledge rules. Various literatures has shown that fuzzy logic controller is one of the most useful methods for expert knowledge utilization. This type of controller is adaptive in nature and can be used successfully as a power system stabilizer. The design of fuzzy logic controllers is mainly based on fuzzy rules and input/output membership functions. Simple and efficient clustering algorithms allow data classification in distinct groups using distance and/or similarity functions. In the present paper, the optimum generation of fuzzy rules base using Fuzzy C-means (FCM) clustering technique is used. In fact, data are classified and the number of fuzzy rules which depends on convergence radius is determined. Finally, the performance of proposed FCM controller is compared with that of conventional controller. The active power, reactive power and bus voltages used as inputs to the fuzzy logic network based power system stabilizer and the parameters of the optimum stabilizer , i.e. gain factor as well as time constants of the lead/lag compensator, are the outputs of the proposed system. The design method has been successfully implemented on a single machine power system connected to an infinite bus over various operating conditions.

DOI 10.11648/j.ijepe.20140305.11
Published in International Journal of Energy and Power Engineering (Volume 3, Issue 5, October 2014)
Page(s) 217-227
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Dynamic Stabilizer, Power System Stabilizers, Online Tuning of Parameters, Fuzzy C-Means Clustering Prediction

References
[1] S.M. Radaideh , I.M. Nejdawi , M.H. Mushtaha," Design of power system stabilizers using two level fuzzy and adaptive neuro-fuzzy inference systems ", Electrical Power and Energy Systems Vol 35 pp . 47–56(2012)
[2] Hardiansyah, Furuye Seizo, Irisawa Juichi. A robust power system stabilizer design using reduced-order models. Electr Power Energy Syst 2006;28:21–8.
[3] Tse CT, Tso SK. Refinement of conventional PSS design in multimachine system by modal analysis. IEEE Trans Power Syst 1993;8(2).
[4] T. Hussein, M.S. Saad , A.L. Elshafei, A. Bahgat ," Robust adaptive fuzzy logic power system stabilizer",Expert Systems with Applications 36 pp 12104–12112 (2009)
[5] E.V. Larsen, D.A. Swann, “Applying power system stabilizers, Part I, II, III”, IEEE Transaction on Power Apparatus and Systems (PAS) Vol.100, No. 6, pp. 3017-3041, 1981.
[6] S.S. Lee, J.K. Park, “Design of reduced-order observer-based variable structure power system stabilizer for unmeasurable state variables”, in: IEE Proceedings of the Generation, Transmission and Distribution, Vol. 145, No. 5, pp. 525–530, 1998.
[7] K.A. El-Metwally, G.C. Hancock, O.P. Malik, “Implementation of a fuzzy logic PSS using a micro-controller and experimental test results”, IEEE Transaction on Energy Conversion, Vol. 11, No. 1, pp. 91-96, 1996.
[8] Y.Y. Hsu, C.L. Chen, “Tuning of power system stabilizers using an artificial neural network”, IEEE Transaction on Energy Conversion, Vol. 6, No. 4, pp. 612-619, 1991.
[9] Y. Park,M. Choi, K.Y. Lee, “A neural network-based power system stabilizer using power flow characteristics”, IEEE Transactions on Energy Conversion, Vol. 11, No. 2, pp. 435–441, 1996.
[10] Y. Zhang, O.P.Malik, G.P. Chen, “Artificial neural network power system stabilizers in multi-machine power systemenvironment”, IEEE Transactions on Energy Conversion, Vol. 10, No. 1, pp. 147–155, 1995.
[11] B. Changaroon, S.C. Srivastava, D. Thukaram, “A neural network based power system stabilizer suitable for on-line training—a practical case study for EGAT system”, IEEE Transactions on Energy Conversion, Vol. 15, No. 1, pp. 103–109, 2000.
[12] Z.Bouchama, M.N.Harmas," Optimal robust adaptive fuzzy synergetic power system stabilizer design",Vol 83 ,pp 170-175 (2012)
[13] K.R. Sudha , Y. ButchiRaju, A. Chandra Sekhar," Fuzzy C-Means clustering for robust decentralized load frequency controlof interconnected power system with Generation Rate Constraint" ,Electrical Power and Energy Systems Vol 37 ,pp. 58–66 (2012)
[14] N.Hossein-Zadeh,A.Kalam,"An indirect adaptive fuzzy-logic power system stabilizer"Electrical Power and Energy Systems vol. 24 pp 837-842 (2002)
[15] Hossam E.A. Talaat , Adel Abdennour, Abdulaziz A. Al-Sulaiman “Design and experimental investigation of a decentralized GA-optimized neuro-fuzzy power system stabilizer” Electrical Power and Energy Systems vol. 32 pp.751_759 (2010)
[16] Z.Bouchama, M.N.Harmas ," Optimal robust adaptive fuzzy synergetic power system stabilizer design", Electric Power Systems Research, Vol 83 pp 170–175(2012)
[17] D.K.Sambariya ,Rajendra Prasad , "Robust Power System Stabilizer Design for Single Machine Infinite Bus System with Different Membership Functions for Fuzzy Logic Controller",IEEE(2012)
[18] Jenica Ileana Corcau, EleonorStoenescu," Fuzzy logic controller as a power system stabilizer ",INTERNATIONAL JOURNAL OF CIRCUITS, SYSTEMS AND SIGNAL PROCESSING Vol 1, pp 266-273(2007)
[19] Dr. Jagdishkumar , P.Pavankumar, Aeidapu Mahesh and AnkitShrivastava Department of Electrical Engineering PEC University of Technology, Chandigarh , Power System Stabilizer Based On Artificial Neural Network , IEEE(2011)
[20] P. W. Sauer and M. A. Pai, Power System Dynamics and Stability, Prentice-Hall, Inc., New Jersey, 1998.
[21] G. Lindfield and J. Penny, Numerical Methods using MATLAB, Ellis Horwood Limited, 1995.
[22] P. M. Anderson and A. A. Foaud, Power System Control and Stability, Ames: Iowa State Univ. Press, 1977.
[23] K.R.Padiyar,Power System Dynamics
Cite This Article
  • APA Style

    Mohammad Hajizade Kanafgorabi, Ali Karami. (2014). Online Tuning of Power System Stabilizers Using Fuzzy Logic Network with Fuzzy C-Means Clustering Prediction: A Case Study. International Journal of Energy and Power Engineering, 3(5), 217-227. https://doi.org/10.11648/j.ijepe.20140305.11

    Copy | Download

    ACS Style

    Mohammad Hajizade Kanafgorabi; Ali Karami. Online Tuning of Power System Stabilizers Using Fuzzy Logic Network with Fuzzy C-Means Clustering Prediction: A Case Study. Int. J. Energy Power Eng. 2014, 3(5), 217-227. doi: 10.11648/j.ijepe.20140305.11

    Copy | Download

    AMA Style

    Mohammad Hajizade Kanafgorabi, Ali Karami. Online Tuning of Power System Stabilizers Using Fuzzy Logic Network with Fuzzy C-Means Clustering Prediction: A Case Study. Int J Energy Power Eng. 2014;3(5):217-227. doi: 10.11648/j.ijepe.20140305.11

    Copy | Download

  • @article{10.11648/j.ijepe.20140305.11,
      author = {Mohammad Hajizade Kanafgorabi and Ali Karami},
      title = {Online Tuning of Power System Stabilizers Using Fuzzy Logic Network with Fuzzy C-Means Clustering Prediction: A Case Study},
      journal = {International Journal of Energy and Power Engineering},
      volume = {3},
      number = {5},
      pages = {217-227},
      doi = {10.11648/j.ijepe.20140305.11},
      url = {https://doi.org/10.11648/j.ijepe.20140305.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20140305.11},
      abstract = {Power system stabilizers (PSS) has been widely used to enhance damping due to the electromechanical low frequency oscillations occurrence in power systems. In this paper, a new method is used for the online tuning of parameters of conventional power system stabilizers (CPSS) using fuzzy logic. Fuzzy logic enables mathematical modeling and computation of some nonlinear parameters of the system, which are usually derived empirically by utilization of expert knowledge rules. Various literatures has shown that fuzzy logic controller is one of the most useful methods for expert knowledge utilization. This type of controller is adaptive in nature and can be used successfully as a power system stabilizer. The design of fuzzy logic controllers is mainly based on fuzzy rules and input/output membership functions. Simple and efficient clustering algorithms allow data classification in distinct groups using distance and/or similarity functions. In the present paper, the optimum generation of fuzzy rules base using Fuzzy C-means (FCM) clustering technique is used. In fact, data are classified and the number of fuzzy rules which depends on convergence radius is determined. Finally, the performance of proposed FCM controller is compared with that of conventional controller. The active power, reactive power and bus voltages used as inputs to the fuzzy logic network based power system stabilizer and the parameters of the optimum stabilizer , i.e. gain factor as well as time constants of the lead/lag compensator, are the outputs of the proposed system. The design method has been successfully implemented on a single machine power system connected to an infinite bus over various operating conditions.},
     year = {2014}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Online Tuning of Power System Stabilizers Using Fuzzy Logic Network with Fuzzy C-Means Clustering Prediction: A Case Study
    AU  - Mohammad Hajizade Kanafgorabi
    AU  - Ali Karami
    Y1  - 2014/09/30
    PY  - 2014
    N1  - https://doi.org/10.11648/j.ijepe.20140305.11
    DO  - 10.11648/j.ijepe.20140305.11
    T2  - International Journal of Energy and Power Engineering
    JF  - International Journal of Energy and Power Engineering
    JO  - International Journal of Energy and Power Engineering
    SP  - 217
    EP  - 227
    PB  - Science Publishing Group
    SN  - 2326-960X
    UR  - https://doi.org/10.11648/j.ijepe.20140305.11
    AB  - Power system stabilizers (PSS) has been widely used to enhance damping due to the electromechanical low frequency oscillations occurrence in power systems. In this paper, a new method is used for the online tuning of parameters of conventional power system stabilizers (CPSS) using fuzzy logic. Fuzzy logic enables mathematical modeling and computation of some nonlinear parameters of the system, which are usually derived empirically by utilization of expert knowledge rules. Various literatures has shown that fuzzy logic controller is one of the most useful methods for expert knowledge utilization. This type of controller is adaptive in nature and can be used successfully as a power system stabilizer. The design of fuzzy logic controllers is mainly based on fuzzy rules and input/output membership functions. Simple and efficient clustering algorithms allow data classification in distinct groups using distance and/or similarity functions. In the present paper, the optimum generation of fuzzy rules base using Fuzzy C-means (FCM) clustering technique is used. In fact, data are classified and the number of fuzzy rules which depends on convergence radius is determined. Finally, the performance of proposed FCM controller is compared with that of conventional controller. The active power, reactive power and bus voltages used as inputs to the fuzzy logic network based power system stabilizer and the parameters of the optimum stabilizer , i.e. gain factor as well as time constants of the lead/lag compensator, are the outputs of the proposed system. The design method has been successfully implemented on a single machine power system connected to an infinite bus over various operating conditions.
    VL  - 3
    IS  - 5
    ER  - 

    Copy | Download

Author Information
  • Dept. of Electrical Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran

  • Dept. of Electrical Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran

  • Sections