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.
| Published in | International Journal of Energy and Power Engineering (Volume 3, Issue 5) |
| DOI | 10.11648/j.ijepe.20140305.11 |
| 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), 2014. Published by Science Publishing Group |
Dynamic Stabilizer, Power System Stabilizers, Online Tuning of Parameters, Fuzzy C-Means Clustering Prediction
| [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 |
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
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
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
Share This Article
Twitter
Linked In
Facebook
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}
}
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 -
Email: