Power System Stabilizer Design Using Compressed Rule Base of Fuzzy Logic Controller

Dhanesh Kumar Sambariya

doi:doi:10.11648/j.jeee.20150303.16

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Power System Stabilizer Design Using Compressed Rule Base of Fuzzy Logic Controller

Dhanesh Kumar Sambariya

Published in Journal of Electrical and Electronic Engineering (Volume 3, Issue 3)

Received: 9 June 2015 Accepted: 18 June 2015 Published: 2 July 2015

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Abstract

In this paper, the fuzzy logic controller (FLC) based power system stabilizer (PSS) with compressed / reduced rule is presented. The FLC rule base is generally based on empirical control rules. In this method, the fuzzy system with a large number of fuzzy rules is compressed to a fuzzy system with a reduced number of rules by removing the redundant and inconsistent rules from the rule base which doesn’t affect the performance of the fuzzy logic controller. The FLC based PSS has two input signals as speed deviation and derivative of speed deviation with an appropriate number of linguistic variables. The number of compressed rules in the rule base through the proposed dominant rule algorithm is reduced to a number as low in the number of selected linguistic variables to represent input and output signals. The application of the FLC with compressed rules as a power system stabilizer (CR-FPSS) is investigated by simulation studies on a single-machine infinite-bus system (SMIB). The superior performance of this compressed rule based fuzzy PSS (CR-FPSS) as compared to conventional PSS and proves the better efficiency of this new CR-FPSS. The reduced CPU computational time and storage space as compared to the fuzzy power system stabilizer (FPSS), proves its applicability in control.

Published in	Journal of Electrical and Electronic Engineering (Volume 3, Issue 3)
DOI	10.11648/j.jeee.20150303.16
Page(s)	52-64
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

Fuzzy Logic Controller, Rule Base Compression, Compressed Rule FPSS, Single Machine Infinite Bus System, Power System Stabilizer, Dominant Fuzzy Rule Compression

References

[1]	P. Kundur, M. Klein, G. J. Rogers, and M. S. Zywno, "Application of power system stabilizers for enhancement of overall system stability," IEEE Transactions on Power Systems, vol. 4, pp. 614-626, 1989. doi:10.1109/59.193836
[2]	E. V. Larsen and D. A. Swann, "Applying Power System Stabilizers Part I: General Concepts," IEEE Transactions on Power Apparatus and Systems, vol. PAS-100, pp. 3017-3024, 1981. doi:10.1109/tpas.1981.316355
[3]	M. Nambu and Y. Ohsawa, "Development of an advanced power system stabilizer using a strict linearization approach," IEEE Transactions on Power Systems, vol. 11, pp. 813-818, 1996. doi:10.1109/59.496159
[4]	S. M. Abd-Elazim and E. S. Ali, "A hybrid Particle Swarm Optimization and Bacterial Foraging for optimal Power System Stabilizers design," International Journal of Electrical Power & Energy Systems, vol. 46, pp. 334-341, 2013. doi:http://dx.doi.org/10.1016/j.ijepes.2012.10.047
[5]	H. M. Soliman, E. H. E. Bayoumi, and M. F. Hassan, "PSO–based Power System Stabilizer for Minimal Overshoot and Control Constraints," Journal of Electrical Engineering, vol. 59, pp. 153–159, 2008. doi:http://iris.elf.stuba.sk/JEEEC/data/pdf/3_108-6
[6]	H. Alkhatib and J. Duveau, "Dynamic genetic algorithms for robust design of multimachine power system stabilizers," International Journal of Electrical Power & Energy Systems, vol. 45, pp. 242-251, 2013. doi:10.1016/j.ijepes.2012.08.080
[7]	S. Panda, "Multi–Objective Non–Dominated shorting Genetic Algorithm–II for Excitation and TCSC–based Controller Design," Journal of Electrical Enginnering, vol. 60, pp. 86-93, 2009. doi:http://iris.elf.stuba.sk/JEEEC/data/pdf/2_109-6
[8]	D. K. Sambariya and R. Prasad, "Robust tuning of power system stabilizer for small signal stability enhancement using metaheuristic bat algorithm," International Journal of Electrical Power & Energy Systems, vol. 61, pp. 229-238, 2014. doi: http://dx.doi.org/10.1016/j.ijepes.2014.03.050
[9]	S. Panda, "Robust coordinated design of multiple and multi-type damping controller using differential evolution algorithm," International Journal of Electrical Power & Energy Systems, vol. 33, pp. 1018-1030, 2011. doi:10.1016/j.ijepes.2011.01.019
[10]	Zong Woo Geem, Joong Hoon Kim, and G. V. Loganathan, "A New Heuristic Optimization Algorithm: Harmony Search," Simulation, vol. 76, pp. 60-68, February 1, 2001 2001. doi:10.1177/003754970107600201
[11]	Z. W. Geem, "Harmony Search Applications in Industry," in Studies in Fuzziness and Soft Computing, Volume 226, Soft Computing Applications in Industry. vol. 226, B. Prasad, Ed., ed: Springer-Verlag Berlin Heidelberg, 2008, pp. 117-134. doi:10.1007/978-3-540-77465-5_6
[12]	D. K. Sambariya and R. Prasad, "Design of Harmony Search Algorithm based tuned Fuzzy logic Power System Stabilizer," International Review of Electrical Engineering (IREE), vol. 8, pp. 1594-1607, October 2013 2013. doi: http://dx.doi.org/10.15866/iree.v8i5.2117
[13]	Y. L. Abdel-Magid, M. A. Abido, and A. H. Mantawy, "Robust tuning of power system stabilizers in multimachine power systems," in IEEE Power Engineering Society Winter Meeting, 2000, p. 1425 vol.2. doi:10.1109/pesw.2000.850180
[14]	M. A. Abido, "Robust design of multimachine power system stabilizers using simulated annealing," IEEE Transactions on Energy Conversion, vol. 15, pp. 297-304, 2000. doi:10.1109/60.875496
[15]	M. A. Abido, "Simulated annealing based approach to PSS and FACTS based stabilizer tuning," International Journal of Electrical Power & Energy Systems, vol. 22, pp. 247-258, 2000. doi:10.1016/s0142-0615(99)00055-1
[16]	M. A. Abido and Y. L. Abdel-Magid, "Optimal Design of Power System Stabilizers Using Evolutionary Programming," IEEE Transactions on Energy Conversion, vol. 17, pp. 429-436, 2002. doi:10.1109/mper.2002.4312476
[17]	S. Mishra, M. Tripathy, and J. Nanda, "Multi-machine power system stabilizer design by rule based bacteria foraging," Electric Power Systems Research, vol. 77, pp. 1595-1607, 2007. doi:10.1016/j.epsr.2006.11.006
[18]	D. K. Sambariya and R. Prasad, "Evaluation of interval type-2 fuzzy membership function & robust design of power system stabilizer for SMIB power system," Sylwan Journal, vol. 158, pp. 289-307, April 2014 2014. doi:http://sylwan.ibles.org/archive.php?v=158&i=5
[19]	D. K. Sambariya and R. Prasad, "Power System Stabilizer design for Multimachine Power System using Interval Type-2 Fuzzy Logic Controller," International Review of Electrical Engineering (IREE), vol. 8, pp. 1556-1565, October 2013 2013. doi: http://dx.doi.org/10.15866/iree.v8i5.2113
[20]	P. S. Bhati and R. Gupta, "Robust fuzzy logic power system stabilizer based on evolution and learning," International Journal of Electrical Power & Energy Systems, vol. 53, pp. 357-366, 2013. doi:http://dx.doi.org/10.1016/j.ijepes.2013.05.014
[21]	T. Hussein, M. S. Saad, A. L. Elshafei, and A. Bahgat, "Robust adaptive fuzzy logic power system stabilizer," Expert Systems with Applications, vol. 36, pp. 12104-12112, 2009. doi:10.1016/j.eswa.2009.04.013
[22]	K. A. El-Metwally and O. P. Malik, "Fuzzy logic power system stabiliser," IEE Proceedings-Generation, Transmission and Distribution, vol. 142, pp. 277-281, 1995. doi:10.1049/ip-gtd:19951748
[23]	K. A. El-Metwally and O. P. Malik, "Application of fuzzy logic stabilisers in a multimachine power system environment," IEE Proceedings-Generation, Transmission and Distribution, vol. 143, pp. 263-268, 1996. doi:10.1049/ip-gtd:19960193
[24]	E. H. Mamdani and S. Assilian, "An experiment in linguistic synthesis with a fuzzy logic controller," International Journal of Man-Machine Studies, vol. 7, pp. 1-13, 1975. doi:http://dx.doi.org/10.1016/S0020-7373(75)80002-2
[25]	N. Hosseinzadeh and A. Kalam, "A rule-based fuzzy power system stabilizer tuned by a neural network," IEEE Transactions on Energy Conversion, vol. 14, pp. 773-779, 1999. doi:10.1109/60.790950
[26]	D. K. Sambariya, R. Gupta, and A. K. Sharma, "Fuzzy Applications to Single Machine Power System Stabilizers," Journal of Theoretical and Applied Information Technology, vol. 5, pp. 317-324, 2009. doi:http://www.jatit.org/volumes/research-papers/Vol5No3/9Vol5No3.pdf
[27]	T. Hiyama, "Application of neural network to real time tuning of fuzzy logic PSS," in Neural Networks to Power Systems, 1993. ANNPS '93., Proceedings of the Second International Forum on Applications of, 1993, pp. 421-426. doi:10.1109/ann.1993.264311
[28]	R. Gupta, D. K. Sambariya, and R. Gunjan, "Fuzzy Logic based Robust Power System Stabilizer for Multi-Machine Power System," in IEEE International Conference on Industrial Technology, ICIT 2006., 2006, pp. 1037-1042. doi:10.1109/icit.2006.372299
[29]	T. Hussein, M. S. Saad, A. L. Elshafei, and A. Bahgat, "Damping inter-area modes of oscillation using an adaptive fuzzy power system stabilizer," Electric Power Systems Research, vol. 80, pp. 1428-1436, 2010. doi:10.1016/j.epsr.2010.06.004
[30]	T. Hussein, A. L. Elshafei, and A. Bahgat, "Design of a hierarchical fuzzy logic PSS for a multi-machine power system," in Mediterranean Conference on Control & Automation (MED '07') 2007, pp. 1-6. doi:10.1109/med.2007.4433681
[31]	M. K. Ciliz, "An advanced tuning methodology for fuzzy control: with application to a vacuum cleaner," in Control Applications, 2003. CCA 2003. Proceedings of 2003 IEEE Conference on, 2003, pp. 257-262 vol.1. doi:10.1109/cca.2003.1223320
[32]	A. Gegov, "Complexity Management in Fuzzy Systems," Springer, Berlin, 2007.
[33]	L. Wang, R. Yang, P. M. Pardalos, L. Qian, and M. Fei, "An adaptive fuzzy controller based on harmony search and its application to power plant control," International Journal of Electrical Power & Energy Systems, vol. 53, pp. 272-278, 2013. doi:http://dx.doi.org/10.1016/j.ijepes.2013.05.015
[34]	A. Gegov and N. Gobalakrishnan, "Advanced Inference in Fuzzy Systems by Rule Base Compression," Mathware & Soft Computing, vol. 14, pp. 201-216, 2007.
[35]	D. K. Sambariya and R. Gupta, "Fuzzy Applications in a Multi-Machine Power System Stabilizer," Journal of Electrical Engineering & Technology, vol. 5, pp. 503-510, 2010. doi:http://www.jeet.or.kr/ltkpsweb/pub/pubfpfile.aspx?ppseq=100
[36]	L. A. Zadeh, "Fuzzy Sets," Information Control, vol. 8, pp. 338-353, 1965.
[37]	D. Flynn, B. W. Hogg, E. Swidenbank, and K. J. Zachariah, "Expert control of a self-tuning automatic voltage regulator," Control Engineering Practice, vol. 3, pp. 1571-1579, 1995. doi:10.1016/0967-0661(95)00167-s
[38]	K. J. Åström and B. Wittenmark, "On self tuning regulators," Automatica, vol. 9, pp. 185-199, 1973. doi:10.1016/0005-1098(73)90073-3
[39]	J. Dombi, "Membership function as an evaluation," Fuzzy Sets and Systems, vol. 35, pp. 1-21, 1990. doi:10.1016/0165-0114(90)90014-w
[40]	Z. Q. Wu, M. Masaharu, and Y. Shi, "An improvement to Kóczy and Hirota's interpolative reasoning in sparse fuzzy rule bases," International Journal of Approximate Reasoning, vol. 15, pp. 185-201, 1996. doi:http://dx.doi.org/10.1016/S0888-613X(96)00054-0
[41]	L. Kóczy and K. Hirota, "Interpolative reasoning with insufficient evidence in sparse fuzzy rule bases," Information Sciences, vol. 71, pp. 169-201, 1993. doi:http://dx.doi.org/10.1016/0020-0255(93)90070-3
[42]	H. Bezine, N. Derbel, and A. M. Alimi, "Fuzzy control of robot manipulators: - some issues on design and rule base size reduction," Engineering Applications of Artificial Intelligence, vol. 15, pp. 401-416, 2002. doi:10.1016/s0952-1976(02)00075-1
[43]	N. Xiong and L. Litz, "Reduction of fuzzy control rules by means of premise learning – method and case study," Fuzzy Sets and Systems, vol. 132, pp. 217-231, 2002. doi:http://dx.doi.org/10.1016/S0165-0114(02)00112-4
[44]	S. Kamalasadan and G. Swann, "A novel power system stabilizer based on fuzzy model reference adaptive controller," in IEEE Power & Energy Society General Meeting (PES '09'), 2009, pp. 1-8. doi:10.1109/pes.2009.5275897
[45]	P. Shamsollahi and O. P. Malik, "Design of a neural adaptive power system stabilizer using dynamic back-propagation method," International Journal of Electrical Power & Energy Systems, vol. 22, pp. 29-34, 2000. doi:10.1016/s0142-0615(99)00032-0
[46]	K. R. Padiyar, Power System Dynamics Stability and Control. Hyderabad, India: BS Publications, 2008.
[47]	M. Ramirez-Gonzalez, R. Castellanos B, and O. P. Malik, "Application of simple fuzzy PSSs for power system stability enhancement of the Mexican Interconnected System," in IEEE Power and Energy Society General Meeting-2010, 2010, pp. 1-8. doi:10.1109/pes.2010.5589526
[48]	F. P. Demello and C. Concordia, "Concepts of Synchronous Machine Stability as Affected by Excitation Control," IEEE Transactions on Power Apparatus and Systems, vol. PAS-88, pp. 316-329, 1969. doi:10.1109/tpas.1969.292452

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Dhanesh Kumar Sambariya. (2015). Power System Stabilizer Design Using Compressed Rule Base of Fuzzy Logic Controller. Journal of Electrical and Electronic Engineering, 3(3), 52-64. https://doi.org/10.11648/j.jeee.20150303.16

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Dhanesh Kumar Sambariya. Power System Stabilizer Design Using Compressed Rule Base of Fuzzy Logic Controller. J. Electr. Electron. Eng. 2015, 3(3), 52-64. doi: 10.11648/j.jeee.20150303.16

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AMA Style

Dhanesh Kumar Sambariya. Power System Stabilizer Design Using Compressed Rule Base of Fuzzy Logic Controller. J Electr Electron Eng. 2015;3(3):52-64. doi: 10.11648/j.jeee.20150303.16

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@article{10.11648/j.jeee.20150303.16,
  author = {Dhanesh Kumar Sambariya},
  title = {Power System Stabilizer Design Using Compressed Rule Base of Fuzzy Logic Controller},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {3},
  number = {3},
  pages = {52-64},
  doi = {10.11648/j.jeee.20150303.16},
  url = {https://doi.org/10.11648/j.jeee.20150303.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20150303.16},
  abstract = {In this paper, the fuzzy logic controller (FLC) based power system stabilizer (PSS) with compressed / reduced rule is presented. The FLC rule base is generally based on empirical control rules. In this method, the fuzzy system with a large number of fuzzy rules is compressed to a fuzzy system with a reduced number of rules by removing the redundant and inconsistent rules from the rule base which doesn’t affect the performance of the fuzzy logic controller.  The FLC based PSS has two input signals as speed deviation and derivative of speed deviation with an appropriate number of linguistic variables. The number of compressed rules in the rule base through the proposed dominant rule algorithm is reduced to a number as low in the number of selected linguistic variables to represent input and output signals. The application of the FLC with compressed rules as a power system stabilizer (CR-FPSS) is investigated by simulation studies on a single-machine infinite-bus system (SMIB). The superior performance of this compressed rule based fuzzy PSS (CR-FPSS) as compared to conventional PSS and proves the better efficiency of this new CR-FPSS. The reduced CPU computational time and storage space as compared to the fuzzy power system stabilizer (FPSS), proves its applicability in control.},
 year = {2015}
}

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TY  - JOUR
T1  - Power System Stabilizer Design Using Compressed Rule Base of Fuzzy Logic Controller
AU  - Dhanesh Kumar Sambariya
Y1  - 2015/07/02
PY  - 2015
N1  - https://doi.org/10.11648/j.jeee.20150303.16
DO  - 10.11648/j.jeee.20150303.16
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 52
EP  - 64
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.20150303.16
AB  - In this paper, the fuzzy logic controller (FLC) based power system stabilizer (PSS) with compressed / reduced rule is presented. The FLC rule base is generally based on empirical control rules. In this method, the fuzzy system with a large number of fuzzy rules is compressed to a fuzzy system with a reduced number of rules by removing the redundant and inconsistent rules from the rule base which doesn’t affect the performance of the fuzzy logic controller.  The FLC based PSS has two input signals as speed deviation and derivative of speed deviation with an appropriate number of linguistic variables. The number of compressed rules in the rule base through the proposed dominant rule algorithm is reduced to a number as low in the number of selected linguistic variables to represent input and output signals. The application of the FLC with compressed rules as a power system stabilizer (CR-FPSS) is investigated by simulation studies on a single-machine infinite-bus system (SMIB). The superior performance of this compressed rule based fuzzy PSS (CR-FPSS) as compared to conventional PSS and proves the better efficiency of this new CR-FPSS. The reduced CPU computational time and storage space as compared to the fuzzy power system stabilizer (FPSS), proves its applicability in control.
VL  - 3
IS  - 3
ER  -

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Dhanesh Kumar Sambariya

Department of Electrical Engineering, Rajasthan Technical University, Kota, India

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Dhanesh Kumar Sambariya. (2015). Power System Stabilizer Design Using Compressed Rule Base of Fuzzy Logic Controller. Journal of Electrical and Electronic Engineering, 3(3), 52-64. https://doi.org/10.11648/j.jeee.20150303.16

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ACS Style

Dhanesh Kumar Sambariya. Power System Stabilizer Design Using Compressed Rule Base of Fuzzy Logic Controller. J. Electr. Electron. Eng. 2015, 3(3), 52-64. doi: 10.11648/j.jeee.20150303.16

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AMA Style

Dhanesh Kumar Sambariya. Power System Stabilizer Design Using Compressed Rule Base of Fuzzy Logic Controller. J Electr Electron Eng. 2015;3(3):52-64. doi: 10.11648/j.jeee.20150303.16

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@article{10.11648/j.jeee.20150303.16,
  author = {Dhanesh Kumar Sambariya},
  title = {Power System Stabilizer Design Using Compressed Rule Base of Fuzzy Logic Controller},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {3},
  number = {3},
  pages = {52-64},
  doi = {10.11648/j.jeee.20150303.16},
  url = {https://doi.org/10.11648/j.jeee.20150303.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20150303.16},
  abstract = {In this paper, the fuzzy logic controller (FLC) based power system stabilizer (PSS) with compressed / reduced rule is presented. The FLC rule base is generally based on empirical control rules. In this method, the fuzzy system with a large number of fuzzy rules is compressed to a fuzzy system with a reduced number of rules by removing the redundant and inconsistent rules from the rule base which doesn’t affect the performance of the fuzzy logic controller.  The FLC based PSS has two input signals as speed deviation and derivative of speed deviation with an appropriate number of linguistic variables. The number of compressed rules in the rule base through the proposed dominant rule algorithm is reduced to a number as low in the number of selected linguistic variables to represent input and output signals. The application of the FLC with compressed rules as a power system stabilizer (CR-FPSS) is investigated by simulation studies on a single-machine infinite-bus system (SMIB). The superior performance of this compressed rule based fuzzy PSS (CR-FPSS) as compared to conventional PSS and proves the better efficiency of this new CR-FPSS. The reduced CPU computational time and storage space as compared to the fuzzy power system stabilizer (FPSS), proves its applicability in control.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Power System Stabilizer Design Using Compressed Rule Base of Fuzzy Logic Controller
AU  - Dhanesh Kumar Sambariya
Y1  - 2015/07/02
PY  - 2015
N1  - https://doi.org/10.11648/j.jeee.20150303.16
DO  - 10.11648/j.jeee.20150303.16
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 52
EP  - 64
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.20150303.16
AB  - In this paper, the fuzzy logic controller (FLC) based power system stabilizer (PSS) with compressed / reduced rule is presented. The FLC rule base is generally based on empirical control rules. In this method, the fuzzy system with a large number of fuzzy rules is compressed to a fuzzy system with a reduced number of rules by removing the redundant and inconsistent rules from the rule base which doesn’t affect the performance of the fuzzy logic controller.  The FLC based PSS has two input signals as speed deviation and derivative of speed deviation with an appropriate number of linguistic variables. The number of compressed rules in the rule base through the proposed dominant rule algorithm is reduced to a number as low in the number of selected linguistic variables to represent input and output signals. The application of the FLC with compressed rules as a power system stabilizer (CR-FPSS) is investigated by simulation studies on a single-machine infinite-bus system (SMIB). The superior performance of this compressed rule based fuzzy PSS (CR-FPSS) as compared to conventional PSS and proves the better efficiency of this new CR-FPSS. The reduced CPU computational time and storage space as compared to the fuzzy power system stabilizer (FPSS), proves its applicability in control.
VL  - 3
IS  - 3
ER  -

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