nach oben

Neural Computing and Applications

Erschienen in:

25.07.2016 | Original Article

Simulation of novel hybrid method to improve dynamic responses with PSS and UPFC by fuzzy logic controller

verfasst von: Mehrdad Khaksar, Alireza Rezvani, Mohammad Hassan Moradi

Erschienen in: Neural Computing and Applications | Ausgabe 3/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In this paper, a hybrid method is proposed to damp frequency and power oscillations in the power system equipped with unified power flow controller (UPFC) and power system stabilizer (PSS) controllers. The method is robust with respect to operating point’s changes. This hybrid method consists of two stages: offline and online. In the offline stage, the coefficients of PSS and UPFC controllers for different operating points have been found by PSO algorithm; then in the second stage, online new fuzzy controller is proposed to select the best PSS and UPFC coefficients according to operating point. The proposed method is simulated for single machine infinite bus system-associated PSS and UPFC for three different operating points in MATLAB software, and results of proposed method simulation are investigated and compared with conventional PSS (CPSS) + UPFC, CPSS controllers. Simulation results show that the proposed method has a better performance.

Vorheriger Artikel Biomimicry of plant root growth using bioinspired foraging model for data clustering

Nächster Artikel Security-constrained optimal power flow with wind and thermal power generators using fuzzy adaptive artificial physics optimization algorithm

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Kundur P (1994) Power system stability and control. EPRI power system engineering series. Mc Graw-Hill, New York

Anderson PM, Fouad AA (1994) Power system control and stability. IEEE Press, New York

Abd-Elazim SM, Ali ES (2013) A hybrid particle swarm optimization and bacterial foraging for optimal power system stabilizers design. Int J Electr Power Energy Syst 46(1):334–341CrossRef

Mostafa HE (2012) Design and allocation of power system stabilizers using the particle swarm optimization technique for an interconnected power system. Int J Electr Power Energy Syst 34(1):57–65MathSciNetCrossRef

Eslami M, Shareef H, Azah M, Khajehzadeh M (2012) An efficient particle swarm optimization technique with chaotic sequence for optimal tuning and placement of PSS in power systems. Int J Electr Power Energy Syst 43:1467–1478CrossRef

Safari A (2013) A PSO procedure for a coordinated tuning of power system stabilizers for multiple operating conditions. Int J Electr Power Energy Syst 11:665–673

Ragavendiran A, Gnanadass R, Ramakrishnan K (2015) A new SPARLS algorithm for tuning power system stabilizer. Int J Electr Power Energy Syst 68:327–335CrossRef

Tayal VK, Lather JS (2015) Reduced order H∞ TCSC controller & PSO optimized fuzzy PSS design in mitigating small signal oscillations in a wide range. Int J Electr Power Energy Syst 68:123–131CrossRef

Peres W, de Oliveira EJ, Passos Filho JA, da Silva Junior IC (2015) Coordinated tuning of power system stabilizers using bio-inspired algorithms. Int J Electr Power Energy Syst 64:419–428CrossRef

10.

Ellithy K, Said S, Kahlout O (2014) Design of power system stabilizers based on μ-controller for power system stability enhancement. Int J Electr Power Energy Syst 63:933–939CrossRef

11.

Soliman M, Elshafei AL, Bendary F, Mansour W (2010) Robust decentralized PID based power system stabilizer design using an ILMI approach. Electr Power Syst Res 80:1488–1497CrossRef

12.

Ramakrishna G, Malik OP (2010) Adaptive PSS using a simple on-line identifier and linear pole-shift controller. Electr Power Syst Res 80:406–416CrossRef

13.

Sambariya DK, Prasad R (2014) Robust tuning of power system stabilizer for small signal stability enhancement using metaheuristic bat algorithm. Int J Electr Power Energy Syst 61:229–238CrossRef

14.

Soliman M (2015) Robust non-fragile power system stabilizer. Int J Electr Power Energy Syst 2015(64):626–634CrossRef

15.

Bhati PS, Gupta R (2013) Robust fuzzy logic power system stabilizer based on evolution and learning. Int J Electr Power Energy Syst 53:357–366CrossRef

16.

Alkhatib H, Duveau J (2013) Dynamic genetic algorithms for robust design of multimachine power system stabilizers. Int J Electr Power Energy Syst 45:242–251CrossRef

17.

Nechadi E, Harmas MN, Hamzaoui A, Essounbouli N (2012) A new robust adaptive fuzzy sliding mode power system stabilizer. Int J Electr Power Energy Syst 42:1–7CrossRef

18.

de Campos VAF, da Cruz JJ, Zanetta LC Jr (2012) Robust and optimal adjustment of power system stabilizers through linear matrix inequalities. Int J Electr Power Energy Syst 42:478–486CrossRef

19.

Khodabakhshian A, Hemmati R (2012) Robust decentralized multi-machine power system stabilizer design using quantitative feedback theory. Int J Electr Power Energy Syst 41:112–119CrossRef

20.

Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353CrossRefMATH

21.

Ying H (2000) Fuzzy control and modeling: analytical foundation and applications. IEEE Press, New YorkCrossRef

22.

Sharma D (2011) Designing and modeling fuzzy control systems. Int J Comput Appl 16:1

23.

Wang LX (1997) A course in fuzzy systems and control. Prentice Hall PTR, Upper Saddle RiverMATH

24.

Driankov D, Hellendoorn H, Reinfrank M (1993) An introduction to fuzzy control. Springer, New YorkCrossRefMATH

25.

Hingorani NG, Gyugyi L (2000) Understanding FACTS. IEEE Press, New York

26.

Simfukwe DD, Pal BC, Jabr RA, Martins N (2012) Robust and low-order design of flexible ac transmission systems and power system stabilisers for oscillation damping. Inst Eng Technol 6:445–452

27.

Eslami M, Shareef H, Mohd RT, Khajehzadeh M (2014) Adaptive particle swarm optimization for simultaneous design of UPFC damping controllers. Int J Electr Power Energy Syst 57:116–128CrossRef

28.

Hashemi A, Tabatabaei NM, Taheri N, Naderi S (2012) A flexible neural controller for transmission systems equipped with UPFC. Int J Tech Phys Probl Eng (IJTPE) 4(3):167–176

29.

Sedighizadeh M, Minooie B, Sarvi M (2013) Power system stability enhancement using a NSPSO designed UPFC damping controller. Int J Tech Phys Probl Eng 5(4):1–8

30.

Hassan L, Moghavvemi M, Almurib HF (2012) Modeling UPFC into multi-machine power systems. Arabian J Sci Eng 37(6):1613–1624CrossRef

31.

Shayeghi H, Safari A (2009) Simultaneous coordinated designing of UPFC and PSS output feedback controllers using PSO. J Electr Eng 60(4):177–184

32.

Tambey N, Kothari ML (2003) Damping of power system oscillations with unified power flow controller. IEE Proc Gener Transm Distrib 150(2):129–140CrossRef

33.

Kennedy J, Eberhart R (1995) Particle swarm optimization. Proc IEEE Intl Conf Neural Netw 4:1942–1948CrossRef

34.

Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization an overview. Int J Swarm Intell 1:33–57CrossRef

35.

Shivakumar R, Lakshmipathi R (2010) Power system stability enhancement using bio inspired genetic and PSO algorithm implementation. Int Rev Electr Eng (IREE) 5(4):1609–1615

36.

Shaheen HI, Rashed GI, Cheng SJ (2010) Application and comparison of computational intelligence techniques for optimal location and parameter setting of UPFC. Eng Appl Artif Intell 23(2):203–216CrossRef

37.

Abido MA (2001) Particle swarm optimization for multimachine power system stabilizer design. IEEE Power Eng Soc Summer Meet 3:1346–1351CrossRef

38.

Wang LX (1997) A course in fuzzy systems and control. Prentice Hall PTR, Upper Saddle RiverMATH

39.

Driankov D, Hellendoorn H, Reinfrank M (1993) An introduction to fuzzy control. Springer, New YorkCrossRefMATH

40.

Rezvani A, Gandomkar M (2016) Modeling and control of grid connected intelligent hybrid photovoltaic system using new hybrid fuzzy-neural method. Sol Energy 127:1–18CrossRef

41.

Rezvani A, Khalili A, Mazareie A, Gandomkar M (2016) Modeling, control, and simulation of grid connected intelligent hybrid battery/photovoltaic system using new hybrid fuzzy-neural method. ISA Trans. doi:10.1016/j.isatra.2016.02.013

42.

Rezvani A, Gandomkar M (2016) Simulation and control of intelligent photovoltaic system using new hybrid fuzzy-neural method. Neural Comput Appl. doi:10.1007/s00521-016-2210-2

43.

Uyar O, Çunkaş M (2013) Fuzzy logic-based induction motor protection system. Neural Comput Appl 23(1):31–40CrossRef

44.

Hamza MF, Yap HJ, Choudhury IA (2015) Recent advances on the use of meta-heuristic optimization algorithms to optimize the type-2 fuzzy logic systems in intelligent control. Neural Comput Appl. doi:10.1007/s00521-015-2111-9

Titel: Simulation of novel hybrid method to improve dynamic responses with PSS and UPFC by fuzzy logic controller
verfasst von: Mehrdad Khaksar
Alireza Rezvani
Mohammad Hassan Moradi
Publikationsdatum: 25.07.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 3/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2487-1

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 3/2018

Symbiotic organisms search optimization algorithm for economic/emission dispatch problem in power systems

Particle classification optimization-based BP network for telecommunication customer churn prediction

RBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW)

Image salient regions encryption for generating visually meaningful ciphertext image

Time series forecasting by recurrent product unit neural networks

A quantum particle swarm optimization driven urban traffic light scheduling model