Transient stability improvement with neuro-fuzzy control of GUPFC in multi machine system

Abstract

In recent years, because of the regeneration and development of power systems many of their technical and economical characteristics have been changed in different sections of generation, transmission, distribution and consumption. This issue has great impact on transmission systems because of the increased demand and limitation in creation of new lines. System stability against disturbances of big signal is one of the important issues that have been under threat as a result of this issue. In such circumstances, FACTS devices have great impact on increase of control ability and power system stability. One of the most effective FACTS devices is generalized unified power flow controller (GUPFC). It can combine the capabilities of SSSC, STATCOM and TCPAR by control of different parameters of network and can be used as a multipurpose tool. In this essay, designing a neuro-fuzzy controller for GUPFC to improve transient stability has been investigated. For this purposed, a controllable compensator has been designed to increase transient stability margin and damp transient oscillation in such systems by use of lyapunov stability criterion. Since transient energy function of system is a suitable tool for investigating of stability issue, optimization of GUPFC energy function has been noticed in order to reach the highest margin of transient stability. This idea is the basis of producing required teaching information in ANFIS network and can be used as a GUPFC controller. In this essay, the impact of GUPFC on single machine system, infinite bus (SMIB) and 9-bus system (Anderson and Fouad, 1977) by use of supposed method has been studied. Moreover by simulation of other FACTS devices such as UPFC and IPFC, the priority of GUPFC has been shown and the comparison of its result has been proved.

Keywords

Generalized unified power flow controller (GUPFC)transient stability lyapunov energy function critical cleaning time (CCT)neuro-fuzzy control

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References

Fardanesh

, Shperling

, Uzunovic

and Zelingher

, Multi-converter FACTS devices: the generalized unified power flow controller (GUPFC) Power Engineering Society Summer Meeting (Cat. No.00CH4), 2 (2000), 1020–1025.

Zhang

X.-P.

, Handschin

and Yao

, Modeling of the generalized unified power flow controller (GUPFC) in a nonlinear interior point OPFPICA Innovative Computing for Power - Electric Energy Meets the Market. 22nd IEEE Power Engineering Society. International Conference on Power Industry Computer Applications (Cat.No.01CH5), (2001), 283–286.

Sun

Lin

, Mei

Shengwei

, Lu

Qiang

and Ma

Jin

, Application of GUPFC in China’s Sichuan power grid - modeling, control strategy and case study 2003, IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH1) 1 (2003), 175–181.

Zhang

X.-P.

, Modelling of the interline power flow controller and the generalised unified power flow controller in Newton power flowIEE Proceedings - Generation, Transmission and Distribution 150(3) (2003), 268–274.

Azbe

and Mihalic

, Damping of power-system oscillations with the application of a GUPFC 2009, IEEE Bucharest PowerTech (2009), 1–6.

Kumar

Ashwani

and Sekhar

Charan

, Congestion management based on demand management and impact of GUPFCInternational Conference on Power, Signals, Controls and Computation (2012), 1–6.

Brochu

, Pelletier

, Beauregard

and Morin

, The interphase power controller: A new concept for managing power flow within AC networks, IEEE Transactions on Power Delivery 9(2) (1994), 833–841.

SyafutraLubis

Rakhmad

, PramonoHadi

Sasongko

and Tumiran , Modeling of the generalized unified power flow controller for optimal power flow Proceedings of the International Conference on Electrical Engineering and Informatics, (2011), 1–6.

SyafutraLubis

Rakhmad

, Modeling and simulation of generalized unified power flow controller (GUPFC) 2nd International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (2011), 207–213.

10.

Kumar

Ashwani

, Comon of IPFC and GUPFC for congestion management in deregulated electricity markets 2014 9th International Conference on Industrial and Information Systems (ICIIS), (2014)1–7.

11.

Syafutra Lubis

Rakhmad

, PramonoHadi

Sasongko

and Tumiran , Using the UPFC and GUPFC Controllers to Maximize Available Transfer Capability (ATC), International Journal on Electrical Engineering and Informatics, 6(2) (2014).

12.

Singh

J.G.

, Teripathy

, Singh

S.N.

and Srivastava

S.C.

, Development of a fuzzy rule based generalized unified power flow controller, European Transactions On Electrical Power Euro. Trans. Electr. Power 19 (2009), 702–717.

13.

Azbe

and Mihalic

, Damping of power-system oscillations with the application of a GUPFC, IEEE Bucharest Power Tech (2009), 1–6.

14.

Raja Reddy

and Venkata Prasanth

Dr. B.

, Comarision Of Distributed Power-Flow Controller (DPFC) And Generalized Unified Power Flow Controller (GUPFC) In Power Quality Improvement, International Journal Of Innovative Research In Electrical, Electronics, Instrumentation And Control Engineering 3(4) (2015).

15.

Valle

D.B.

and Araujo

P.B.

, The influence of GUPFC FACTS device on small signal stability of the electrical power systems, Electrical Power and Energy Systems 65 (2015), 299–306.

16.

Ali Marjanian

Ehsanjafari

, Solaymani

Soodabeh

and Shahgholian

Ghazanfar

, Designing an Emotional Intelligent Controller for IPFC to Improve the Transient Stability Based on ergy Function, J Electr Eng Technol 8(3) (2013), 478–489.

17.

Marjanian

Ali

, Jafari

Ehsan

and Barati

Hasan

, Designing of a TEF-based neuro-fuzzy controller for IPFC to improve the transient stability, Euro. Trans. Electr. Power (2012).

18.

Long Vu

Thanh

and Turitsyn

Konstantin

, Lyapunov Functions Family Approach to Transient Stability Assessment, IEEE Transactions on Power Systems 31(2) (2016), 1269–1277.

19.

E-Saify

M.H.

, El-Garhy

A.M.

and El-Sheikh

G.A.

, Brain Emotional Learning Based Intelligent Decoupler for Nonlinear Multi-Input Multi-Output Distillation Columns, Mathematical Problems in Engineering 2017.

20.

Milasi

R.M.

, Lucas

and Araabi

B.N.

, Intelligent modeling and control of washing machine using LLNF modeling and modified BELBIC, International Conference on Control and Automation 2 (2005), 812–817.

21.

Abootorabi

, Zarchi

E. Daryabeigi

, Arab Markadeh

Gh. R.

and Soltani

, Emotional controller (BELBIC) based DTC for encoder less Synchronous Reluctance Motor drives 2nd Power Electronics, Systems and Technologies Conference (2011), 478–483.

22.

Daryabeigi

, Sadeghi

, Arab Markadeh

G.R.

and Lucas

, Speed control of brushless DC motors using emotional intelligent controller, IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society (2010), 2920–2925.

23.

Daryabeigi

, Arab markadeh

Gh.

and Lucas

, Simultaneously, speed and flux control of a induction motor, with brain emotional learning based intelligent controller (BELBIC), 2009 IEEE International Electric Machines and Drives Conference (2009), 894–901.

24.

El-Garhy

Ahmed M.

and El-Shimy

Mohamed E.

, BELBIC for MRAS with highly non-linear process, Alexandria Engineering Journal 54(1) (2015), 7–16.

25.

Sobha

, kumar

R.S.

and George

, “ANFIS based UPFC supplementary controller for damping low frequency oscillations in power systems”, JES 2007 on-line:http://journal.esrgroups.org/jes/.

26.

Sadeghzadeh

S.M.

and Ansarian

, “Transient Stability Improvement with Neuro- Fuzzy Control of FACTS Devices”, 1st International Power and Energy Conf. PECon 2006 (2006), pp.297–302.

27.

Azbe

, Gabrijel

, Povh

and Mihalic

, “The Energy Function of a General Multimachine System With a Unified Power Flow Controller”, IEEE Trans. Power Systems 20(3) (2005).

28.

Chamia

“Thyristor-Controlled Series Capacitor Design and Field Test”, EPRI Flexible AC Transmission System (FACTS) Conf.May 2013.

29.

Cutsem

Th. V.

and Ribbens-Pavella

, “Structure preserving direct methods for transient stability analysis of power systems”, Proc. 24th Conf. on Decision and Control, pp.70–77, Dec.2013.