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Robustness Analysis of LFC for Multi Area Power System integrated with SMES–TCPS by Artificial Intelligent Technique

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Abstract

Load frequency control (LFC) plays an essential role in a power system (PS) to sustain the grid frequency for the period of sudden load demand variations. Hence, this manuscript deals with the execution of adaptive neuro fuzzy inference system (ANFIS) approach for LFC of three-area unequal thermal power system. The ANFIS controller proposed in the manuscript combines the advantages of Fuzzy Logic Control (FLC) as well as rapid response and flexible nature of artificial neural network. To improve the LFC performance, offered controller is simulated with superconducting magnetic energy storage (SMES) units and Thyristor controlled phase shifter (TCPS) individually and in combination. The performance of proposed ANFIS controller is superior and robust compared to existing control schemes and improved performance is observed particularly in the presence of SMES–TCPS combination. The realization of SMES & TCPS combination curtail frequency and tie power variation quickly after an unexpected load disturbance. To validate the usefulness of the proposed controller, integral time weighted absolute error (ITAE) and integral square error (ISE) performance error indices are used. Robustness of offered controller is demonstrated against wide variation in the system parameters.

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Abbreviations

TAUTPS:

Three area unequal thermal power system

ACE:

Area control error

LDV:

Load demand variation

∆F:

Change in frequency

∆PTie-line :

Change in tie-line power

i :

Subscript referring to area (i = 1, 2, 3)

∆P Mi :

Mechanical power input to power system

∆P Li :

Variations in load demand

∆P Gi :

Changes in governor power

∆Ptie,i :

Tie line power deviation in ith area

H i :

Equivalent inertia constant of area i

Tij :

Synchronizing coefficient for tie-line

∆f j :

Frequency deviations in jth control area

R i :

Droop characteristics of area i

β i :

Frequency bias constant of area i

D i :

Equivalent damping constant of area i

T Gi :

Time constant of governor for area i

T Ti :

Time constant of turbine for area i

a 12 :

Participation factor between ith and jth areas

References

  1. Iracleous DP, Alexandridis AT (2005) A multi-task automatic generation control for power regulation. Electr Power Syst Res 73(3):275–285

    Article  Google Scholar 

  2. Oysal Yusuf (2005) A comparative study of adaptive load frequency controller designs in a power system with dynamic neural network models. Energy Convers Manag 46(15–16):2656–2668

    Article  Google Scholar 

  3. Talaq Jawad, Al-Basri Fadel (1999) Adaptive fuzzy gain scheduling for load frequency control. IEEE Trans Power Syst 14(1):145–150

    Article  Google Scholar 

  4. Aravindan P, Sanavullah MY (2009) Fuzzy logic based automatic load frequency control of two area power system with GRC. Int J Comput Intell. 5:37–44

    Google Scholar 

  5. Khunti Swasti R, Panda Sidhartha (2012) Simulation study for automatic generation control of a multi-area power system by ANFIS approach. Appl Soft Comput J 12(1):333–341

    Article  Google Scholar 

  6. Barisal AK (2015) Comparative performance analysis of teaching learning based optimization for automatic load frequency control of multi-source power systems. Int J Electr Power Energy Syst 66:67–77

    Article  Google Scholar 

  7. Raju More, Saikia LC, Sinha N (2016) Department, “Automatic generation control of a multi-area system using ant lion optimizer algorithm based PID plus second order derivative controller”. Int J Electr Power Energy Syst 80:52–63

    Article  Google Scholar 

  8. Dash Puja, Saikia LC, Sinha N (2014) Comparison of performances of several Cuckoo search algorithm based 2DOF controllers in AGC of multi-area thermal system. Int J Electr Power Energy Syst 55:429–436

    Article  Google Scholar 

  9. Jagatheesan K, S. Samanta, A. Choudhury, N. Dey, B. Anand, A. S. Ashour (2018),Quantum inspired evolutionary algorithm in load frequency control of multi-area interconnected thermal power system with non-linearity. Quantum Comput Environ Intell Large Scale Real Appl 30:389–417

  10. Dhillon SS, Lather JS, Marwaha S (2016) Multi objective load frequency control using hybrid bacterial foraging and particle swarm optimized PI controller. Int J Electr Power Energy Syst 79:196–209

    Article  Google Scholar 

  11. Rajbongshi R, Saikia LC (2017) Performance of coordinated FACTS and energy storage devices in combined multiarea ALFC and AVR system. J Renew Sustain Energy 9(6):064101

    Article  Google Scholar 

  12. Mitani Y, Tsuji K, Murakami Y (1988) Application of superconducting magnetic energy storage to improve power system dynamic performance. IEEE Trans Power Syst 3:1418–1425

    Article  Google Scholar 

  13. Tripathy SC, Juengst KP (1997) Sampled data automatic generation control with superconducting magnetic energy storage in power systems. IEEE Trans Energy Convers 12(2):187–192

    Article  Google Scholar 

  14. Sudha KR, Santhi RV (2012) Load frequency control of an interconnected reheat thermal system using Type-2 fuzzy system including SMES units. Int J Electr Power Energy Syst 43(1):1383–1392

    Article  Google Scholar 

  15. Aditya SK, Das D (2001) Battery energy storage for load frequency control of an interconnected power system. Electr Power Energy Syst Res 58:179–185

    Article  Google Scholar 

  16. Dhundhara S, Verma YP (2018) Capacitive energy storage with optimized controller for frequency regulation in realistic multisource deregulated power system. Energy 147:1108–1128

    Article  Google Scholar 

  17. Hingorani NG, Gyugyi L (2000) Understanding FACTS : concepts and technology of flexible AC transmission systems. Wiley-IEEE Press, New York

    Google Scholar 

  18. Deepak M, Abraham RJ (2015) Electrical power and energy systems load following in a deregulated power system with Thyristor controlled series compensator. Int J Electr Power Energy Syst 65:136–145

    Article  Google Scholar 

  19. Reddy K, Reddy S, Padhy NP, Patel RN (2006) Congestion management in deregulated power system using FACTS devices. In: 2006 IEEE Power India Conf., pp 376–383, 2006

  20. Menniti D, Pinnarelli A, Scordino N, Sorrentino N (2004) Using a FACTS device controlled by a decentralised control law to damp the transient frequency deviation in a deregulated electric power system. Electr Power Syst Res 72(3):289–298

    Article  Google Scholar 

  21. Bhatt P, Roy R, Ghoshal SP (2011) Comparative performance evaluation of SMES–SMES, TCPS–SMES and SSSC–SMES controllers in automatic generation control for a two-area hydro-hydro system. Int J Electr Power Energy Syst 33:1585–1597

    Article  Google Scholar 

  22. Dahiya P, Sharma V, Naresh R (2017) Optimal sliding mode control for frequency regulation in deregulated power systems with DFIG-based wind turbine and TCSC–SMES. Neural Comput Appl 1–18. https://doi.org/10.1007/s00521-017-3250-y

  23. Bevrani H (2009) Robust power system frequency control. Springer, New York, US

    Book  MATH  Google Scholar 

  24. Pappachen A, Fathima AP (2016) Load frequency control in deregulated power system integrated with SMES–TCPS combination using ANFIS controller. Int J Electr Power Energy Syst 82:519–534

    Article  Google Scholar 

  25. Abraham RJ, Das D, Patra A (2007) Effect of TCPS on oscillations in tie-power and area frequencies in an interconnected hydrothermal power system. IET Gener Transm Distrib 1(4):632–639

    Article  Google Scholar 

  26. Tripathy SC, Balasubramanian R, Nair PSC (1992) Adaptive automatoc generation control with superconducting magnetic energy in power systems. IEEE Trans Energy Convers 7(3):434–441

    Article  Google Scholar 

  27. Zadeh L (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  MATH  Google Scholar 

  28. Mendel JM, Mouzouris GC (1997) Designing fuzzy logic systems. IEEE Trans. Circ Syst II Analog Digit Signal Process 44(11):885–895

    Article  Google Scholar 

  29. Yin X, Lin Y, Li W, Liu H, Gu Y (2015) Fuzzy-logic sliding-mode control strategy for extracting maximum wind power. IEEE Trans Energy Convers 30(4):1–12

    Article  Google Scholar 

  30. Yin X, Lin Y, Li W, Gu Y, Liu H, Lei P (2015) A novel fuzzy integral sliding mode current control strategy for maximizing wind power extraction and eliminating voltage harmonics. Energy 85:677–686

    Article  Google Scholar 

  31. Yin X, Lin Y, Li W, Gu Y, Lei P, Liu H (2015) Sliding mode voltage control strategy for capturing maximum wind energy based on fuzzy logic control. Int J Electr Power Energy Syst 70:45–51

    Article  Google Scholar 

  32. Yin X, Lin Y, Li W, Liu H, Gu Y (2014) Output power control for hydro-viscous transmission based continuously variable speed wind turbine. Renew Energy 72:395–405

    Article  Google Scholar 

  33. Yin X, Lin Y, Li W, Gu Y (2014) Integrated pitch control for wind turbine based on a novel pitch control system. J Renew Sustain Energy 6(4):043106

    Article  Google Scholar 

  34. Prakash S, Sinha SK (2014) Simulation based neuro-fuzzy hybrid intelligent PI control approach in four-area load frequency control of interconnected power system. Appl Soft Comput J 23:152–164

    Article  Google Scholar 

  35. Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern. 23(3):665–685

    Article  Google Scholar 

  36. Rojas I, Bernier JL, Rodriguez-Alvarez R, Prieto Z (2000)What are the main functional blocks involved in the design of adaptive neuro-fuzzy inference systems? In: Proceedings of the IEEE-INNS-enns international joint conference on neural networks. neural computing: new challenges and perspectives for the new millennium, vol. 6, pp 551–556, 2000

  37. Abdelrahim EM, Yahagi T (2001) A new transformed input-domain ANFIS for highly nonlinear system modeling and prediction. In: IEICE transactions on fundamentals of electronics, communications and computer sciences, vol. E84–A, no. 8. IEEE, Toronto, Ontario, Canada, pp 1981–1985, 2001.

  38. Prakash S, Sinha SK (2017) Automatic load frequency control of six areas’ hybrid multi-generation power systems using neuro-fuzzy intelligent controller. IETE J Res 2063:1–11

    Google Scholar 

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Acknowledgements

This work is supported by Electrical and Instrumentation Engineering Department, Thapar University, Patiala, Punjab and Electrical Engineering Department, Guru Kashi University, Bathinda, Punjab.

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Authors and Affiliations

  1. Department of Electrical Engineering, Guru Kashi University, Bathinda, Punjab, India

    Mandeep Sharma & Raj Kumar Bansal

  2. Electrical and Instrumentation Engineering Department, Thapar Institute of Engineering and Technology (Deemed to be University), Patiala, Punjab, India

    Surya Prakash

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  1. Mandeep Sharma
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  2. Raj Kumar Bansal
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Correspondence to Surya Prakash.

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Sharma, M., Bansal, R.K. & Prakash, S. Robustness Analysis of LFC for Multi Area Power System integrated with SMES–TCPS by Artificial Intelligent Technique. J. Electr. Eng. Technol. 14, 97–110 (2019). https://doi.org/10.1007/s42835-018-00035-3

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  • DOI: https://doi.org/10.1007/s42835-018-00035-3

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