Skip to main content
SpringerLink
Log in

Application of Chaos-Based Firefly Algorithm Optimized Controller for Automatic Generation Control of Two Area Interconnected Power System with Energy Storage Unit and UPFC

  • Chapter
  • First Online:
Book cover Applications of Firefly Algorithm and its Variants

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

Abstract

Firefly algorithm (FFA) optimization technique is a popular continuous optimization technique and also widely applied to tune controller gain values of proportional–integral–derivative (PID) controller. In the chaos-based firefly algorithm, the chaotic maps are included to improve the randomness when generating new solutions and thereby to increase the diversity of the population. It increases global search mobility for robust global optimization. The investigated power system incorporates two thermal power systems, and two areas are interconnected via tie-line. Also, this work system is equipped with a secondary (PID) controller. The gain value of the controller is optimized by implementing chaos-based firefly algorithm (CFFA) by applying step load in area 1. The effectiveness and supremacy of proposed optimization technique tuned controller performance are verified by comparing genetic algorithm (GA), particle swarm optimization (PSO) technique and firefly algorithm tuned PID controller performance in the same interconnected power system. The proposed optimization technique-based controller response is evaluated by considering time domain specifications parameters of the proposed optimization technique-based controller response. Further, effectiveness of recommended optimization technique is verified by adding nonlinearity’s [generation rate constraint (GRC) and governor dead band (GCB)] in the investigated power system with connecting unified power flow controller (UPFC) is applied, and it is a flexible alternating current transmission system (FACTS) controller family device connected parallel to the tie-line. Further, hydrogen aqua electrolyzer (HAE) energy storage unit with a fuel cell is connected in area 2 for improving the performance of the system during sudden load demand. Finally, simulation result proved that the proposed CFFA technique gives better controlled performance compared with GA, PSO and FFA technique tuned controller performance in terms of fast settled response with a minimum peak over and undershoot, damping oscillations during unexpected load demand conditions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Kundur P, Balu NJ, Lauby MG (1994) Power system stability and control, vol 7. McGraw-Hill, New York

    Google Scholar 

  2. Yang TC, Cimen H, Zhu QM (1998) Decentralised load-frequency controller design based on structured singular values. IEE Gener Transm Distrib 145(1):7–14

    Article  Google Scholar 

  3. Kundur P (1994) Power system stability and control. McGraw-Hill

    Google Scholar 

  4. Rerkpreedapong D, Hasanovic A, Feliachi A (2003) Robust load frequency control using genetic algorithms and linear matrix inequalities. IEEE Trans Power Syst 18(2):855–861

    Article  Google Scholar 

  5. Zribi M, Al-Rashed M, Alrifai M (2005) Adaptive decentralized load frequency control of multi-area power systems. Int J Electr Power Energy Syst 27(8):575–583

    Article  Google Scholar 

  6. Kocaarslan I, Çam E (2005) Fuzzy logic controller in interconnected electrical power systems for load-frequency control. Int J Electr Power Energy Syst 27(8):542–549

    Article  Google Scholar 

  7. Pothiya S, Ngamroo I, Runggeratigul S, Tantaswadi P (2006) Design of optimal fuzzy logic based PI controller using multiple tabu search algorithm for load frequency control. Int J Control Autom Syst 4(2):155–164

    Google Scholar 

  8. Taher SA, Hematti R, Abdolalipour A, Tabei SH (2008) Optimal decentralized load frequency control using HPSO algorithms in deregulated power systems. Am J Appl Sci 5(9):1167–1174

    Article  Google Scholar 

  9. Shayeghi H, Jalili A, Shayanfar HA (2008) Multi-stage fuzzy load frequency control using PSO. Energy Convers Manag 49(10):2570–2580

    Article  Google Scholar 

  10. Sabahi K, Sharifi A, Sh MA, Teshnehlab M, Alisghary M (2008) Load frequency control in interconnected power system using multi-objective PID controller. J Appl Sci 8(20):3676–3682

    Article  Google Scholar 

  11. Panda G, Panda S, Ardil C (2009) Automatic generation control of interconnected power system with generation rate constraints by hybrid neuro fuzzy approach. Int J Electr Power Energy Syst Eng 2(1):13–18

    Google Scholar 

  12. Ramesh S, Krishnan A (2009) Modified genetic algorithm based load frequency controller for interconnected power system. Int J Electr Power Eng 3(1):26–30

    Google Scholar 

  13. Aravindan P, Sanavullah MY (2009) Fuzzy logic based automatic load frequency control of two area power system with GRC. Int J Comput Intell Res 5(1):37–45

    Google Scholar 

  14. Ford JJ, Bevrani H, Ledwich G (2009) Adaptive load shedding and regional protection. Int J Electr Power Energy Syst 31(10):611–618

    Article  Google Scholar 

  15. Roy R, Bhatt P, Ghoshal SP (2010) Evolutionary computation based three-area automatic generation control. Expert Syst Appl 37(8):5913–5924

    Article  Google Scholar 

  16. Shankar R (2018) Sine-Cosine algorithm based PIDD 2 controller design for AGC of a multisource power system incorporating GTPP in DG unit. In: 2018 international conference on computational and characterization techniques in Engineering & Sciences (CCTES). IEEE, pp 197–201

    Google Scholar 

  17. Tasnin W, Saikia LC, Saha A, Saha D, Rajbongshi R (2018) Effect of different renewables and FACT device on an interconnected thermal system using SCA optimized fractional order cascade controllers. In: 2018 2nd international conference on power, energy and environment: towards smart technology (ICEPE). IEEE, pp 1–6

    Google Scholar 

  18. Khadanga RK, Kumar A (2019) Analysis of PID controller for the load frequency control of static synchronous series compensator and capacitive energy storage source-based multi-area multi-source interconnected power system with HVDC link. Int J Bio-Inspired Comput 13(2):131–139

    Article  Google Scholar 

  19. Sahu RK, Sekhar GC, Priyadarshani S (2019) Differential evolution algorithm tuned tilt integral derivative controller with filter controller for automatic generation control. Evol Intell, 1–16

    Google Scholar 

  20. Åström KJ, Hägglund T (1995) PID controllers: theory, design, and tuning, vol 2. Instrument Society of America, Research Triangle Park, NC

    Google Scholar 

  21. Padhan S, Sahu RK, Panda S (2014) Application of firefly algorithm for load frequency control of multi-area interconnected power system. Electric Power Compon Syst 42(13):1419–1430

    Article  Google Scholar 

  22. Pradhan PC, Sahu RK, Panda S (2016) Firefly algorithm optimized fuzzy PID controller for AGC of multi-area multi-source power systems with UPFC and SMES. Eng Sci Technol Int J 19(1):338–354

    Article  Google Scholar 

  23. Sharma V, Naresh R, Pulluri H (2016) Automatic generation control of multi-source interconnected power system including DFIG wind turbine. In: 2016 IEEE 1st international conference on power electronics, intelligent control and energy systems (ICPEICES). IEEE, pp 1–6

    Google Scholar 

  24. Jagatheesan K, Anand B, Dey N, Ashour AS, Satapathy SC (2017) Performance evaluation of objective functions in automatic generation control of thermal power system using ant colony optimization technique-designed proportional–integral–derivative controller. Electr Eng, 1–17

    Google Scholar 

  25. Jagatheesan K, Anand B, Samanta S, Dey N, Santhi V, Ashour AS, Balas VE (2017) Application of flower pollination algorithm in load frequency control of multi-area interconnected power system with nonlinearity. Neural Comput Appl 28(1):475–488 (SCIE, SCOPUS, Impact Factor: 4.213)

    Article  Google Scholar 

  26. Jagatheesan K, Anand B, Samanta S, Dey N, Ashour AS, Balas VE (2017) Design of a proportional-integral-derivative controller for an automatic generation control of multi-area power thermal systems using firefly algorithm. IEEE/CAA J Automat Sin

    Google Scholar 

  27. Jagatheesan K, Anand B, Ebrahim MA (2014) Stochastic particle swarm optimization for tuning of PID controller in load frequency control of single area reheat thermal power system. Int J Electr Power Eng 8(2):33–40

    Google Scholar 

  28. Jagatheesan K, Anand B, Nilanjan D, Ashour AS, Rajesh K (2019) Bat algorithm optimized controller for automatic generation control of interconnected thermal power system. Frontiers in artificial intelligence and applications, vol 314: information technology and intelligent transportation systems. Springer, Cham, pp 276–286

    Google Scholar 

  29. Sahu RK, Gorripotu TS, Panda S (2015) A hybrid DE-PS algorithm for load frequency control under deregulated power system with UPFC and RFB. Ain Shams Eng J 6:893–911

    Article  Google Scholar 

  30. Francis R, Chidambaram IA (2015) Optimized PI + load frequency controller using BWNN approach for an interconnected reheat power system with RFB and hydrogen electrolyser units. Int J Electr Power Energy Syst 67:381–392

    Article  Google Scholar 

  31. Yang XS (2008) Nature-inspired metaheuristic algorithms. Luniver Press

    Google Scholar 

  32. Yang XS (2009) Firefly algorithms for multimodal optimization. In: Stochastic algorithms: foundations and applications (SAGA 2009). Lecture notes in computer sciences, vol 5792, pp 169–178

    Chapter  Google Scholar 

  33. Lukasik S, Zak S (2009) Firefly algorithm for continuous constrained optimization task. In: International conference on computational collective intelligence (ICCCI 2009). Lecture notes in artificial intelligence, vol 5796, pp 97–100

    Chapter  Google Scholar 

  34. Yang XS (2010) Firefly algorithm, stochastic test functions and design optimization. Int J Bio-Inspired Comput 2(2):78–84

    Article  Google Scholar 

  35. Dey N (ed) (2017) Advancements in applied metaheuristic computing. IGI Global

    Google Scholar 

  36. Dey N, Samanta S, Chakraborty S, Das A, Chaudhuri SS, Suri JS (2014) Firefly algorithm for optimization of scaling factors during embedding of manifold medical information: an application in ophthalmology imaging. J Med Imaging Health Inform 4(3):384–394

    Article  Google Scholar 

  37. Kumar R, Rajan A, Talukdar FA, Dey N, Santhi V, Balas VE (2017) Optimization of 5.5-GHz CMOS LNA parameters using firefly algorithm. Neural Comput Appl 28(12):3765–3779

    Article  Google Scholar 

  38. Kumar R, Talukdar FA, Dey N, Balas VE (2016) Quality factor optimization of spiral inductor using firefly algorithm and its application in amplifier. Int J Adv Intell Paradigms

    Google Scholar 

  39. Chakraborty S, Dey N, Samanta S, Ashour AS, Balas VE (2016) Firefly algorithm for optimized nonrigid demons registration. In: Bio-inspired computation and applications in image processing. Academic Press, pp 221–237

    Google Scholar 

  40. Samanta S, Mukherjee A, Ashour AS, Dey N, Tavares JMR, Abdessalem Karâa WB, Hassanien AE (2018) Log transform based optimal image enhancement using firefly algorithm for autonomous mini unmanned aerial vehicle: an application of aerial photography. Int J Image Graph 18(04):1850019

    Article  Google Scholar 

  41. Gandomi AH, Yang X-S, Talatahari S, Alavi AH (2013) Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simulat 18(2013):89–98

    Article  MathSciNet  MATH  Google Scholar 

  42. Yang XS, Deb S (2010) Eagle strategy using Levy walk and firefly algorithms for stochastic optimization. In: Nature inspired cooperative strategies for optimization (NISCO 2010). Studies in computational intelligence, vol 284. Springer, Berlin, pp 101–111

    Chapter  Google Scholar 

  43. dos Santos Coelho L, de Andrade Bernert DL, Mariani VC (2011) A chaotic firefly algorithm applied to reliability-redundancy optimization. In: 2011 IEEE congress of evolutionary computation (CEC), New Orleans, LA, pp 517–521

    Google Scholar 

  44. Fister Jr I, Perc M, Kamal SM, Fister I (2015) A review of chaos-based firefly algorithms: perspectives and research challenges. Appl Mathe Comput 252(2015):155–165

    Article  MathSciNet  MATH  Google Scholar 

  45. Coelho L, Mariani VC (2008) Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization. Expert Syst Appl 34:1905–1913

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Paavai Engineering College, Namakkal, Tamil Nadu, India

    K. Jagatheesan

  2. Department of Electronics and Instrumentation Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, India

    B. Anand

  3. Department of Computer Science & Engineering, University Institute of Technology, BU, Burdwan, West Bengal, India

    Soumadip Sen & Sourav Samanta

Authors
  1. K. Jagatheesan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. Anand
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Soumadip Sen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sourav Samanta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Jagatheesan .

Editor information

Editors and Affiliations

  1. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Nilanjan Dey

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Jagatheesan, K., Anand, B., Sen, S., Samanta, S. (2020). Application of Chaos-Based Firefly Algorithm Optimized Controller for Automatic Generation Control of Two Area Interconnected Power System with Energy Storage Unit and UPFC. In: Dey, N. (eds) Applications of Firefly Algorithm and its Variants. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-0306-1_8

Download citation

Publish with us

Policies and ethics

Search

Navigation