Skip to main content
SpringerLink
Log in

Self-adaptiveness in particle swarm optimisation to enhance available transfer capability using thyristor-controlled series compensation (TCSC)

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

Available transfer capability (ATC) is one of the challenging criteria under the functioning of the deregulated power system. The high demand for improving ATC is generally met using flexible alternating current transmission system (FACTS) devices in the power system. However, it suffers from serious crisis during determination of the optimal location and compensation stage of FACTS. The present study uses thyristor-controlled series compensation (TCSC) devices in order to compensate for the limitation of FACTS. Further, a novel self-adapted particle swarm optimisation (SAPSO) algorithm is proposed in this study for enhancing ATC. Experiments are carried on three benchmark bus systems such as IEEE 24, IEEE 30 and IEEE 57. Performance and statistical analyses are carried out by comparing the proposed SAPSO with the conventional PSO. Eventually, the study proves the effectiveness of the proposed method in case of ATC enhancement.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3

Similar content being viewed by others

References

  1. Gyugyi L 1992 A unified power flow control concept for flexible AC transmission systems. IEEE Proceedings of Generation, Transmission and Distribution, Pittsburgh, USA, 139(4): 323–331

    Article  Google Scholar 

  2. Swamy S M, Rajakumar B R and Valarmathi I R 2013 Design of hybrid wind and photovoltaic power system using opposition–based genetic algorithm with Cauchy mutation. In: Proceedings of IET Chennai 4th International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2013), Chennai, pp. 504–510

  3. Ejebe G C, Tong J, Waight J G, Frame J G, Wang X and Tinney W F 1998 Available transfer capability calculations. IEEE Trans. Power Syst. 13(4): 1521–1527

    Article  Google Scholar 

  4. Padmavathi S V, Sahu S and Jayalakshmi A 2012 Available transfer capability enhancement by using Particle Swarm Optimization algorithm based FACTS allocation. In: 2012 Asia Pacific Conference on Postgraduate Research in Microelectronics and Electronics, Hyderabad, pp. 184–187

  5. Yadav N 2015 Genetic algorithm with dual mutation probabilities for TCSC based ATC enhancement. In: Proceedings of Sixth International Conference on Advances in Computing, Control, and Telecommunication Technologies - ACT

  6. Ali E S and Abd-Elazim S M 2012 Coordinated design of PSSs and TCSC via bacterial swarm optimization algorithm in a multimachine power system. Int. J. Elec. Power. 36(1): 84–92

    Article  Google Scholar 

  7. Farahmand H, Rashidi-Nejad M and Fotuhi-Firoozabad M 2004 Implementation of FACTS devices for ATC enhancement using RPF technique. In: Power Engineering, 2004 LESCOPE-04. 2004 Large Engineering systems Conference, pp. 30–35

  8. Transmission Transfer Capability Task Force 1996 Available transfer capability Definitions and determination, North American Electric Reliability Council, New Jersey

    Google Scholar 

  9. Galiana F D, Almeida K, Toussaint M, Griffin J, Atanackovic D, Ooi B T and McGillis D T 1996 Assessment and control of the impact of FACTS devices on power system performance. IEEE Trans. Power Syst. 11(4): 1931–1936

    Article  Google Scholar 

  10. Nireekshana T, Kesava Rao G and Siva Naga Raju S 2012 Enhancement of ATC with FACTS devices using Real-code Genetic Algorithm. Int. J. Elec. Power 43(1): 1276–1284

    Article  Google Scholar 

  11. Acharya N and Mithulananthan N 2007 Locating series FACTS devices for congestion management in deregulated electricity markets. Electr. Pow. Syst. Res. 77(3–4): 352–360

    Article  Google Scholar 

  12. Canizares C A and Z T Faur 1999 Analysis of SVC and TCSC controllers in voltage collapse. IEEE Trans. Power Syst. 14(1): 158–165

    Article  Google Scholar 

  13. Ibraheem N and Yadav N K 2011 Implementation of FACTS Device for Enhancement of ATC Using PTDF. Int. J. Comput. Electr. Eng. 3(3): 343–348

    Article  Google Scholar 

  14. Mahmoudian M and Yousefi G R ATC improvement and losses estimation considering dynamic transmission line ratings. In: 20th Iranian Conference on Electrical Engineering (ICEE2012), pp. 404–409

  15. Hingorani N G and Gyugyi L 2000 Understanding facts, concepts and technology of flexible AC transmission systems. New York: IEEE

    Google Scholar 

  16. Bavithra K, Charles Raja S and Venkatesh P 2016 Optimal setting of FACTS devices using particle swarm optimization for ATC enhancement in deregulated power system. In: 4th IFAC Conference on Advances in Control and Optimization of Dynamical Systems ACODS 2016, 49(1): 450–455

  17. Bavithra K, Charles Raja S and Venkatesh P 2016 Optimal setting of FACTS devices using particle swarm optimization for ATC enhancement in deregulated power system. IFAC-PapersOnLine 49(1): 450–455

    Article  Google Scholar 

  18. Nireekshana T, Kesava Rao G and Siva Naga Raju S 2012 Enhancement of ATC with FACTS devices using Real-code Genetic Algorithm. Int. J. Electr. Power 43(1): 1276–1284

    Article  Google Scholar 

  19. Gupta A and Kumar A 2016 Impact of TCSC installation on ATC in a system incorporating wind and hydro generations. Proced. Technol. 25: 743–750

    Article  Google Scholar 

  20. Farahmand H, Rashidinejad M, Mousavi A, Gharaveisi A A, Irving M R and Taylor G A 2012 Hybrid mutation particle swarm optimisation method for available transfer capability enhancement. Electr. Power Energy Syst. 42(1): 240–249

    Article  Google Scholar 

  21. Karimi-Nasab M, Modarres M and Seyedhoseinic S M 2015 A self-adaptive PSO for joint lot sizing and job shop scheduling with compressible process times. Appl. Soft Comput. 27: 137–147

    Article  Google Scholar 

  22. Zuo X, Zhang G and Tan W 2014 Self-adaptive learning PSO-based deadline constrained task scheduling for hybrid IaaS cloud. IEEE Trans. Autom. Sci. Eng. 11(2): 564–573

    Article  Google Scholar 

  23. Rashidinejad M, Farahmand H, Fotuhi-Firuzabad M and Gharaveisi A A 2008 ATC enhancement using TCSC via artificial intelligent techniques. Electr. Power Syst. Res. 78(1): 11–20

    Article  Google Scholar 

  24. Khaburi M A and Haghifam M R 2010 A probabilistic modeling based approach for total transfer capability enhancement using FACTS devices. Int. J. Electr. Power. 32(1): 12–16

    Article  Google Scholar 

  25. Hiraki Y, Hiraiwa T and Iwamoto S 2012 NAS battery system design and allocation for improvement of transient stability ATC. In: 2012 10th International Power & Energy Conference (IPEC), Chicago, pp. 190–195

  26. Satoh T, Tanaka H and Iwamoto S 2007 ATC Improvement by phase shifter application considering dynamic rating, In: Power Symposium, 2007 NAPS ‘07 39th North American, Las Cruces, pp. 528–533

  27. Jain T, Singh S N and Srivastava S C Dynamic ATC enhancement through optimal placement of FACTS controllers. Electr. Power Syst. Res. 79(11): 1473–1482

  28. De A, Mamanduru V K R, Gunasekaran A, Subramanian N and Tiwari M K 2016 Composite particle algorithm for sustainable integrated dynamic ship routing and scheduling optimization. Comput. Ind. Eng. 96: 201–215

    Article  Google Scholar 

  29. Alexandridis A, Chondrodima E and Sarimveis H 2016 Cooperative learning for radial basis function networks using particle swarm optimization. Appl. Soft Comput. 49: 485–497

    Article  Google Scholar 

  30. De A, Kumar S, Gunasekaran A and Tiwari M K 2017 Sustainable maritime inventory routing problem with time window constraints. Eng. Appl. Artif. Intell. 61: 77–95

    Article  Google Scholar 

  31. Soleimani H and Kannan G 2015 A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Appl. Math. Model. 39(14): 3990–4012

    Article  MathSciNet  Google Scholar 

  32. De A, Awasthi A and Tiwari M K 2015 Robust formulation for optimizing sustainable ship routing and scheduling problem. IFAC-PapersOnLine. 48(3): 368–373

    Article  Google Scholar 

  33. Li C-Y and Liu C-W 2002 A new algorithm for available transfer capability computation. Int J. Elec. Power. 24(2): 159–166

    Article  MathSciNet  Google Scholar 

  34. Vaithilingam C and Kumudini Devi R P 2013 Available transfer capability estimation using Support Vector Machine. Int. J. Elec. Power. 47: (May): 387–393

    Article  Google Scholar 

  35. Sheng H and Chiang H-D 2014 CDFLOW: A practical tool for tracing stationary behaviors of general distribution networks. IEEE Trans. Power Syst. 29(3): 1365–1371

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, DCR University of Science and Technology, Murthal (Sonepat), India

    NARESH KUMAR YADAV & ANJU BALA

Authors
  1. NARESH KUMAR YADAV
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. ANJU BALA
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to NARESH KUMAR YADAV.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

YADAV, N.K., BALA, A. Self-adaptiveness in particle swarm optimisation to enhance available transfer capability using thyristor-controlled series compensation (TCSC). Sādhanā 43, 152 (2018). https://doi.org/10.1007/s12046-018-0877-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12046-018-0877-z

Keywords

Search

Navigation