Comparative performance analysis of one rank cuckoo search technique based optimization for automatic generation control of interconnected power systems

  • Authors

    • N. Manoharan
    • Subhransu Sekhar Dash
    • Raghuraman Sivalingam
    • Dheeraj P. R.
    2017-12-28
    https://doi.org/10.14419/ijet.v7i1.2.8987
  • Automatic Generation Control (AGC), Cuckoo Search, Interconnected Power System, One Rank Cuckoo Search, Classical PID Controller.

  • Abstract

    This paper presents a one rank cuckoo search optimization technique is proposed to design classical PID Controllers for Automatic Generation Control (AGC) of interconnected power systems. This method is proposed based on the original cuckoo search method. It was found in original cuckoo search the convergence speed is comparative lesser in reaching optimal solutions. To overcome the above mentioned problem one rank cuckoo search algorithm has been proposed which uses a bound by best solution technique to get the valid dimension so as to improve the system performance and rate of convergence. The proposed approach is applied to a four area hydro-thermal system in which area-1 and area-2 are steam reheat power plant and area-3 and area-4 are hydro power plant. The controller gains are derived using original cuckoo search and one rank cuckoo search methods. The superiority of the proposed approach is compared with the results obtained with original cuckoo search algorithm.

  • References

    1. [1] Ranjan Kumar Mallick, Manoj Kumar Debnath, fahimulHaque, RasmiRanjan Rout, Application of Grey Wolves based optimization technique in multi-area Automatic Generation Control, ICEEOT-2016.

      [1] J. Talaq, F. Al-Basri, Adaptive fuzzy gain scheduling for load frequency control, IEEE Trans. Power Syst. 14 (February (1)) (1999). https://doi.org/10.1109/59.744505.

      [2] P. Aravindan, M.Y. Sanavullah, Fuzzy logic based automatic load frequency control of two area power system with GRC, Int. J. Comput. Intell. Res. 5 (1) (2009)37–44.

      [3] L.C. Saikia, J. Nanda, S. Mishra, Performance comparison of several classical controllers in AGC for multi-area interconnected thermal system, Int. J. Electr.Power Energy Syst. 33 (2011) 394–401.

      [4] Y. Oysal, A comparative study of adaptive load frequency controller designs in power system with dynamic neural networks models, Energy Convers.Manage. 46 (2005) 2656–2668. https://doi.org/10.1016/j.ijepes.2010.08.036.

      [5] D. Rerkpreedapong, A. Hasanovic, A. Feliachi, Robust load frequency control using genetic algorithms and linear matrix inequalities, IEEE Trans. Power Syst. 18 (2) (2003) 855–861. https://doi.org/10.1016/j.enconman.2004.12.010.

      [6] S. Prakash, S.K. Sinha, Simulation based neuro-fuzzy hybrid intelligent PI control approach in four-area load frequency control of interconnected power system, Appl. Soft Comput. 23 (2014) 152–164. https://doi.org/10.1109/TPWRS.2003.811005.

      [7] H. Bevrani, P.R. Daneshmand, Fuzzy logic based load frequency control concerning high penetration of wind turbines, IEEE Syst. J. 6 (1) (2012) 173–180. https://doi.org/10.1016/j.asoc.2014.05.020.

      [8] H.A. Yousel, K.A.L. Kharusi, M.H. Albadi, N. Hosseinzadeh, Load frequency control of a multi-area power system: an adaptive fuzzy logic approach, IEEE Trans. Power Syst. 29 (4) (2014) 1822–1830. https://doi.org/10.1109/JSYST.2011.2163028.

      [9] H. Yousef, Adaptive fuzzy logic load frequency control of multi-area power system, Int. J. Elect. Power Syst. Res. 68 (2015) 384–395. https://doi.org/10.1109/TPWRS.2013.2297432.

      [10] M.I. Alomoush, Load frequency control and automatic generation control using fractional order controllers, Electr. Eng. Springer 91 (2010) 357–368. https://doi.org/10.1016/j.ijepes.2014.12.074.

      [11] S. Saxsena, Y.V. Hote, Load frequency control in power system via internal model control scheme and model order reduction, IEEE Trans. Power Syst. 28 (3) (2013) 2749–2757. https://doi.org/10.1007/s00202-009-0145-7.

      [12] S.B. Shyree, N. Kamaraj, Hybrid Neuro Fuzzy approach for automatic generation control in restructured power system, Int. J. Electr. Power Energy Syst. 74 (2016) 274–285 https://doi.org/10.1109/TPWRS.2013.2245349.

      [13] H. Shayeghi, A. Jalili, H.A. Shayanfar, Multi-stage fuzzy load frequency control using PSO, Energy Convers. Manage. 49 (2008) 2570–2580. https://doi.org/10.1016/j.ijepes.2015.05.029.

      [14] H. Golpira, H. Bevrani, A framework for economic load frequency control design using modiï¬ed multi-objective load frequency control, Electr. Power Compon. Syst. 42 (8) (2014) 788–797. https://doi.org/10.1016/j.enconman.2008.05.015.

      [15] D. Guha, P.K. Roy, S. Banerjee, Optimal design of superconducting magnetic energy storage based multi-area hydro-thermal system using biogeography based optimization, in: Proc. 2014 IEEE Fourth Int. Conf. Emerging Application of Information Technology (EAIT-2014), ISI Kolkata, 2014, pp. 52–57. https://doi.org/10.1080/15325008.2014.893545.

      [16] D. Guha, P.K. Roy, S. Banerjee, Study of dynamic responses of an interconnected two-area all thermal power system with governor and boiler nonlinearities using BBO, in: Proc. 2015 third Int. Conf. ComputerCommunication Control Information Technology (C3IT-2015), Academy of Technology, 2015, pp. 1–6. https://doi.org/10.1109/EAIT.2014.27.

      [17] D. Guha, P.K. Roy, S. Banerjee, Application of Krill Herd algorithm for optimum design of load frequency controller for multi-area power system network with generation rate constraint, in: 4th Int. Conf. on Frontiers in Intelligent Computing: Theory and Applications (FICTA 2015), 404, Springer, NIT-Durgapur, 2015, pp. 245–257. https://doi.org/10.1109/C3IT.2015.7060122.

      [18] R.K. Sahu, T.S. Gorripotu, S. Panda, Automatic generation control of multi-area power systems with diverse energy sources using Teaching Learning Based Optimization Algorithm, Eng. Sci. Technol. Int. J. 19 (2016) 113–134.

      [19] A.K. Barisal, Comparative performance analysis of teaching learning basedoptimization for automatic load frequency control of multi-source power system, Int. J. Electr. Power Energy Syst. 66 (2015) 67–77. https://doi.org/10.1016/j.jestch.2015.07.011.

      [20] J. Nanda, S. Mishra, L.C. Saikia, Maiden application of bacterial foraging based optimization technique inmulti-area automatic generation control, IEEE Trans.Power Syst. 24 (2) (2009) 602–609. https://doi.org/10.1016/j.ijepes.2014.10.019.

      [21] R.K. Sahu, S. Panda, S. Padhan, Optimal gravitational search algorithm for automatic generation control of an interconnected power system, Ain Shames Eng. J. 5 (3) (2014) 721–733. https://doi.org/10.1109/TPWRS.2009.2016588.

      [22] R.K. Sahu, S. Panda, G.T.C. Sekhar, A novel hybrid PSO-PS optimized PI-controller for AGC in multi-area interconnected power systems, Int. J. Electr.Power Energy Syst. 64 (2015) 880–893. https://doi.org/10.1016/j.asej.2014.02.004.

      [23] R.K. Sahu, S. Panda, S. Padhan, A hybrid ï¬refly algorithm and pattern search techniques for automatic generation control of multi-are power system, Int. J. Electr. Power Energy Syst. 64 (2015) 9–23. https://doi.org/10.1016/j.ijepes.2014.08.021.

      [24] Z. Al-Hamouz, N. Al-Musabi, H. Al-Duwaish, S. Al-Baiyat, On the design of variable structure load frequency controllers by tabu search algorithm:application to nonlinear interconnected models, Electr. Power Compon. Syst.33 (11) (2005) 1253–1267. https://doi.org/10.1016/j.ijepes.2014.07.013.

      [25] C.K. Shiva, V. Mukherjee, A novel quasi-oppositional harmony search algorithm for AGC optimization of three-area multi-unit power system after deregulation, Eng. Sci. Technol. Int. J. 19 (2016) 395–420. https://doi.org/10.1080/15325000590951735.

      [26] C.K. Shiva, V. Mukherjee, A novel quasi-oppositional harmony search algorithm for automatic generation control of power system, Appl. Soft Comput. 35 (2015) 749–765. https://doi.org/10.1016/j.jestch.2015.07.013.

      [27] S.M. Abd-Elazim, E.S. Ali, Load frequency controller design via BAT algorithmfor nonlinear interconnected power system, Int. J. Electr. Power Energy Syst.77 (2016) 166–177. https://doi.org/10.1016/j.asoc.2015.05.054.

      [28] D. Guha, P.K. Roy, S. Banerjee, Application of backtracking search algorithm inload frequency control of multi-area interconnected power system, Ain ShamsEng. J. (2016). in press. https://doi.org/10.1016/j.ijepes.2015.11.029.

      [29] EhsanValian, ShahramMohanna and Saeed Tavakoli, Improved Cuckoo Search algorithm for Feed-forward neural network training, International Journal of Artificial Intelligence & Applications (IJAIA), Vol.2, No.3, July 2011.

      [30] Nguyen HuuThien An, Vo Ngoc Dieu, ThangTrung Nguyen and Vo TrungKien, One Rank Cuckoo Search

      [31] Algorithm for Optimal Reactive Power Dispatch, N. H. T. An et al. / GMSARN International Journal 9 (2015) 73 – 82.

  • Downloads

  • How to Cite

    Manoharan, N., Sekhar Dash, S., Sivalingam, R., & P. R., D. (2017). Comparative performance analysis of one rank cuckoo search technique based optimization for automatic generation control of interconnected power systems. International Journal of Engineering & Technology, 7(1.2), 37-42. https://doi.org/10.14419/ijet.v7i1.2.8987

    Received date: 2017-12-30

    Accepted date: 2017-12-30

    Published date: 2017-12-28