Advertisement

CLSA-MRPID controller for automatic generation control of a three-area hybrid system

  • B. V. S. Acharyulu
  • Prakash Kumar Hota
  • Banaja Mohanty
Original Paper
  • 9 Downloads

Abstract

In the paper, an optimal controller is proposed for Automatic Generation Control (AGC) of three areas under deregulated power system. In the power system area, each area under the deregulated framework incorporated with the renewable energy sources like Wind Turbine (WT) and Photovoltaic (PV). The proposed optimal controller is the utilization of Combined Lightening Search Algorithm (CLSA) with wavelet based Multi Resolution Proportional-Integral-Derivative (MRPID) controller. The purpose of the proposed controller is to sustain the system frequency close to its nominal value, regulate system output and maintain power balance under load variations. Here, the LSA optimization process is utilized for optimally tuning the parameters of MRPID controller for each area. The attained LSA gain parameters are subject to the minimum Integral Square Error (ISE), which ensures that the system frequency deviation, Area Control Error (ACE) and tie-line power as minimum. The proposed controller is implemented in MATLAB/Simulink platform and analyzed their AGC responses, frequency deviation. Examinations reveal that the LSA enhanced MRPID controller performance is superior to anything others as far as settling time, peak overshoot and magnitude of oscillations in the system for various classes of extensive deregulated cases. Under these circumstances, the simulation results demonstrate that the designed power system model might be a feasible one and the proposed CLSA-MRPID algorithm might be a promising method. In the work, optimization algorithm based control technique is developed for evaluating AGC. Therefore, the convergence graph is analyzed for the proposed and existing methods.

Keywords

AGC LSA MRPID Wavelet Frequency Three area system PV Thermal WT 

References

  1. 1.
    Oliveira, E., Honório, L., Anzai, A., Oliveira, L., Costa, E.: Optimal transient droop compensator and PID tuning for load frequency control in hydro power systems. Int. J. Electr. Power Energy Syst. 68, 345–355 (2015)CrossRefGoogle Scholar
  2. 2.
    Vijaya Chandrakala, K., Balamurugan, S., Sankaranarayanan, K.: Variable structure fuzzy gain scheduling based load frequency controller for multi source multi area hydro thermal system. Int. J. Electr. Power Energy Syst. 53, 375–381 (2013)CrossRefGoogle Scholar
  3. 3.
    Sahu, R., Panda, S., Yegireddy, N.: A novel hybrid DEPS optimized fuzzy PI/PID controller for load frequency control of multi-area interconnected power systems. J. Process Control 24, 1596–1608 (2014)CrossRefGoogle Scholar
  4. 4.
    Sahu, R., Chandra Sekhar, G., Panda, S.: DE optimized fuzzy PID controller with derivative filter for LFC of multi source power system in deregulated environment. Ain Shams Eng. J. 6, 511–530 (2015)CrossRefGoogle Scholar
  5. 5.
    Zhang, C., Jiang, L., Wu, Q., He, Y., Wu, M.: Further results on delay-dependent stability of multi-area load frequency control. IEEE Trans. Power Syst. 28, 4465–4474 (2013)CrossRefGoogle Scholar
  6. 6.
    Dong, L., Tang, Y., He, H., Sun, C.: An event-triggered approach for load frequency control with supplementary ADP. IEEE Trans. Power Syst. 32, 581–589 (2017)CrossRefGoogle Scholar
  7. 7.
    Shivaie, M., Kazemi, M., Ameli, M.: A modified harmony search algorithm for solving load-frequency control of non-linear interconnected hydrothermal power systems. Sustain. Energy Technol. Assess. 10, 53–62 (2015)Google Scholar
  8. 8.
    Mosaad, M., Salem, F.: LFC based adaptive PID controller using ANN and ANFIS techniques. J. Electr. Syst. Inf. Technol. 1, 212–222 (2014)Google Scholar
  9. 9.
    Parmar, K., Majhi, S., Kothari, D.: LFC of an interconnected power system with multi-source power generation in deregulated power environment. Int. J. Electr. Power Energy Syst. 57, 277–286 (2014)CrossRefGoogle Scholar
  10. 10.
    Yousef, H.: Adaptive fuzzy logic load frequency control of multi-area power system. Int. J. Electr. Power Energy Syst. 68, 384–395 (2015)CrossRefGoogle Scholar
  11. 11.
    Mohamed, T., Bevrani, H., Hassan, A., Hiyama, T.: Decentralized model predictive based load frequency control in an interconnected power system. Energy Convers. Manag. 52, 1208–1214 (2011)CrossRefGoogle Scholar
  12. 12.
    Pan, I., Das, S.: Fractional-order load-frequency control of interconnected power systems using chaotic multi-objective optimization. Appl. Soft Comput. 29, 328–344 (2015)CrossRefGoogle Scholar
  13. 13.
    Naidu, K., Mokhlis, H., Bakar, A.: Multiobjective optimization using weighted sum artificial bee colony algorithm for load frequency control. Int. J. Electr. Power Energy Syst. 55, 657–667 (2014)CrossRefGoogle Scholar
  14. 14.
    Ma, M., Zhang, C., Liu, X., Chen, H.: Distributed model predictive load frequency control of the multi-area power system after deregulation. IEEE Trans. Industr. Electron. 64, 5129–5139 (2017)CrossRefGoogle Scholar
  15. 15.
    Sahu, R., Gorripotu, T., Panda, S.: A hybrid DE–PS algorithm for load frequency control under deregulated power system with UPFC and RFB. Ain Shams Eng. J. 6, 893–911 (2015)CrossRefGoogle Scholar
  16. 16.
    Arya, Y., Kumar, N.: BFOA-scaled fractional order fuzzy PID controller applied to AGC of multi-area multi-source electric power generating systems. Swarm Evolut. Comput. 32, 202–218 (2017)CrossRefGoogle Scholar
  17. 17.
    Madasu, S., Kumar, M., Singh, A.: Comparable investigation of backtracking search algorithm in automatic generation control for two area reheat interconnected thermal power system. Appl. Soft Comput. 55, 197–210 (2017)CrossRefGoogle Scholar
  18. 18.
    Rahman, A., Saikia, L., Sinha, N.: Automatic generation control of an interconnected two-area hybrid thermal system considering dish-stirling solar thermal and wind turbine system. Renew Energy 105, 41–54 (2017)CrossRefGoogle Scholar
  19. 19.
    Muhssin, M., Cipcigan, L., Obaid, Z., AL-Ansari, W.: A novel adaptive deadbeat-based control for load frequency control of low inertia system in interconnected zones north and south of Scotland. Int. J. Electr. Power Energy Syst. 89, 52–61 (2017)CrossRefGoogle Scholar
  20. 20.
    Singh, S., Prakash, T., Singh, V., Babu, M.: Analytic hierarchy process based automatic generation control of multi-area interconnected power system using Jaya algorithm. Eng. Appl. Artif. Intell. 60, 35–44 (2017)CrossRefGoogle Scholar
  21. 21.
    Ma, M., Zhang, C., Liu, X., Chen, H.: Distributed model predictive load frequency control of the multi-area power system after deregulation. IEEE Trans. Industr. Electron. 64, 5129–5139 (2017)CrossRefGoogle Scholar
  22. 22.
    Liu, X., Zhang, Y., Lee, K.: Coordinated distributed MPC for load frequency control of power system with wind farms. IEEE Trans. Industr. Electron. 64, 5140–5150 (2017)CrossRefGoogle Scholar
  23. 23.
    Ma, M., Chen, H., Liu, X., Allgöwer, F.: Distributed model predictive load frequency control of multi-area interconnected power system. Int. J. Electr. Power Energy Syst. 62, 289–298 (2014)CrossRefGoogle Scholar
  24. 24.
    Behera, A., Panigrahi, T., Sahoo, A., Ray, P.: Hybrid ITLBO-DE optimized fuzzy PI controller for multi-area automatic generation control with generation rate constraint. Smart Comp. Inform. 77, 713–722 (2017)CrossRefGoogle Scholar
  25. 25.
    Belkacemi, R., Rimal, A.: A novel NERC compliant automatic generation control in multi-area power systems in the presence of renewable-energy resources. Electr. Eng. 99, 931–941 (2016)CrossRefGoogle Scholar
  26. 26.
    Sahu, R., Panda, S., Sekhar, G.C.: A novel hybrid PSO-PS optimized fuzzy PI controller for AGC in multi area interconnected power systems. Int. J. Electr. Power Energy Syst. 64, 880–893 (2015)CrossRefGoogle Scholar
  27. 27.
    Dhillon, S., Lather, J., Marwaha, S.: Multi objective load frequency control using hybrid bacterial foraging and particle swarm optimized PI controller. Int. J. Electr. Power Energy Syst. 79, 196–209 (2016)CrossRefGoogle Scholar
  28. 28.
    Bernard, M., Mohamed, T., Qudaih, Y., Mitani, Y.: Decentralized load frequency control in an interconnected power system using coefficient diagram method. Int. J. Electr. Power Energy Syst. 63, 165–172 (2014)CrossRefGoogle Scholar
  29. 29.
    Yousef, H., AL-Kharusi, K., Albadi, M., Hosseinzadeh, N.: Load frequency control of a multi-area power system: an adaptive fuzzy logic approach. IEEE Trans. Power Syst. 29, 1822–1830 (2014)CrossRefGoogle Scholar
  30. 30.
    Padhan, S., Sahu, R., Panda, S.: Application of firefly algorithm for load frequency control of multi-area interconnected power system. Electric. Power Compon. Syst. 42, 1419–1430 (2014)CrossRefGoogle Scholar
  31. 31.
    Shiva, C., Mukherjee, V.: Automatic generation control of multi-unit multi-area deregulated power system using a novel quasi-oppositional harmony search algorithm. IET Gener. Transm. Distrib. 9, 2398–2408 (2015)CrossRefGoogle Scholar
  32. 32.
    Mohanty, B., Hota, P.: Comparative performance analysis of fruit fly optimisation algorithm for multi-area multi-source automatic generation control under deregulated environment. IET Gener. Transm. Distrib. 9, 1845–1855 (2015)CrossRefGoogle Scholar
  33. 33.
    Khan, M., Rahman, M.: Implementation of a wavelet-based MRPID controller for benchmark thermal system. IEEE Trans. Ind. Electron. 57, 4160–4169 (2010)CrossRefGoogle Scholar
  34. 34.
    Shareef, H., Ibrahim, A., Mutlag, A.: Lightning search algorithm. Appl. Soft Comput. 36, 315–333 (2015)CrossRefGoogle Scholar
  35. 35.
    Shareef, H., Mutlag, A., Mohamed, A.: A novel approach for fuzzy logic PV inverter controller optimization using lightning search algorithm. Neurocomputing. 168, 435–453 (2015)CrossRefGoogle Scholar
  36. 36.
    Ghoshal, S.: Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control. Electric. Power Syst. Res. 72, 203–212 (2004)CrossRefGoogle Scholar
  37. 37.
    AlRashidi, M., El-Hawary, M.: A survey of particle swarm optimization applications in electric power systems. IEEE Trans. Evol. Comput. 13, 913–918 (2009)CrossRefGoogle Scholar
  38. 38.
    Ghoshal, S.: Application of GA/GA-SA based fuzzy automatic generation control of a multi-area thermal generating system. Electric. Power Syst. Res. 70, 115–127 (2004)CrossRefGoogle Scholar
  39. 39.
    Dasgupta, K., Mandal, B., Dutta, P., Mandal, J., Dam, S.: A genetic algorithm (GA) based load balancing strategy for cloud computing. Procedia Technol. 10, 340–347 (2013)CrossRefGoogle Scholar
  40. 40.
    Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8, 687–697 (2008)CrossRefGoogle Scholar
  41. 41.
    Dahiya, P., Sharma, V., Naresh, R.: Solution approach to automatic generation control problem using hybridized gravitational search algorithm optimized PID and FOPID controllers. Adv. Electr. Comput. Eng. 15, 23–34 (2015)CrossRefGoogle Scholar
  42. 42.
    Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179, 2232–2248 (2009)CrossRefGoogle Scholar
  43. 43.
    Sharma, Y., Saikia, L.: Automatic generation control of a multi-area ST—thermal power system using Grey Wolf Optimizer algorithm based classical controllers. Int. J. Electr. Power Energy Syst. 73, 853–862 (2015)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • B. V. S. Acharyulu
    • 1
  • Prakash Kumar Hota
    • 1
  • Banaja Mohanty
    • 1
  1. 1.Veer Surendra Sai University of TechnologySambalpurIndia

Personalised recommendations