access icon free Hybrid adaptive ‘gbest’-guided gravitational search and pattern search algorithm for automatic generation control of multi-area power system

In this research article, a maiden approach of hybrid adaptive ‘gbest’ guided gravitational search and pattern search (hGGSA-PS) optimization method are proposed for load frequency control (LFC) of multi-area interconnected power system considering the nonlinear effect of generation rate constraint (GRC). At first, the two area single stage thermal–thermal power system with conventional proportional integral derivative (PID) controller is analyzed and the parameters of the PID controller are optimized by the proposed technique. Initially, the ‘gbest’ guided gravitational search algorithm (GGSA) using integral time absolute error (ITAE) fitness function is used and then pattern search (PS) technique is employed to fine-tune the obtained best solution from the GGSA. The supremacy of the hGGSA-PS optimized PID controller is presented by comparing its results with other modern soft computing techniques. Later in order to demonstrate the robustness of the proposed controller, the sensitive analysis is performed. Finally, the proposed technique is extended to a two area multi-source power system. The parameters of the controller for each area are optimized using the novel hGGSA-PS technique. From the simulation results, it can be seen that the proposed technique has superior performance than the prior results with lesser settling time and different performance index values.

Inspec keywords: optimisation; search problems; load regulation; power system interconnection; performance index; three-term control; frequency control; hydrothermal power systems; power generation control; nonlinear control systems; thermal power stations

Other keywords: hGGSA-PS optimisation method; two-area single-stage thermal-thermal power system; generation rate constraint; automatic generation control; PID controller; ITAE fitness function; integral time absolute error; performance index; hydrothermal power system; nonlinear effect; load frequency control; Hybrid adaptive gbest-guided gravitational search; multi-area interconnected power system; proportional integral derivative controller; tie-line power deviation; multisource power system; pattern search algorithm

Subjects: Nonlinear control systems; Thermal power stations and plants; Combinatorial mathematics; Optimisation techniques; Optimisation techniques; Frequency control; Combinatorial mathematics; Hydroelectric power stations and plants; Control of electric power systems; Power system control

References

    1. 1)
      • 19. Sahu, R.K., Panda, S., Pradhan, S.: ‘A novel hybrid gravitational search and pattern search algorithm for load frequency control of nonlinear power system’, Appl. Soft Comput., 2015, 29, pp. 310327.
    2. 2)
      • 18. ES, A., Abd-Elazim, S.M.: ‘BFOA based design of PID controller for two area load frequency control with nonlinearities’, Int. J. Electr. Power Energy Syst., 2013, 51, pp. 224231.
    3. 3)
      • 2. Elgerd, O.I.: ‘Electric energy systems theory an introduction’ (Tata McGraw Hill, New Delhi, 2000, 2nd edn.).
    4. 4)
      • 5. Ali, E.S., Elazim, S.M.A.: ‘Bacteria foraging optimization algorithm based load frequency controller for interconnected power system’, Int. J. Electr. Power Energy Syst., 2011, 33, pp. 633638.
    5. 5)
      • 1. Kundur, P.: ‘Power system stability and control’ (Tata McGraw Hill, 2009, 8th reprint).
    6. 6)
      • 24. Pavlovsky, V., Steliuk, A.: Modelling of Automatic Generation Control in Power Systems in Gonzalez-Longatt, F., Luis Rueda, J. (Eds.) ‘Power Factory Applications for Power System Analysis’ (Springer International Publishing, Switzerland, 2015).
    7. 7)
      • 9. Rashedi, E., Pour, H.N., Saryazdi, S.: ‘Filter modelling using gravitational search algorithm’, Eng. Appl. Artif. Intell., 2011, 24, pp. 117122.
    8. 8)
      • 23. Panda, S., Yegireddy, N.K.: ‘Automatic generation control of multi-area power system using multi-objective non-dominated sorting genetic algorithm-II’, Int. J. Electr. Power Energy Syst., 2013, 53, pp. 5463.
    9. 9)
      • 14. Seyedali, M., Lewis, A.: ‘Adaptive gbest-guided gravitational search algorithm’, Neural Comput. Appl., 2014, 25, pp. 15691584.
    10. 10)
      • 13. Khadanga, R.K., Satapathy, J.K.: ‘A new hybrid GA–GSA algorithm for tuning damping controller parameters for a unified power flow controller’, Int. J. Electr. Power Energy Syst., 2015, 73, pp. 10601069.
    11. 11)
      • 15. Bao, Y., Hu, Z., Xiong, T.: ‘A PSO and pattern search based memetic algorithm for SVMs parameters optimization’, Neurocomputing, 2013, 117, pp. 98106.
    12. 12)
      • 4. Nanda, J., Mishra, S., Saikia, L.C.: ‘Maiden application of bacterial foraging based optimization technique in multi-area automatic generation control’, IEEE Trans. Power System, 2009, 24, pp. 602609.
    13. 13)
      • 21. Serhat, D., Yorukeren, N.: ‘Automatic generation control of the two area non-reheat thermal power system using gravitational search algorithm’, System, 2012, 1, pp. 12.
    14. 14)
      • 22. Sahu, R.K., Panda, S., Rout, U.K.: ‘DE optimized parallel 2-DOF PID controller for load frequency control of power system with governor dead-band nonlinearity’, Int. J. Electr. Power Energy Syst., 2013, 49, pp. 1933.
    15. 15)
      • 6. Rout, U.K., Sahu, R.K., Panda, S.: ‘Design and analysis of differential evolution algorithm based automatic generation control for interconnected power system’, Ain Shams Eng. J., 2013, 3, pp. 409421.
    16. 16)
      • 25. Chandrakala, K.R.M.V., 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., 2013, 53, pp. 375381.
    17. 17)
      • 11. Ibrahim, A.A., Mohamed, A., Shareef, H.: ‘Optimal power quality monitor placement in power systems using an adaptive quantum-inspired binary gravitational search algorithm’, Int. J. Electr. Power Energy Syst., 2014, 57, pp. 404413.
    18. 18)
      • 3. Saikia, L.C., Nanda, J., Mishra, S.: ‘Performance comparison of several classical controllers in AGC for multi-area interconnected thermal system’, Int. J. Electr. Power Energy Syst., 2011, 33, pp. 394401.
    19. 19)
      • 16. Al-Othman, A.K., Ahmed, N.A., AlSharidah, M.E., et al: ‘A hybrid real coded genetic algorithm – Pattern search approach for selective harmonic elimination of PWM AC/AC voltage controller’, Int. J. Electr. Power Energy Syst., 2013, 44, pp. 123133.
    20. 20)
      • 10. Ghasemi, A., Shayeghi, H., Alkhatib, H.: ‘Robust design of multi-machine power system stabilizers using fuzzy gravitational search algorithm’, Int. J. Electr. Power Energy Syst., 2013, 51, pp. 190200.
    21. 21)
      • 20. Sahu, R.K., Panda, S., Pradhan, S.: ‘A hybrid firefly algorithm and pattern search technique for automatic generation control of multi area power systems’, Int. J. Electr. Power Energy Syst., 2015, 64, pp. 0923.
    22. 22)
      • 17. Dolan, E.D., Lewis, R.M., Torczon, V.: ‘On the local convergence of pattern search’, SIAM J. Optimiz., 2003, 14, pp. 567583.
    23. 23)
      • 8. Rashedi, E., Pour, H.N., Saryazdi, S.: ‘GSA: a gravitational search algorithm’, Inf. Sci., 2009, 13, pp. 22322248.
    24. 24)
      • 12. Khadanga, R.K., Satapathy, J.K.: ‘Time delay approach for PSS and SSSC based coordinated controller design using hybrid PSO–GSA algorithm’, Int. J. Electr. Power Energy Syst., 2015, 71, pp. 262273.
    25. 25)
      • 7. Shabani, H., Vahidi, B., Ebrahimpour, M.: ‘A robust PID controller based on imperialist competitive algorithm for load-frequency control of power systems’, ISA Trans.., 2012, 52, pp. 8895.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2016.1542
Loading

Related content

content/journals/10.1049/iet-gtd.2016.1542
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading