http://iet.metastore.ingenta.com
1887

Multi-verse optimisation: a novel method for solution of load frequency control problem in power system

Multi-verse optimisation: a novel method for solution of load frequency control problem in power system

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Generation, Transmission & Distribution — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In this article, a maiden attempt has been made to derive an optimal and effective outcome of load frequency control problem (LFC) using a novel evolutionary algorithm called multi-verse optimisation. The main inspiration of this algorithm is based on three concepts in cosmology: white hole, black hole, and wormhole. To show the effectiveness, a four-area hydrothermal power plant with distinct proportional-integral-derivative (PID) controller is investigated at the first instant and then the study is forwarded to the five-area thermal power plant. To enhance the dynamic stability, an optimal PID plus double-derivative controller (PID + DD) is designed and included in the control areas. The superiority of the proposed method has been established over some recently addressed control algorithms by transient analysis method. To add some degree of non-linearity, generation rate constraint and governor dead band are included in the model and their impacts on the system dynamics have been examined. Finally, a random load perturbation is given to the test systems to affirm the robustness of the designed controllers.

References

    1. 1)
      • 1. Yesil, E., Guzelkaya, M., Eksin, I.: ‘Self-tuning fuzzy PID type load and frequency controller’, Energy Convers. Manage, 2004, 45, pp. 377390.
    2. 2)
      • 2. Guha, D., Roy, P.K., Banerjee, S.: ‘Load frequency control of large power system using quasi-oppositional grey wolf optimization algorithm’, Int. J. Eng. Sci. Technol., 2016, 19, (4), pp. 16931713.
    3. 3)
      • 3. Pandey, S.K., Mohanty, S.R., Kishore, N.: ‘A literature review on LFC for conventional and distributed generation power system’, Renew. Sustain. Energy Rev., 2013, 25, pp. 318324.
    4. 4)
      • 4. Saikia, L.C., Nanda, J., Mishra, S.: ‘Performance comparison of several classical controllers in AGC for multi-area interconnected thermal system’, Int. J. Elect. Power Energy Syst., 2011, 33, pp. 394401.
    5. 5)
      • 5. Saikia, L.C., Sinha, N., Nanda, J.: ‘Maiden application of bacterial foraging based fuzzy IDD controller in AGC of a multi-area hydrothermal system’, Int. J. Elect. Power Energy Syst., 2013, 45, pp. 98106.
    6. 6)
      • 6. Prakash, S., Sinha, S.K.: ‘Simulation based neuro-fuzzy hybrid intelligent PI control approach in four-area load frequency control of interconnected power system’, Appl. Soft Comput., 2014, 23, pp. 152164.
    7. 7)
      • 7. Yousef, H.: ‘Adaptive fuzzy logic load frequency control of multi-area power system’, Int. J. Elect. Power Energy Syst., 2015, 68, pp. 384395.
    8. 8)
      • 8. Tripathy, S.C., Hope, G.S., Malik, O.P.: ‘Optimisation of load-frequency control parameters for power systems with reheat steam turbines and governor dead band nonlinearity’, IEE Proc., 1982, 129, (1), pp. 1016.
    9. 9)
      • 9. Tripathy, S.C., Bhatti, T.S., Jha, C.S., et al: ‘Sampled data automatic generation control analysis with reheat steam turbines and governor dead band effects’, IEEE Trans. Power Apparatus Syst., 1984, PAS-103, (5), pp. 10451051.
    10. 10)
      • 10. Guha, D., Roy, P.K., Banerjee, S.: ‘Load frequency control of interconnected power system using grey wolf optimization’, Swarm Evol. Comput., 2016, 27, pp. 97115.
    11. 11)
      • 11. Sahu, R.K., Panda, S., Rout, U.K., et al: ‘Teaching learning based optimization algorithm for automatic generation control of power system using 2-DOF PID controller’, Int. J. Elect. Power Energy Syst., 2016, 77, pp. 287301.
    12. 12)
      • 12. Irsheid, A.M.: ‘Load frequency control and automatic generation control using fractional-order controllers’, Electr. Eng., 2010, 91, pp. 357368.
    13. 13)
      • 13. Patra, S., Sen, S., Ray, G.: ‘Design of robust load frequency controller: H loop shaping approach’, Electr. Power Compon. Syst., 2007, 35, (7), pp. 769783.
    14. 14)
      • 14. Saxsena, S., Hote, Y.V.: ‘Load frequency control in power system via internal model control scheme and model order reduction’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 27492757.
    15. 15)
      • 15. Mohanty, B.: ‘TLBO optimized sliding mode controller for multi-area multi-source nonlinear interconnected AGC system’, Int. J. Elect. Power Energy Syst., 2015, 73, pp. 872881.
    16. 16)
      • 16. Bevranic, H., Yasunori, M., Kiichiro, T.: ‘Sequential design of decentralized load frequency controller using µ-synthesis and analysis’, Energy Convers. Manag., 2004, 46, (4), pp. 865881.
    17. 17)
      • 17. Abd-Elazim, S.M., Ali, E.S.: ‘Load frequency controller design via BAT algorithm for nonlinear interconnected power system’, Int. J. Elect. Power Energy Syst., 2016, 77, pp. 166177.
    18. 18)
      • 18. Raju, M., Saikia, L.C., Sinha, N.: ‘Automatic generation control of a multi-area system using ant lion optimizer algorithm based PID plus second order derivative controller’, Int. J. Elect. Power Energy Syst., 2016, 80, pp. 5263.
    19. 19)
      • 19. Abdelaziz, A.Y., Ali, E.S.: ‘Cuckoo search algorithm based load frequency controller design for nonlinear interconnected power system’, Int. J. Elect. Power Energy Syst., 2015, 73, pp. 632643.
    20. 20)
      • 20. Mohanty, B., Panda, S., Hota, P.K.: ‘Controller parameters tuning of differential evolution algorithm and its application to load frequency control of multi-source power system’, Int. J. Elect. Power Energy Syst., 2014, 54, pp. 7785.
    21. 21)
      • 21. Daneshfar, F., Bevrani, H.: ‘Multiobjective design of load frequency control using genetic algorithms’, Elect. Power Energy Syst., 2012, 42, pp. 257263.
    22. 22)
      • 22. Farhangi, R., Boroushaki, M., Hosseini, S.H.: ‘Load–frequency control of interconnected power system using emotional learning-based intelligent controller’, Int. J. Elect. Power Energy Syst., 2012, 36, pp. 7683.
    23. 23)
      • 23. Guha, D., Roy, P.K., Banerjee, S.: ‘Application of backtracking search algorithm in load frequency control of multi-area interconnected power system’, Ain Shams Eng. J., 2016 (in press).
    24. 24)
      • 24. Pothiya, S., Ngamroo, I., Runggeratigul, S., et al: ‘Design of optimal fuzzy logic based PI controller using multiple Tabu search algorithm for load frequency control’, Int. J. Control Autom. Syst., 2006, 4, (2), pp. 155164.
    25. 25)
      • 25. Guha, D., Roy, P.K., Banerjee, S.: ‘Application of modified biogeography based optimization in AGC of an interconnected multi-unit multi-source AC-DC linked power system’, Int. J. Energy Opt. Eng., 2016, 5, (3), pp. 118.
    26. 26)
      • 26. Shankar, G., Mukherjee, V.: ‘Load frequency control of an autonomous hybrid power system by quasi-oppositional harmony search algorithm’, Int. J. Elect. Power Energy Syst., 2016, 78, pp. 715734.
    27. 27)
      • 27. Shiva, C.K., Mukherjee, V.: ‘A novel quasi-oppositional harmony search algorithm for AGC optimization of three-area multi-unit power system after deregulation’, Int. J. Eng. Sci. Tech., 2016, 19, pp. 395420.
    28. 28)
      • 28. Shiva, C.K., Mukherjee, V.: ‘A novel quasi-oppositional harmony search algorithm for automatic generation control of power system’, Appl. Soft Comput., 2015, 35, pp. 749765.
    29. 29)
      • 29. Sahu, R.K., Panda, S., Pradhan, P.C.: ‘Design and analysis of hybrid firefly algorithm-pattern search based fuzzy PID controller for LFC of multi area power systems’, Int. J. Elect. Power Energy Syst., 2015, 69, pp. 200212.
    30. 30)
      • 30. Panda, S., Mohanty, B., Hota, P.K.: ‘Hybrid BFOA–PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems’, Appl. Soft Comput., 2013, 13, pp. 47184730.
    31. 31)
      • 31. Alam, S., Singh, A., Guha, D.: ‘Optimal solutions of load frequency control problem using oppositional krill herd algorithm’. IEEE First Int. Conf. on Control, Measurement and Instrumentation (CMI), Jadavpur University, India, 2016, pp. 610.
    32. 32)
      • 32. Guha, D., Roy, P.K., Banerjee, S.: ‘Application of krill herd algorithm for optimum design of load frequency controller for multi-area power system network with generation rate constraint’. Proc. fourth Int. Conf. on Frontiers in Intelligent Computing: Theory and Applications (FICTA) 2015, Advances in Intelligent Systems and Computing, 2015, vol. 404, pp. 245257.
    33. 33)
      • 33. Mirjalili, S.: ‘Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm’, Knowledge-Based Syst., 2015, 89, pp. 228249.
    34. 34)
      • 34. Mirjalili, S.: ‘Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems’, Neural Comput. Appl., 2016, 27, (4), pp. 10531073.
    35. 35)
      • 35. Civicioglu, P.: ‘Transforming geocentric Cartesian coordinates to geodetic coordinates by using differential search algorithm’, Comput. Geosci., 2012, 46, pp. 229247.
    36. 36)
      • 36. Rao, R.V.: ‘Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems’, Int. J. Ind. Eng. Comput., 2016, 7, pp. 116.
    37. 37)
      • 37. Mirjalili, S., Mirjalili, S.M., Hatamlou, A.: ‘Multi-verse optimizer: a nature-inspired algorithm for global optimization’, Neural Comput. Appl., 2016, 27, (2), pp. 495513.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2017.0296
Loading

Related content

content/journals/10.1049/iet-gtd.2017.0296
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address