© The Institution of Engineering and Technology
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 multiverse 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 fourarea hydrothermal power plant with distinct proportionalintegralderivative (PID) controller is investigated at the first instant and then the study is forwarded to the fivearea thermal power plant. To enhance the dynamic stability, an optimal PID plus doublederivative 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 nonlinearity, 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. Yesil, E., Guzelkaya, M., Eksin, I.: ‘Selftuning fuzzy PID type load and frequency controller’, Energy Convers. Manage, 2004, 45, pp. 377–390.

2)

2. Guha, D., Roy, P.K., Banerjee, S.: ‘Load frequency control of large power system using quasioppositional grey wolf optimization algorithm’, Int. J. Eng. Sci. Technol., 2016, 19, (4), pp. 1693–1713.

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. 318–324.

4)

4. Saikia, L.C., Nanda, J., Mishra, S.: ‘Performance comparison of several classical controllers in AGC for multiarea interconnected thermal system’, Int. J. Elect. Power Energy Syst., 2011, 33, pp. 394–401.

5)

5. Saikia, L.C., Sinha, N., Nanda, J.: ‘Maiden application of bacterial foraging based fuzzy IDD controller in AGC of a multiarea hydrothermal system’, Int. J. Elect. Power Energy Syst., 2013, 45, pp. 98–106.

6)

6. Prakash, S., Sinha, S.K.: ‘Simulation based neurofuzzy hybrid intelligent PI control approach in fourarea load frequency control of interconnected power system’, Appl. Soft Comput., 2014, 23, pp. 152–164.

7)

7. Yousef, H.: ‘Adaptive fuzzy logic load frequency control of multiarea power system’, Int. J. Elect. Power Energy Syst., 2015, 68, pp. 384–395.

8)

8. Tripathy, S.C., Hope, G.S., Malik, O.P.: ‘Optimisation of loadfrequency control parameters for power systems with reheat steam turbines and governor dead band nonlinearity’, IEE Proc., 1982, 129, (1), pp. 10–16.

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, PAS103, (5), pp. 1045–1051.

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. 97–115.

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 2DOF PID controller’, Int. J. Elect. Power Energy Syst., 2016, 77, pp. 287–301.

12)

12. Irsheid, A.M.: ‘Load frequency control and automatic generation control using fractionalorder controllers’, Electr. Eng., 2010, 91, pp. 357–368.

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. 769–783.

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. 2749–2757.

15)

15. Mohanty, B.: ‘TLBO optimized sliding mode controller for multiarea multisource nonlinear interconnected AGC system’, Int. J. Elect. Power Energy Syst., 2015, 73, pp. 872–881.

16)

16. Bevranic, H., Yasunori, M., Kiichiro, T.: ‘Sequential design of decentralized load frequency controller using µsynthesis and analysis’, Energy Convers. Manag., 2004, 46, (4), pp. 865–881.

17)

17. AbdElazim, 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. 166–177.

18)

18. Raju, M., Saikia, L.C., Sinha, N.: ‘Automatic generation control of a multiarea system using ant lion optimizer algorithm based PID plus second order derivative controller’, Int. J. Elect. Power Energy Syst., 2016, 80, pp. 52–63.

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. 632–643.

20)

20. Mohanty, B., Panda, S., Hota, P.K.: ‘Controller parameters tuning of differential evolution algorithm and its application to load frequency control of multisource power system’, Int. J. Elect. Power Energy Syst., 2014, 54, pp. 77–85.

21)

21. Daneshfar, F., Bevrani, H.: ‘Multiobjective design of load frequency control using genetic algorithms’, Elect. Power Energy Syst., 2012, 42, pp. 257–263.

22)

22. Farhangi, R., Boroushaki, M., Hosseini, S.H.: ‘Load–frequency control of interconnected power system using emotional learningbased intelligent controller’, Int. J. Elect. Power Energy Syst., 2012, 36, pp. 76–83.

23)

23. Guha, D., Roy, P.K., Banerjee, S.: ‘Application of backtracking search algorithm in load frequency control of multiarea interconnected power system’, Ain Shams Eng. J., 2016 ().

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. 155–164.

25)

25. Guha, D., Roy, P.K., Banerjee, S.: ‘Application of modified biogeography based optimization in AGC of an interconnected multiunit multisource ACDC linked power system’, Int. J. Energy Opt. Eng., 2016, 5, (3), pp. 1–18.

26)

26. Shankar, G., Mukherjee, V.: ‘Load frequency control of an autonomous hybrid power system by quasioppositional harmony search algorithm’, Int. J. Elect. Power Energy Syst., 2016, 78, pp. 715–734.

27)

27. Shiva, C.K., Mukherjee, V.: ‘A novel quasioppositional harmony search algorithm for AGC optimization of threearea multiunit power system after deregulation’, Int. J. Eng. Sci. Tech., 2016, 19, pp. 395–420.

28)

28. Shiva, C.K., Mukherjee, V.: ‘A novel quasioppositional harmony search algorithm for automatic generation control of power system’, Appl. Soft Comput., 2015, 35, pp. 749–765.

29)

29. Sahu, R.K., Panda, S., Pradhan, P.C.: ‘Design and analysis of hybrid firefly algorithmpattern search based fuzzy PID controller for LFC of multi area power systems’, Int. J. Elect. Power Energy Syst., 2015, 69, pp. 200–212.

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. 4718–4730.

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. 6–10.

32)

32. Guha, D., Roy, P.K., Banerjee, S.: ‘Application of krill herd algorithm for optimum design of load frequency controller for multiarea 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. 245–257.

33)

33. Mirjalili, S.: ‘Mothflame optimization algorithm: a novel natureinspired heuristic paradigm’, KnowledgeBased Syst., 2015, 89, pp. 228–249.

34)

34. Mirjalili, S.: ‘Dragonfly algorithm: a new metaheuristic optimization technique for solving singleobjective, discrete, and multiobjective problems’, Neural Comput. Appl., 2016, 27, (4), pp. 1053–1073.

35)

35. Civicioglu, P.: ‘Transforming geocentric Cartesian coordinates to geodetic coordinates by using differential search algorithm’, Comput. Geosci., 2012, 46, pp. 229–247.

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. 1–16.

37)

37. Mirjalili, S., Mirjalili, S.M., Hatamlou, A.: ‘Multiverse optimizer: a natureinspired algorithm for global optimization’, Neural Comput. Appl., 2016, 27, (2), pp. 495–513.
http://iet.metastore.ingenta.com/content/journals/10.1049/ietgtd.2017.0296
Related content
content/journals/10.1049/ietgtd.2017.0296
pub_keyword,iet_inspecKeyword,pub_concept
6
6