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Multi-verse Optimization Algorithm for LFC of Power System with Imposed Nonlinearities Using Three-Degree-of-Freedom PID Controller

  • Joyti MudiEmail author
  • Chandan K. Shiva
  • V. Mukherjee
Research paper
  • 27 Downloads

Abstract

The dynamic controller design problem for the load frequency control (LFC) of a realistic interconnected power system model is being studied in this paper. In view of this, the design and LFC performance analysis of three-degree-of-freedom proportional–integral–derivative (3DOF-PID) controller, intended by the proposed multi-verse optimization (MVO) algorithm, is presented. The simplicity of the structure and acceptability of the responses of the well-known proportional–integral–derivative controller, inherently, forces the higher DOF to be employed. The proposed MVO algorithm efficiently balances exploration and exploitation of the search space that yields promising solutions over the course of iteration. In response to this, it offers superior results as constrained optimization task. The investigated test system is a four-area configuration having each area composed of identical reheated thermal unit. The considered system is also installed with interline power flow controller in one of the areas. In the aforesaid system, the important physical constraints like time delay, governor deadband, boiler dynamics and generation rate constraint are also imposed. The performance of MVO-based design controller is compared to genetic algorithm-based one. The presented simulation results reveal the dominance of MVO-based 3DOF-PID controller in terms of settling time, peak deviation and magnitude of oscillations with the other investigated controller types. Additionally, sensitivity of the MVO-based designed 3DOF-PID controller from nominal condition to under loading and model parametric uncertainties is studied. This study reveals that the designed controller produces almost similar responses.

Keywords

Load frequency control (LFC) Multi-verse optimization (MVO) Physical constraints Reheat thermal power system Three-degree-of-freedom PID (3DOF-PID) controller 

References

  1. Abdel-Magid YL, Dawoud MM (1995) Genetic algorithms applications in load frequency control genetic algorithms in engineering systems: innovations and applications. Presented at the first international conference on (Conf. Publ. No. 414), GALESIAGoogle Scholar
  2. Arya Y (2018a) Automatic generation control of two-area electrical power systems via optimal fuzzy classical controller. J Frankl Inst 355(5):2662–2688MathSciNetCrossRefGoogle Scholar
  3. Arya Y (2018b) Improvement in automatic generation control of two-area electric power systems via a new fuzzy aided optimal PIDN-FOI controller. ISA Trans 80:475–490CrossRefGoogle Scholar
  4. Arya Y, Kumar N (2017) Optimal control strategy-based AGC of electrical power systems: a comparative performance analysis. Optim Control Appl Methods 38(6):982–992MathSciNetCrossRefGoogle Scholar
  5. Bengiamin NN, Chan WC (1982) Variable structure control of electric power generation. IEEE Trans Power Appl Syst 101:376–380CrossRefGoogle Scholar
  6. Çam E, Kocaarslan I (2005) Load frequency control in two area power systems using fuzzy logic controller. Energy Convers Manag 46:233–243CrossRefGoogle Scholar
  7. Chandrakala KRMV, Balamurugan S (2016) Simulated annealing based optimal frequency and terminal voltage control of multi source multi area system. Int J Electr Power Energy Syst 78:823–829CrossRefGoogle Scholar
  8. Dash P, Saikia LC, Sinha N (2015) Comparison of performances of several FACTS devices using cuckoo search algorithm optimized 2DOF controllers in multi-area AGC. Int J Electr Power Energy Syst 65:316–324CrossRefGoogle Scholar
  9. Davies PC (1978) Thermodynamics of black holes. Rep Prog Phys 41:1313CrossRefGoogle Scholar
  10. Debbarma S, Saikia LC, Sinha N (2014a) Solution to automatic generation control problem using firefly algorithm optimized I λ D μ controller. ISA Trans 53:358–366CrossRefGoogle Scholar
  11. Debbarma S, Saikia LC, Sinha N (2014b) Automatic generation control using two degree of freedom fractional order PID controller. Int J Electr Power Energy Syst 58:120–129CrossRefGoogle Scholar
  12. Debbarma S, Saikia LC, Sinha N (2014c) Robust two-degree-of-freedom controller for automatic generation control of multi-area system. Int J Electr Power Energy Syst 63:878–886CrossRefGoogle Scholar
  13. Eardley DM (1974) Death of white holes in the early universe. Phys Rev Lett 33:442CrossRefGoogle Scholar
  14. El-Hameed MA, El-Fergany AA (2016) Water cycle algorithm-based load frequency controller for interconnected power systems comprising non-linearity. IET Gener Transm Distrib 10(15):3950–3961CrossRefGoogle Scholar
  15. Golpira H, Bevrani H, Golpira H (2011) Application of GA optimization for automatic generation control design in an interconnected power system. Energy Convers Manag 52:2247–2255CrossRefGoogle Scholar
  16. Gozde H, Taplamacioglu MC, Kocaarslan I (2012) Comparative performance analysis of artificial bee colony algorithm in automatic generation control for interconnected reheat thermal power system. Int J Electr Power Energy Syst 42:167–178CrossRefGoogle Scholar
  17. Jangir P, Parmar SA, Trivedi IN, Bhesdadiya RH (2017) A novel hybrid particle swarm optimizer with multi verse optimizer for global numerical optimization and optimal reactive power dispatch problem. Eng Sci Technol Int J 20(2):570–586CrossRefGoogle Scholar
  18. Johnson MA, Mohammad HM (2005) PID control: new identification and design methods. Springer, BerlinCrossRefGoogle Scholar
  19. Karthikeyan K, Dhal PK (2017) Multi verse optimization (MVO) technique based voltage stability analysis through continuation power flow in IEEE 57 bus. Energy Procedia 117:583–591CrossRefGoogle Scholar
  20. Khuntia SR, Panda S (2012) Simulation study for automatic generation control of a multi-area power system by ANFIS approach. Appl Soft Comput 12:333–341CrossRefGoogle Scholar
  21. Kim DH (2012) Design and tuning approach of 3-DOF emotion intelligent PID (3-DOF-EI-PID) controller. In: Sixth UKSim/AMSS European modelling symposium on computer modelling and simulation, Korea, pp 74–77Google Scholar
  22. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513CrossRefGoogle Scholar
  23. Morris MS, Thorne KS (1988) Wormholes in space time and their use for interstellar travel: a tool for teaching general relativity. Am J Phys 56:395–412CrossRefGoogle Scholar
  24. Nise NS (2006) Control system engineering. Wiley, PomanazbMATHGoogle Scholar
  25. Parmar KPS, Majhi S, Kothari DP (2012) Load frequency control of a realistic power system with multi-source power generation. Int J Electr Power Energy Syst 42:426–433CrossRefGoogle Scholar
  26. Rahman A, Saikia LC, Sinha N (2015) Load frequency control of a hydro-thermal system under deregulated environment using biogeography-based optimised three degree-of-freedom integral–derivative controller. IET Gener Transm Distrib 9(15):2284–2293CrossRefGoogle Scholar
  27. Rahman A, Saikia LC, Sinha N (2016) Maiden application of hybrid pattern search biogeography based optimisation technique in automatic generation control of a multi-area system incorporating interline power flow controller. IET Gener Transm Distrib 10(7):1654–1662CrossRefGoogle Scholar
  28. Sahu RK, Panda S, Rout UK (2013) 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 49:19–33CrossRefGoogle Scholar
  29. Sahu RK, Panda S, Padhan S (2015) A hybrid firefly algorithm and pattern search technique for automatic generation control of multi area power systems. Int J Electr Power Energy Syst 64:9–23CrossRefGoogle Scholar
  30. Saikia LC, Nanda J, Mishra S (2011) Performance comparison of several classical controllers in AGC for multi-area interconnected thermal system. Int J Electr Power Energy Syst 33:394–401CrossRefGoogle Scholar
  31. Shabani H, Vahidi B, Ebrahimpour M (2012) A robust PID controller based on imperialist competitive algorithm for load-frequency control of power systems. ISA Trans 52:88–95CrossRefGoogle Scholar
  32. Tan W (2010) Unified tuning of PID load frequency controller for power systems via IMC. IEEE Trans Power Syst 25(1):341–350CrossRefGoogle Scholar
  33. Teerathana S, Yokoyama A (2004) An optimal power flow control method of power system using interline power flow controller (IPFC). In: IEEE region 10 conference TENCON, vol 3, pp 343–346Google Scholar

Copyright information

© Shiraz University 2019

Authors and Affiliations

  1. 1.Department of Electrical EngineeringBankura Unnayani Institute of EngineeringBankuraIndia
  2. 2.Department of Electrical and Electronics EngineeringS R Engineering CollegeAnanthsagar, Hasanparthy, WarangalIndia
  3. 3.Department of Electrical EngineeringIndian Institute Technology (Indian School of Mines)DhanbadIndia

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