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Hybrid flower pollination and pattern search algorithm optimized sliding mode controller for deregulated AGC system

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Abstract

A new evolutionary optimisation technique, namely hybrid flower pollination and pattern search (hFPA-PS) technique is developed to tune output feedback sliding mode controller (OFSMC)for a multi-sources interconnected deregulated automatic generation control (AGC) system. The developed algorithm is justified considering popular benchmark functions. The developed algorithm is applied first time for the AGC system, to tune parameters of different classical controllers. The supremacy of recommended approach is established by competing the dynamic system performances with FPA, fruit fly optimization (FOA) and particle swarm optimization (PSO) technique for different power scenario for different classical controllers. Dynamic response of the system with hFPA-PS optimized OFSMC is established to be better than the classical controllers. The dynamic response of the system is analysed in the presence of generation rate constraint (GRC), governor dead band (GDB) and time delay. Additionally, the supremacy of recommended approach is analysed with sensitivity analysis, under uncertainty of system parameters.

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Abbreviations

I:

Subscript ‘i’ referred to area-i (i = T, H, N)

LFC:

Load frequency control

FPA:

Flower pollination algorithm

PS:

Pattern search algorithm

GDB:

Governor dead band

GRC:

Generation rate constraint

ISE:

Integral absolute square error

I:

Integral

PI:

Proportional–integral

ID:

Integral–derivative

PID:

Proportional–integral–derivative

IDD:

Integral double derivative

PIDD:

Proportional integral double derivative

OFSMC:

Output feedback sliding mode controller

TGi :

Governor time constant

Tt :

Turbine time constant

KPs :

Power system gain

TPS :

Power system time constants

B1 & B2 :

Frequency bias constants

R1, R2& R3 :

Speed regulation constants

T12 :

Synchronizing time constant

ΔPD :

Load disturbance

Δfi :

Frequency deviations

ΔPtie :

Tie line power deviation

Kr :

Steam turbine reheat gain

Tr :

Reheater time constant

TW :

Starting time of water in penstock

TRS :

Hydro turbine speed governor reset time

TRH :

Hydro turbine speed governor transient droop time constant

TGH :

Hydro turbine speed governor main servo time constant

TRH1 :

Time constant of first LP turbine

TRH2 :

Time constant of second LP turbine

TT1 :

Time constant of nuclear turbine

KHI :

Gain of HP turbine

KR1 :

Gain of LP turbine

Ki :

Participation factor of each generating unit

KDC :

Gain of HVDC link

TDC :

Time constant of HVDC link

References

  • Abd-Elazim SM, Ali ES (2016) Load frequency controller design via BAT algorithm for nonlinear interconnected power system. Int J Electr Power Energy Syst 77C:166–177

    Google Scholar 

  • Abd-Elazim SM, Ali ES (2018) Load frequency controller design of a two-area system composing of PV grid and thermal generator via firefly algorithm. Neural Comput Appl 30(2):607–616

    Google Scholar 

  • Abdelaziz AY, Ali ES (2015a) Cuckoo search algorithm based load frequency controller design for nonlinear interconnected power system. Int J Electr Power Energy Syst 73C:632–643

    Google Scholar 

  • Abdelaziz AY, Ali ES (2015b) Static var compensator damping controller design based on flower pollination algorithm for a multi-machine power system. Electr Power Compon Syst 43(11):1268–1277

    Google Scholar 

  • Abdelaziz AY, Ali ES, Abd Elazim SM (2016a) Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems. Energy (Elsevier) 101C:506–518

    Google Scholar 

  • Abdelaziz AY, Ali ES, Abd Elazim SM (2016b) Flower pollination algorithm and loss sensitivity factors for optimal sizing and placement of capacitors in radial distribution systems. Int J Electr Power Energy Syst 78C:207–214

    Google Scholar 

  • Al Hamouz Z, Al Duwaish HN (2000) A new load frequency variable structure controller using genetic algorithms. Electr Power Syst Res 55:1–6

    Google Scholar 

  • Al-Hamouza Z, Al-Duwaisha H, Al-Musabib N (2011) Optimal design of a sliding mode AGC controller: application to a nonlinear interconnected model. Electr Power Syst Research 81:1403–1409

    Google Scholar 

  • Ali ES, Abd-Elazim SM (2013) BFOA based design of PID controller for two area load frequency control with nonlinearities. Int J Electr Power Energy Syst 51:224–231

    Google Scholar 

  • Al-Musabi N, Al-Hamouza Z, Al-Duwaish H, Al-Baiyat S (2003) Variable structure load frequency controller using particle swarm optimization technique. In: Proceedings of the 10th IEEE international conference on electronics, circuits and systems, pp 380–383

  • Al-Othman AK, Ahmed AN, AlSharidah ME, AlMekhaizim HA (2013) 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 44(1):123–133

    Google Scholar 

  • Arya Y, Kumar N (2017) BFOA-scaled fractional order fuzzy PID controller applied to AGC of multi-area multi-source electric power generating systems. Swarm Evol Comput 32:202–218

    Google Scholar 

  • BaghyaShree S, Kamaraj N (2016) Hybrid neuro fuzzy approach for automatic generation control in restructured power system. Int J Electr Power Energy Syst 74:274–285

    Google Scholar 

  • Beverani H (2009) Robust power system frequency control. Springer, New York

    Google Scholar 

  • Chandrakala KRMV, Balamurugan S, Sankara-narayanan K (2013) 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

    Google Scholar 

  • Chang CS, Fu W (1997) Area load frequency control using fuzzy gain scheduling of PI controllers. Electr Power Syst Research 42:145–152

    Google Scholar 

  • Christie RD, Bose A (1996) Load frequency control issues in power system operation after deregulation. IEEE Trans Power Syst 11(3):1191–1200

    Google Scholar 

  • Das D, Kothari ML, Kothari DP, Nanda J (1991) Variable structure control strategy to automatic generation control of interconnected reheat thermal systems. IEE Proc Control Theory Appl 138(6):579–585

    Google Scholar 

  • Debbarma S, Saikia LC, Sinha N (2013) AGC of a multi-area thermal system under deregulated environment using a non-integer controller. Electr Power Syst Res 95:175–183

    Google Scholar 

  • Dolan ED, Lewis RM, Torczon V (2003) On the local convergence of pattern search. SIAM J Optim 14(2):567–583

    MathSciNet  MATH  Google Scholar 

  • Donde V, Pai MA (2001) Simulation and optimization in an AGC system after deregulation. IEEE Trans Power Syst 16:481–488

    Google Scholar 

  • Draa A (2015) On the performances of the flower pollination algorithm—qualitative and quantitative analyses. Appl Soft Comput 34:349–371

    Google Scholar 

  • Elgerd OI (1983) Electric energy systems theory an introduction. Tata McGraw-Hill, New Delhi

    Google Scholar 

  • Elgerd OI, Fosha C (1970) Optimum megawatt frequency control of multi-area electric energy systems. IEEE Trans Power Appl Syst 89(4):556–563

    Google Scholar 

  • Fosha CE, Elgerd OI (1970) The megawatt frequency control problem: a new approach via optimal control theory. IEEE Trans Power Appl Syst 89(4):563–577

    Google Scholar 

  • Golshannavaz S, Khezri R, Esmaeeli M, Siano P (2018) A two-stage robust-intelligent controller design for efficient LFC based on Kharitonov theorem and fuzzy logic. J Ambient Intell Human Comput 9(5):1445–1454

    Google Scholar 

  • Hsu Y, Chan W (1984) Optimal variable structure controller for the load–frequency control of interconnected hydrothermal power systems. Int J Electr Power Energy Syst 6(4):221–229

    Google Scholar 

  • Itkis V (1976) Control systems of variable structure. Keter Publishing House, Jerusalem

    Google Scholar 

  • Khan ZA, Zafar A, Javaid S, Aslam S, Rahim MH, Javaid N (2019) Hybrid meta-heuristic optimisation based home energy management system in smart grid. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-018-01169-y

    Article  Google Scholar 

  • Khuntia SR, Panda S (2012) Simulation study for automatic generation control of a multi-area power system by ANFIS approach. Appl Soft Comput 12:333–341

    Google Scholar 

  • Kundur P (1994) Power system stability and control. Mc-Grall Hill, New York

    Google Scholar 

  • Mohanty B (2015) TLBO optimized sliding mode controller for multi-area multi-source nonlinear interconnected AGC system. Electr Power Energy Syst 73:872–881

    Google Scholar 

  • Mohanty B, Hota PK (2015) Comparative performance analysis of fruit fly optimisation algorithm for multi-area multisource automatic generation control under deregulated environment. IET Gen Trans Distr 9(14):1845–1855

    Google Scholar 

  • Nanda J, Sreedhar M, Dasgupta A (2015) A new technique in hydro-thermal interconnected automatic generation control system using minority charged carrier inspired algorithm. Int J Electr Power Energy Syst 68:259–268

    Google Scholar 

  • Parmar KPS, Majhi S, Kothari DP (2012a) Load frequency control of a realistic power system with multi-source power generation. Electr Power Energy Syst 42:426–433

    Google Scholar 

  • Parmar KPS, Majhi S, Kothari DP (2012b) Improvement of dynamic performance of LFC of the two area power system an analysis using MATLAB. Int J Comput Appl 40(10):28–32

    Google Scholar 

  • Parmar KPS, Majhi S, Kothari DP (2014) LFC of an interconnected power system with multi-source power generation in deregulated power environment. Int J Electr Power Energy Syst 57:277–286

    Google Scholar 

  • Rajesh KS, Dash SS (2019) Load frequency control of autonomous power system using adaptive fuzzy based PID controller optimized on improved sine cosine algorithm. J Ambient Intell Humaniz Comput 10(6):2361–2373

    Google Scholar 

  • Sahu RK, Panda S, Yegireddy NK (2014) A novel hybrid DE-PS optimized fuzzy PI/PID controller for load frequency control of multi-area interconnected power systems. J Process Control 24:1596–1608

    Google Scholar 

  • Sahu RK, Panda S, Sekhar GTC (2015) 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

    Google Scholar 

  • Saikia LC, Nanda J, Mishra S (2011a) Performance comparison of several classical controllers in AGC for multi-area interconnected thermal system. Int J Elect Power Energy Syst 33:394–401

    Google Scholar 

  • Saikia LC, Mishra S, Sinha N, Nanda J (2011b) Automatic generation control of a multi-area hydro thermal system using reinforced learning neural network controller. Int J Electr Power Energy Syst 33:1101–1108

    Google Scholar 

  • Sathya MR, Ansari MMT (2015) Load frequency control using Bat inspired algorithm based dual mode gain scheduling of PI controllers for interconnected power system. Int J Electr Power Energy Syst 64:365–374

    Google Scholar 

  • Sekhar GTC, Sahu RK, Baliarsingh AK, Panda S (2016) Load frequency control of power system under deregulated environment using optimal firefly algorithm. Int J Electr Power Energy Syst 74:195–211

    Google Scholar 

  • Shayeghi H, Shayanfar HA, Malik OP (2007) Robust decentralized neural networks based LFC in a deregulated power system. Electr Power Syst Research 77:241–251

    Google Scholar 

  • Sivarmakrishnan Y, Hariharan MV, Srisailam MC (1984) Design of variable structure load frequency controller using pole assignment technique. Int J Control 40:487–498

    MathSciNet  MATH  Google Scholar 

  • Su S, Zhao S (2017) A hierarchical hybrid of genetic algorithm and particle swarm optimization for distributed clustering in large-scale wireless sensor networks. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-017-0619-9

    Article  Google Scholar 

  • Tahani M, Babayan N, Astaraei FR, Moghadam AA (2015) Multi objective optimization of horizontal axis tidal current turbines, using meta-heuristics algorithms. Energy Convers Manag 103:487–498

    Google Scholar 

  • Wang Y, Zhou R, Wen C (1993) Robust load–frequency controller design for power systems. IEE Proc C Gen Trans Distr 140(1):11–16

    Google Scholar 

  • Yang X, Karamanoglu S (2013) Multi-objective flower algorithm for optimization. Proced Comput Sci 18:861–868

    Google Scholar 

  • Yang M, Lu H (1999) Sliding mode load–frequency controller design for dynamic stability enhancement of large-scale interconnected power systems, In: ISIE 99: proceedings of the IEEE international symposium on industrial electronics, Bled, Slovenia, pp 1316–1321

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Authors and Affiliations

  1. Department of Electrical Engineering, Veer Surendra Sai University of Technology (VSSUT), Burla, Odisha, 768018, India

    Banaja Mohanty

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  1. Banaja Mohanty
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Correspondence to Banaja Mohanty.

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Appendices

Appendix 1

B1= B2= 0.4312 p.u.MW/Hz; Prt= 2000 MW; PL = 1840 MW; R1= R2= R3= 2.4 Hz/p.u.; TSG= 0.08 s; TT= 0.3 s; KR= 0.3; TR= 10 s; KPS1= KPS2= 68.9566 Hz/p.u.MW; TPS1= TPS2= 11.49 s; T12= 0.545; a12= − 1; TW= 1 s; TRS= 5 s; TRH= 28.75 s; TGN= 0.2 s; KHI= 2; KR1= 0.3; TT1= 0.5 s; TRH1= 7 s; TRH2= 9 s;KT = 0. 543478; KH = 0.326084; KN = 0.130438;

Appendix 2

See Tables 7, 8.

Table 7 Sensitivity analysis
Full size table
Table 8 Statistical analysis of performances comparison for hFPA-PS tuned SMC controller and FOA tuned PIDD controller
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Mohanty, B. Hybrid flower pollination and pattern search algorithm optimized sliding mode controller for deregulated AGC system. J Ambient Intell Human Comput 11, 763–776 (2020). https://doi.org/10.1007/s12652-019-01348-5

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