Skip to main content

Advertisement

Log in

Design of new fractional order PI–fractional order PD cascade controller through dragonfly search algorithm for advanced load frequency control of power systems

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Owing to integrating the dense range of distinct electric power sources, high volume of power generation units, abrupt and continuous changes in load demand, and rising utilization of power electronics, the electric power system (EPS) is striving for high-performance control schemes to counterwork the concerns depicted above. Additionally, it is highly creditable to have the controller structure as simple as possible from a viewpoint of practical implementation. Thus, this paper describes a virgin application of fractional order proportional integral–fractional order proportional derivative (FOPI–FOPD) cascade controller for load frequency control (LFC) of electric power generating systems. The proposed controller includes fractional order PI and fractional order PD controllers connected in cascade wherein orders of integrator (\(\lambda\)) and differentiator (\(\mu\)) may be fractional. The gains and fractional order parameters of the controller are concurrently tuned using recently proposed dragonfly search algorithm (DSA) by minimizing the integral time absolute error (ITAE) of frequency and tie-line power deviations. DSA is the mathematical model and computer simulation of static and dynamic swarming behaviors of dragonflies in nature, and its implementation in LFC studies is very rare, unveiling additional research gap to be bridged. Performance of the advocated approach is first explored on popular two-area thermal PS with/without governor dead band (GDB) nonlinearity and then on three-area hydrothermal PS with suitable generation rate constraints. To highlight the prominence and universality of our proposal, the work is extended to single-/multi-area multi-source EPSs. Several comparisons with DSA optimized FOPID controller and the relevant recent works for each test system indicate the contribution of proposed DSA optimized FOPI–FOPD cascade controller in alleviating settling time/undershoot/overshoot of frequency and tie-line power oscillations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Ali ES, Abd-Elazim SM (2011) Bacteria foraging optimization algorithm based load frequency controller for interconnected power system. Int J Electr Power Energy Syst 33(3):633–638

    Article  Google Scholar 

  • Arya Y (2019a) Impact of ultra-capacitor on automatic generation control of electric energy systems using an optimal FFOID controller. Int J Energy Res 43:8765–8778

    Article  Google Scholar 

  • Arya Y (2019b) A novel CFFOPI-FOPID controller for AGC performance enhancement of single and multi-area electric power systems. ISA Trans. https://doi.org/10.1016/j.isatra.2019.11.025

    Article  Google Scholar 

  • Arya Y (2019c) A new optimized fuzzy FOPI–FOPD controller for automatic generation control of electric power systems. J Frankl Inst 356(11):5611–5629

    Article  MATH  Google Scholar 

  • Arya Y (2019d) AGC of restructured multi-area multi-source hydrothermal power systems incorporating energy storage units via optimal fractional-order fuzzy PID controller. Neural Comput Appl 31(3):851–872

    Article  Google Scholar 

  • Arya Y, Kumar N (2017a) Design and analysis of BFOA-optimized fuzzy PI/PID controller for AGC of multi-area traditional/restructured electrical power systems. Soft Comput 21:6435–6452

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Azarmi R, Tavakoli-Kakhki M, Sedigh AK, Fatehi A (2015) Analytical design of fractional order PID controllers based on the fractional set-point weighted structure: case study in twin rotor helicopter. Mechatronics 31:222–233

    Article  Google Scholar 

  • Barisal AK (2015) Comparative performance analysis of teaching learning based optimization for automatic load frequency control of multi-source power system. Int J Electr Power Energy Syst 66:67–77

    Article  Google Scholar 

  • Çelik E (2020) Improved stochastic fractal search algorithm and modified cost function for automatic generation control of interconnected electric power systems. Eng Appl Artif Intell 88:103407

    Article  Google Scholar 

  • Dahiya P, Sharma V, Naresh R (2015) Solution approach to automatic generation control problem using hybridized gravitational search algorithm optimized PID and FOPID controllers. Adv Electr Comput Eng 15(2):23–34

    Article  Google Scholar 

  • Dash P, Saikia LC, Sinha N (2014) Comparison of performances of several Cuckoo search algorithm based 2DOF controllers in AGC of multi-area thermal system. Int J Electr Power Energy Syst 55:429–436

    Article  Google Scholar 

  • Dash P, Saikia LC, Sinha N (2016) Flower pollination algorithm optimized PI–PD cascade controller in automatic generation control of a multi-area power system. Electr Power Energy Syst 82:19–28

    Article  Google Scholar 

  • Gozde H, Taplamacioglu MC (2011) Automatic generation control application with craziness based particle swarm optimization in a thermal power system. Int J Electr Power Energy Syst 33(1):8–16

    Article  Google Scholar 

  • Gozde H, Taplamacioglu MC, Kocaarslan İ (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(1):167–178

    Article  Google Scholar 

  • Guha D, Roy PK, Banerjee S (2016a) Load frequency control of large scale power system using quasi-oppositional grey wolf optimization algorithm. Int J Eng Sci Technol 19(4):1693–1713

    Google Scholar 

  • Guha D, Roy PK, Banerjee S (2016b) Load frequency control of interconnected power system using grey wolf optimization. Swarm Evolut Comput 27:97–115

    Article  Google Scholar 

  • Guha D, Roy P, Banerjee S (2017a) Quasi-oppositional symbiotic organism search algorithm applied to load frequency control. Swarm Evolut Comput 33:46–67

    Article  Google Scholar 

  • Guha D, Roy PK, Banerjee S (2017b) Study of differential search algorithm based automatic generation control of an interconnected thermal-thermal system with governor dead-band. Appl Soft Comput 52:160–175

    Article  Google Scholar 

  • Guha D, Roy PK, Banerjee S (2018) Application of backtracking search algorithm in load frequency control of multi-area interconnected power system. Ain Shams Eng J 9:257–276

    Article  Google Scholar 

  • Hota PK, Mohanty B (2016) Automatic generation control of multi source power generation under deregulated environment. Int J Electr Power Energy Syst 75:205–214

    Article  Google Scholar 

  • Ismayil C, Sreerama KR, Sindhu TK (2014) Automatic generation control of single area thermal power system with fractional order PID (PIλDμ) controllers. In: Proceedings of the 3rd international conference on advances in control and optimization of dynamical systems, March 13–15, Kanpur, India, pp 552–557

  • Jesus IS, Machado JAT, Barbosa RS (2010) Control of a heat diffusion system through a fractional order nonlinear algorithm. Comput Math Appl 59(5):1687–1694

    Article  MathSciNet  MATH  Google Scholar 

  • Jezierski E, Ostalczyk P (2009) Fractional-order mathematical model of pneumatic muscle drive for robotic applications. Robot Motion Control LNCIS 396:113–122

    MathSciNet  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

    Article  Google Scholar 

  • Kumar N, Tyagi B, Kumar V (2016) Deregulated multi area AGC scheme using BBBC-FOPID controller. Arab J Sci Eng 42(7):2641–2649

    Article  Google Scholar 

  • Mandal B, Roy PK (2014) Multi-objective optimal power flow using quasi-oppositional teaching learning based optimization. Appl Soft Comput 21:590–606

    Article  Google Scholar 

  • Mohanty B, Panda S, Hota PK (2014a) Controller parameters tuning of differential evolution algorithm and its application to load frequency control of multi-source power system. Int J Electr Power Energy Syst 54:77–85

    Article  Google Scholar 

  • Mohanty B, Panda S, Hota PK (2014b) Differential evolution algorithm based automatic generation control for interconnected power systems with non-linearity. Alex Eng J 53:537–552

    Article  Google Scholar 

  • Mousavi Y, Alfi A (2015) A memetic algorithm applied to trajectory control by tuning of fractional order proportional-integral derivative controllers. Appl Soft Comput 36:599–617

    Article  Google Scholar 

  • Naderi E, Pourakbari-Kasmaei M, Abdi H (2019) An efficient particle swarm optimization algorithm to solve optimal power flow problem integrated with FACTS devices. Appl Soft Comput 80:243–262

    Article  Google Scholar 

  • Nanda J, Parida M, Kalam A (2006) Automatic generation control of a multi-area power system with conventional integral controllers. In: Proceedings AUPEC 2006, Melbourne, Australia

  • Nayak JR, Shaw B, Sahu BK (2018) Application of adaptive-SOS (ASOS) algorithm based interval type-2 fuzzy-PID controller with derivative filter for automatic generation control of an interconnected power system. Int J Eng Sci Technol 21(3):465–485

    Google Scholar 

  • Nithilasaravanan K, Thakwani N, Mishra P, Kumar V, Rana KPS (2019) Efficient control of integrated power system using self-tuned fractional-order fuzzy PID controller. Neural Comput Appl 31:4137–4155

    Article  Google Scholar 

  • Nour EL, Kouba Y, Menaa M, Hasni M, Boudour M (2018) A novel optimal combined fuzzy PID controller employing dragonfly algorithm for solving automatic generation control problem. Electr Power Compon Syst 46(19–20):2054–2070

    Google Scholar 

  • Oustaloup A, Levron F, Matthieu B, Nanot FM (2000) Frequency-band complex noninteger differentiator: characterization and synthesis. IEEE Trans Circuits Syst I Fundam Theory Appl 47(1):25–39

    Article  Google Scholar 

  • Padhy S, Panda S (2017) A hybrid stochastic fractal search and pattern search technique based cascade PI–PD controller for automatic generation control of multi-source power systems in presence of plug in electric vehicles. CAAI Trans Intell Technol 2:12–25

    Article  Google Scholar 

  • Padhy S, Panda S, Mahapatra S (2017) A modified GWO technique based cascade PI–PD controller for AGC of power systems in presence of plug in electric vehicles. Int J Eng Sci Technol 20(2):427–442

    Google Scholar 

  • Pan I, Das S (2015) Fractional order load-frequency control of interconnected power systems using chaotic multi-objective optimization. Appl Soft Comput 29:328–344

    Article  Google Scholar 

  • Panda S, Mohanty B, Hota PK (2013) Hybrid BFOA-PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems. Appl Soft Comput 13:4718–4730

    Article  Google Scholar 

  • 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 42:426–433

    Article  Google Scholar 

  • Patel NC, Debnath MK, Sahu BK, Das P (2019) 2DOF-PID controller-based load frequency control of linear/nonlinear unified power system. In: Bhaskar M, Dash S, Das S, Panigrahi B (eds) International conference on intelligent computing and applications. Advances in intelligent systems and computing, vol 846. Springer, Singapore

    Google Scholar 

  • Podlubny I (1999) Fractional-order systems and PIλDμ-controllers. IEEE Trans Autom Control 44(1):208–214

    Article  MathSciNet  MATH  Google Scholar 

  • Rout UK, Sahu RK, Panda S (2013) Design and analysis of differential evolution algorithm based automatic generation control for interconnected power system. Ain Shams Eng J 4(3):409–421

    Article  Google Scholar 

  • Saha S (2010) Design of a fractional order phase shaper for iso-damped control of a PHWR under step-back condition. IEEE Trans Nucl Sci 57(3):1602–1612

    Article  Google Scholar 

  • Sahu RK, Panda S, Rou K (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 49:19–33

    Article  Google Scholar 

  • Sahu RK, Panda S, Padhan S (2015a) A hybrid firefly algorithm and pattern search technique for automatic generation control of multi area power systems. Int J Electr Power Energy Syst 64:9–23

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Sahu RK, Panda S, Rout UK, Sahoo DK (2016a) Teaching learning based optimization algorithm for automatic generation control of power system using 2-DOF PID controller. Int J Electr Power Energy Syst 77:287–301

    Article  Google Scholar 

  • Sahu BK, Pati TK, Nayak JR, Panda S, Kar SK (2016b) A novel hybrid LUS-TLBO optimized fuzzy-PID controller for load frequency control of multi-source power system. Int J Electr Power Energy Syst 74:58–69

    Article  Google Scholar 

  • Saikia LC, Mishra S, Sinha N, Nanda J (2011) Automatic generation control of a multi area hydrothermal system using reinforced learning neural network controller. Int J Electr Power Energy Syst 33(4):1101–1108

    Article  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

    Article  Google Scholar 

  • Seyedali M (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073

    Article  Google Scholar 

  • Shiva CK, Shankar G, Mukherjee V (2015) Automatic generation control of power system using a novel quasi-oppositional harmony search algorithm. Int J Electr Power Energy Syst 73:787–804

    Article  Google Scholar 

  • Singh SP, Prakash T, Singh VP, Babu MG (2017) Analytic hierarchy process based automatic generation control of multi-area interconnected power system using Jaya algorithm. Eng Appl Artif Intell 60:35–44

    Article  Google Scholar 

  • Sivalingam R, Chinnamuthu S, Dash SS (2017) A hybrid stochastic fractal search and local unimodal sampling based multistage PDF plus (1 + PI) controller for automatic generation control of power systems. J Frankl Inst 354:4762–4783

    Article  MathSciNet  MATH  Google Scholar 

  • Sondhi S, Hote YV (2014) Fractional order PID controller for load frequency control. Energy Convers Manag 85:343–353

    Article  Google Scholar 

  • Venkatesh M, Sudheer G (2017) Optimal load frequency regulation of micro-grid using dragonfly algorithm. Int Res J Eng Technol 4(8):978–981

    Google Scholar 

  • Vrdoljak K, Peric N, Petrovic I (2010) Sliding mode based load frequency controller in power systems. Electr Power Syst Res 80(5):514–527

    Article  Google Scholar 

  • Zamani A, Barakati SM, Yousofi-Darmian S (2016) Design of a fractional order PID controller using GBMO algorithm for load frequency control with governor saturation consideration. ISA Trans 64:56–66

    Article  Google Scholar 

  • Zeng GQ, Chen J, Dai YX, Li LM, Zheng CW, Chen MR (2015) Design of fractional order PID controller for automatic regulator voltage system based on multi-objective extremal optimization. Neurocomputing 160:173–184

    Article  Google Scholar 

  • Zhong J, Li L (2015) Tuning fractional-order PIλDμ controllers for a solid-core magnetic bearing system. IEEE Trans Control Syst Technol 23(4):1648–1656

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Emre Çelik.

Ethics declarations

Conflict of interest

The author does not have any conflict of interest.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix A

Appendix A

1.1 A.1 Nominal parameters of test system-1 are (Ali and Abd-Elazim 2011; Panda et al. 2013; Sahu et al. 2015, 2016)

\(f = 60\) Hz, \(B = 0.425\) p.u MW/Hz, \(R = 2.4\) Hz/pu, \(T_{\text{g}} = 0.03\) s, \(T_{\text{t}} = 0.3\) s, \(K_{\text{ps}} = 120\) Hz/pu, \(T_{\text{ps}} = 20\) s, \(T_{12} = 0.545\) p.u MW/rad.

1.2 A.2 Nominal parameters of test system-2 are (Panda et al. 2013; Sahu et al. 2016; Çelik 2020; Gozde and Taplamacioglu 2011)

\(f = 60\) Hz, \(B = 0.425\) p.u MW/Hz, \(R = 2.4\) Hz/pu, \(T_{\text{g}} = 0.2\) s, \(T_{\text{t}} = 0.3\) s, \(K_{\text{ps}} = 120\) Hz/pu, \(T_{\text{ps}} = 20\) s, \(T_{12} = 0.444\) p.u MW/rad.

1.3 A.3 Nominal parameters of test system-3 are (Panda et al. 2013; Çelik 2020; Nanda et al. 2006)

\(f = 60\) Hz, \(B = 0.425\) p.u MW/Hz, \(R = 2.4\) Hz/pu, \(T_{\text{g}} = 0.08\) s, \(K_{\text{r}} = 0.5\), \(T_{\text{r}} = 10\) s, \(T_{\text{t}} = 0.3\) s, \(K_{p} = 1.0\), \(K_{d} = 4.0\), \(K_{i} = 5.0\), \(T_{\text{w}} = 1\) s, \(K_{\text{ps}} = 120\) Hz/pu, \(T_{\text{ps}} = 20\) s, \(T_{12} = T_{23} = T_{13} = 0.086\) p.u MW/rad.

1.4 A.4 Nominal parameters of test system-4 are (Mohanty et al. 2014; Padhy et al. 2017; Parmar et al. 2012; Barisal 2015)

\(f = 60\) Hz, \(R = 2.4\) Hz/pu, \(T_{\text{sg}} = 0.08\) s, \(K_{\text{r}} = 0.3\), \(T_{\text{r}} = 10\) s, \(T_{\text{t}} = 0.3\) s, \(T_{\text{gh}} = 0.2\) s, \(T_{\text{rs}} = 5\) s, \(T_{\text{rh}} = 28.75\) s \(T_{\text{w}} = 1\) s, \(b_{\text{g}} = 0.05\) s, \(c_{\text{g}} = 1\), \(X_{\text{c}} = 0.6\) s, \(Y_{\text{c}} = 1\) s, \(T_{\text{cr}} = 0.01\) s, \(T_{\text{f}} = 0.23\) s, \(T_{\text{cd}} = 0.2\) s, \(K_{\text{T}} = 0.543478\) pu, \(K_{\text{H}} = 0.326084\) pu, \(K_{\text{G}} = 0.130438\) pu, \(K_{\text{ps}} = 68.9566\) Hz/pu MW, \(T_{\text{ps}} = 11.49\) s

1.5 A.5 Nominal parameters of test system-5 are (Mohanty et al. 2014; Padhy et al. 2017; Padhy and Panda 2017)

\(f = 60\) Hz, \(B = 0.4312\) pu, \(R = 2.4\) Hz/pu, \(T_{\text{sg}} = 0.08\) s, \(K_{\text{r}} = 0.3\), \(T_{\text{r}} = 10\) s, \(T_{\text{t}} = 0.3\) s, \(T_{\text{gh}} = 0.2\) s, \(T_{\text{rs}} = 5\) s, \(T_{\text{rh}} = 28.75\) s \(T_{\text{w}} = 1\) s, \(b_{\text{g}} = 0.05\) s, \(c_{\text{g}} = 1\), \(X_{\text{c}} = 0.6\) s, \(Y_{\text{c}} = 1\) s, \(T_{\text{cr}} = 0.01\) s, \(T_{\text{f}} = 0.23\) s, \(T_{\text{cd}} = 0.2\) s, \(K_{\text{T}} = 0.543478\) pu, \(K_{\text{H}} = 0.326084\) pu, \(K_{\text{G}} = 0.130438\) pu, \(T_{12} = 0.0433\), \(K_{\text{ps}} = 68.9566\) Hz/pu MW, \(T_{\text{ps}} = 11.49\) s

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Çelik, E. Design of new fractional order PI–fractional order PD cascade controller through dragonfly search algorithm for advanced load frequency control of power systems. Soft Comput 25, 1193–1217 (2021). https://doi.org/10.1007/s00500-020-05215-w

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-05215-w

Keywords

Navigation