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Improving the Performance of AVR System Using Grasshopper Evolutionary Technique

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Green Technology for Smart City and Society

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 151))

Abstract

The design of the optimal controller with an appropriate setting of its parameters is very much essential for an automatic voltage regulator (AVR) system. Even though a lot of research has been undertaken in the past few decades, as no unanimously accepted methodology does not result yet, it is an open and very important field of research for designing an optimal controller for AVR. In this paper, an advanced version of the classical proportional-integral–differential (PID) control technique based on fractional calculus is designed titled as fractional order-PID control (FO-PID) controller for the AVR operation. The new strategy has been employed for computing the gain parameters of the controller unlike the conventional controller used generally in the real-time applications of an AVR. To enhance further the performance of FO-PID, a grasshopper evolutionary technique (GET) has been adapted for optimally setting the parameters for the enhanced performance of the controller. A comparative analysis of the proposed GET-FO-PID controller to justify its performance with conventional tuned PID controller using Ziegler–Nichols, Nelder Mead, and GET is presented and analyzed with. It has been demonstrated that the proposed approach produces a substantial improvement in the AVR system response with better controllability and stability. To justify the performance of the proposed GET-FO-PID controller including the AVR, time-domain analysis is also presented through MATLAB simulation.

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References

  1. Kundur PS (2017) Power system stability. In Power system stability and control. CRC Press, pp 8–1

    Google Scholar 

  2. Khalid A, Shahid AH, Zeb K, Ali A, Haider A (2016) Comparative assessment of classical and adaptive controllers for Automatic Voltage Regulator. In: 2016 international conference on advanced mechatronic systems (ICAMechS), pp 538–543. IEEE

    Google Scholar 

  3. Sahu BK, Panda S, Mohanty PK, Mishra N (2012) Robust analysis and design of PID controlled AVR system using Pattern Search algorithm. In: 2012 IEEE international conference on power electronics, drives and energy systems (PEDES), pp 1–6. IEEE

    Google Scholar 

  4. Gaing ZL (2004) A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Trans Energy Convers 19(2):384–391

    Article  Google Scholar 

  5. Mukherjee V, Ghoshal SP (2007) Intelligent particle swarm optimized fuzzy PID controller for AVR system. Electr Power Syst Res 77(12):1689–1698

    Article  Google Scholar 

  6. Hang CC, Åström KJ, Ho WK (1991) Refinements of the Ziegler–Nichols tuning formula. In: IEE Proceedings D (Control Theory and Applications), vol 138, No 2, pp 111–118. IET Digital Library.

    Google Scholar 

  7. Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7(4):308–313

    Article  MathSciNet  Google Scholar 

  8. Podlubny I (1999) Fractional-order systems and PI/sup/spl lambda//D/sup/spl mu//-controllers. IEEE Trans Autom Control 44(1):208–214

    Article  MathSciNet  Google Scholar 

  9. Åström KJ, Hägglund T (2001) The future of PID control. Control Eng Pract 9(11):1163–1175

    Article  Google Scholar 

  10. Tepljakov A, Alagoz BB, Yeroglu C, Gonzalez E, HosseinNia SH, Petlenkov E (2018) FOPID controllers and their industrial applications: a survey of recent results. IFAC-PapersOnLine 51(4):25–30

    Article  Google Scholar 

  11. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

    Article  Google Scholar 

  12. Tripathy MC, Biswas K, Sen S (2013) A design example of a fractional-order Kerwin–Huelsman–Newcomb biquad filter with two fractional capacitors of different order. Circuits Syst Signal Proces 32(4):1523–1536

    Article  MathSciNet  Google Scholar 

  13. Mafarja M, Aljarah I, Heidari AA, Hammouri AI, Faris H, Ala’M AZ, Mirjalili S (2018) Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowl Based Syst 145:25–45

    Google Scholar 

  14. Hekimoğlu B, Ekinci S (2018) Grasshopper optimization algorithm for automatic voltage regulator system. In: 2018 5th international conference on electrical and electronic engineering (ICEEE), pp 152–156. IEEE

    Google Scholar 

  15. Dulău M, Gligor A, Dulău TM (2017) Fractional order controllers versus integer order controllers. Procedia Eng 181:538–545

    Article  Google Scholar 

  16. Duman S, Yörükeren N, Altaş IH (2016) Gravitational search algorithm for determining controller parameters in an automatic voltage regulator system. Turkish J Electr Eng Comput Sci 24(4):2387–2400

    Article  Google Scholar 

  17. Valério D, Da Costa JS (2006) Tuning of fractional PID controllers with Ziegler–Nichols-type rules. Signal Proces 86(10):2771–2784

    Article  Google Scholar 

  18. Baig WM, Hou Z, Ijaz S (2017) Fractional order controller design for a semi-active suspension system using nelder-mead optimization. In 2017 29th Chinese Control And Decision Conference (CCDC), pp 2808–2813. IEEE

    Google Scholar 

  19. Ewees AA, Elaziz MA, Houssein EH (2018) Improved grasshopper optimization algorithm using opposition-based learning. Expert Syst Appl 112:156–172

    Article  Google Scholar 

  20. Almugren N, Alshamlan H (2019) A survey on hybrid feature selection methods in microarray gene expression data for cancer classification. IEEE Access 7:78533–78548

    Article  Google Scholar 

  21. Basu A, Mohanty S, Sharma R (2017) Tuning of FOPID controller for meliorating the performance of the heating furnace using conventional tuning and optimization technique. Int J Electr Eng Res 9(1):69–85

    Google Scholar 

  22. Guha D, Roy PK, Banerjee S (2019) Grasshopper optimization algorithm-scaled fractional-order PI-D controller applied to reduced-order model of load frequency control system. Int J Model Simul 1–26

    Google Scholar 

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Correspondence to Sunita S. Biswal .

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Biswal, S.S., Swain, D.R., Rout, P.K. (2021). Improving the Performance of AVR System Using Grasshopper Evolutionary Technique. In: Sharma, R., Mishra, M., Nayak, J., Naik, B., Pelusi, D. (eds) Green Technology for Smart City and Society. Lecture Notes in Networks and Systems, vol 151. Springer, Singapore. https://doi.org/10.1007/978-981-15-8218-9_34

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