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Stability Analysis in RECS-Integrated Multi-area AGC System with SOS Algorithm Based Fuzzy Controller

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 711))

Abstract

This paper aims toward coordination between generation and demand of electric power, which is termed as automatic generation control (AGC). A wind energy conversion system (WECS)-based doubly fed induction generator (DFIG) integrated with two equal areas conventional thermal generation was proposed. A fuzzy-Proportional Integral Derivative (fuzzy-PID) controller was used for stabilizing deviation in frequency (∆f) and tie-line power (∆Ptie). The gains of fuzzy-PID and DFIG controller are tuned optimally using a multi-objective optimization technique called symbiotic organism search (SOS) algorithm. In addition, the dynamic response and accuracy of system under study was investigated using integral of time multiplied absolute error (ITAE). The performance of fuzzy-PID controller was compared with conventional PID, PI, and fuzzy-PI controller in terms of settling time and peak overshoot. Finally, it was observed experimentally that the proposed SOS optimized fuzzy-PID controller gives superior dynamic and robust performance as compared to other controllers under various operating conditions.

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References

  1. Eltra, E. “Wind turbines connected to grids with voltages above 100 kV.” Regulation document TF 3.5 (2004).

    Google Scholar 

  2. Van Hulle, F. J. L. “Large Scale Integration of Wind Energy in the European Power Supply: Analysis, Issues and Recommendations”: Executive Summary. European Wind Energy Association, 2005.

    Google Scholar 

  3. Mullane, Alan, and Mark O’Malley. “The inertial response of induction-machine-based wind turbines.” IEEE Transactions on power systems 20.3 (2005): 1496–1503.

    Article  Google Scholar 

  4. Lalor, Gillian, Alan Mullane, and Mark O’Malley. “Frequency control and wind turbine technologies.” IEEE Transactions on Power Systems 20.4 (2005): 1905–1913.

    Article  Google Scholar 

  5. Anaya-Lara, O., et al. “Contribution of DFIG-based wind farms to power system short-term frequency regulation.” IEE Proceedings-Generation, Transmission and Distribution 153.2 (2006): 164–170.

    Article  Google Scholar 

  6. Mauricio, Juan Manuel, et al. “Frequency regulation contribution through variable-speed wind energy conversion systems.” IEEE Transactions on Power Systems 24.1 (2009): 173–180.

    Article  Google Scholar 

  7. Bhatt, Praghnesh, Ranjit Roy, and S. P. Ghoshal. “Dynamic participation of doubly fed induction generator in automatic generation control.” Renewable Energy 36.4 (2011): 1203–1213.

    Article  Google Scholar 

  8. Elgerd, Olle I., and Charles E. Fosha. “Optimum megawatt-frequency control of multiarea electric energy systems.” IEEE Transactions on Power Apparatus and Systems 4 (1970): 556–563.

    Article  Google Scholar 

  9. Ghoshal, Sakti Prasad. “Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control.” Electric Power Systems Research 72.3 (2004): 203–212.

    Article  Google Scholar 

  10. Ghoshal, S. P., and S. K. Goswami. “Application of GA based optimal integral gains in fuzzy based active power-frequency control of non-reheat and reheat thermal generating systems.” Electric Power Systems Research 67.2 (2003): 79–88.

    Article  Google Scholar 

  11. R. K. Sahu, S. Panda and G.T. Chandra Sekhar. “A novel hybrid PSO-PS optimized fuzzy PI controller for AGC in multi area interconnected power systems”, Int J Electr Power Energy Syst, Vol. 64, pp. 880–93, 2015.

    Article  Google Scholar 

  12. P. Dash, L. C. Saikia and N. Sinha, “Comparison of performances of several Cuckoo search algorithm based 2DOF controllers in AGC of multi-area thermal system” Int. J Electric Power Energy Syst, Vol. 55, pp. 429–36, 2014.

    Article  Google Scholar 

  13. M.Y. Cheng, D. Prayogo, Symbiotic organisms search: a new metaheuristic optimization algorithm, Comput. Struct”. 139 (2014) 98–112.

    Article  Google Scholar 

  14. P. Dash, L. C. Saikia and N. Sinha, “Automatic generation control of multi area thermal system using Bat algorithm optimized PD–PID cascade controller” Int J Electr Power Energy Syst, 68, pp. 364–78, 2015.

    Article  Google Scholar 

  15. Nanda, A. Mangla and S. Suri, “Some new findings on automatic generation control of an interconnected hydrothermal system with conventional controllers,” IEEE Trans Energy Convers, Vol. 21 (1), pp. 187–94, 2006.

    Article  Google Scholar 

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Correspondence to Prakash Chandra Sahu .

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Appendix

Appendix

  1. A.

    Nominal parameter of system

    • i = subscript referred to area (1, 2)

    • Hi = Inertia constant = H1 = H2 = 5

    • ∆fi = Incremental change in frequency (Hz)

    • ∆Pdi = Incremental load change

    • Di = 8.33 × 10−3 p.u MW/Hz

    • R1 = R2 = 2.4 Hz/p.u MW

    • Tg1 = Tg2 = 0.08 s (steam governor time constant)

    • Kr1 = Kr2 = 0.5 (steam turbine reheat coefficient)

    • Tr1 = Tr2 = 10 s (steam turbine reheat time constant)

    • Tt1 = Tt2 = 0.3 s (steam turbine time constant)

    • \( \upbeta1 =\upbeta1 = 0.425 \) (frequency bias)

    • f1 = f2 = 60 Hz

    • Tp1 = Tp2 = 20 s (power system time constant)

    • Kp1 = Kp2 = 120 Hz/p.u MW (power system gain constant)

    • T12 = synchronizing coefficient

    • Pr1 = Pr2 = 2000 MW (area capacity)

  2. B.

    Nominal parameter of system with DFIG

    • \( \upbeta1 =\upbeta2 = 0.314 \) (frequency bias)

    • He = 3.5 s (equivalent WECS time constant)

    • Tw = 6 s (washout filter time constant)

    • TR = frequency transducer time constant

    • TA = controlled WECS time constant

    • Kpw = speed regulator proportional time constant

    • Kiw = speed regulator integral constant

    • \( P_{NC}^{\hbox{min} } /P_{nc}^{\hbox{max} } = \) WECS output power limit = 0/1.2 p.u.

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Sahu, P.C., Prusty, R.C., Panda, S. (2019). Stability Analysis in RECS-Integrated Multi-area AGC System with SOS Algorithm Based Fuzzy Controller. In: Behera, H., Nayak, J., Naik, B., Abraham, A. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_21

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