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Load frequency control of a dynamic interconnected power system using generalised Hopfield neural network based self-adaptive PID controller

Load frequency control of a dynamic interconnected power system using generalised Hopfield neural network based self-adaptive PID controller

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A novel generalised Hopfield neural network (GHNN) based self-adaptive proportional–integral–derivative (PID) controller for load frequency control (LFC) is designed for a two-area interconnected power system with nonlinearities of generator rate constraint and governor dead band. The control problem is conceptualised as an optimisation problem with an objective function as an area control error in terms of the PID controller parameters. The differential equations governing the behaviour of the GHNN were solved to obtain the controller parameters K p, K i and K d. To test the feasibility and robustness of the proposed controller, the system is tested in the presence of randomness in load demands, imprecisely modelled system dynamics, nonlinearities in the system model and uncertainties in the system parameter variations. The proposed method is simulated using Matlab R2014b/Simulink and the results obtained have shown that the propounded controller performance is superior to the integral, PID and fuzzy-based proportional–integral controllers. In addition, the Lyapunov stability analysis of the overall closed-loop system was carried out and the controller is implemented in real-time digital simulator run in hardware-in-the-loop to validate the effectiveness of the proposed method. Furthermore, the proposed controller is applied to the three-area power system to test its adaptability.

References

    1. 1)
      • 1. Tripathy, S.C., Hope, G.S., Malik, O.P.: ‘Optimization of load-frequency control parameters for power systems with reheat steam turbines and governor dead band nonlinearity’, IEE Proc. Gener. Transm. Distrib., 1982, 129, (1), pp. 1016.
    2. 2)
      • 2. Lim, K.Y., Wang, Y., Zhou, R.: ‘Robust decentralised load-frequency control of multi-area power systems’, IEE Proc. Gener. Transm. Distrib., 1996, 143, (5), pp. 377386.
    3. 3)
      • 3. Bevrani, H.Y., Mitani, Y., Tsuji, K.: ‘Robust decentralised load-frequency control using an iterative linear matrix inequalities algorithm’, IEE Proc. Gener. Transm. Distrib., 2004, 151, (3), pp. 347354.
    4. 4)
      • 4. Sudha, K.R., Vijayasanthi, R.: ‘Robust decentralized load frequency control of interconnected power system with generation rate constraint using type-2 fuzzy approach’, Int. J. Electr. Power Energy Syst., 2011, 33, (3), pp. 699707.
    5. 5)
      • 5. Dong, L., Zhang, Y., Gao, Z.: ‘A robust decentralized load frequency controller for interconnected power systems’, ISA Trans.., 2012, 51, (3), pp. 410419.
    6. 6)
      • 6. Bevrani, H., Daneshmand, P.R.: ‘Fuzzy logic-based load-frequency control concerning high penetration of wind turbines’, IEEE Syst. J., 2012, 6, (1), pp. 173180.
    7. 7)
      • 7. Trinh, H., Fernando, T., Lu, H.H.C., et al: ‘Quasi-decentralized functional observers for the LFC of interconnected power systems’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 35133514.
    8. 8)
      • 8. Sivaramakrishnan, A.Y., Hariharan, M.V., Srisailam, M.C.: ‘Design of variable-structure load-frequency controller using pole assignment technique’, Int. J. Control, 1984, 40, (3), pp. 487498.
    9. 9)
      • 9. Chan, W.-C., Hsu, Y.-Y: ‘Automatic generation control of interconnected power systems using variable-structure controllers’, IEE Proc. Gener. Transm. Distrib., 1981, 128, (5), pp. 269279.
    10. 10)
      • 10. Tan, W.: ‘Unified tuning of PID load frequency controller for power systems via IMC’, IEEE Trans. Power Syst., 2010, 25, (1), pp. 341350.
    11. 11)
      • 11. Saxena, S., Hote, Y.V.: ‘Load frequency control in power systems via internal model control scheme and model-order reduction’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 27492757.
    12. 12)
      • 12. Bevrani, H., Hiyama, T.: ‘On load–frequency regulation with time delays: design and real-time implementation’, IEEE Trans. Energy Convers., 2009, 24, (1), pp. 292300.
    13. 13)
      • 13. Jiang, L., Yao, W., Wu, Q.H., et al: ‘Delay-dependent stability for load frequency control with constant and time-varying delays’, IEEE Trans. Power Syst., 2012, 27, (2), pp. 932941.
    14. 14)
      • 14. Zhang, C.-K., Jiang, L., Wu, Q.H., et al: ‘Delay-dependent robust load frequency control for time delay power systems’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 21922201.
    15. 15)
      • 15. Yousef, H.A., AL-Kharusi, K., Albadi, M.H., et al: ‘Load frequency control of a multi-area power system: an adaptive fuzzy logic approach’, IEEE Trans. Power Syst., 2014, 29, (4), pp. 18221830.
    16. 16)
      • 16. Chang, C.S., Fu, W.: ‘Area load frequency control using fuzzy gain scheduling of PI controllers’, Electr. Power Syst. Res., 1997, 42, (2), pp. 145152.
    17. 17)
      • 17. Abdennour, A.: ‘Adaptive optimal gain scheduling for the load frequency control problem’, Electr. Power Comput. Syst., 2002, 30, (1), pp. 4556.
    18. 18)
      • 18. Kocaarslan, I., Cam, E.: ‘Fuzzy logic controller in interconnected electrical power systems for load-frequency control’, Int. J. Electr. Power Energy Syst., 2005, 27, (8), pp. 542549.
    19. 19)
      • 19. Juang, C.F., Lu, C.F.: ‘Load-frequency control by hybrid evolutionary fuzzy PI controller’, IEE Proc. Gener. Transm. Distrib., 2006, 153, (2), pp. 196204.
    20. 20)
      • 20. Arivoli, R., Chidambaram, I.A.: ‘CPSO based LFC for a two-area power system with GDB and GRC nonlinearities interconnected through TCPS in series with the tie-line’, Int. J. Comput. Appl., 2012, 38, (7), pp. 110.
    21. 21)
      • 21. Talaq, J., Al-Basri, F.: ‘Adaptive fuzzy gain scheduling for load frequency control’, IEEE Trans. Power Syst., 1999, 14, (1), pp. 145150.
    22. 22)
      • 22. Xu, D., Liu, J., Yan, X.-G., et al: ‘A novel adaptive neural network constrained control for multi-area interconnected power system with hybrid energy storage’, IEEE Trans. Ind. Electron., 2017, 65, (8), pp. 66256634.
    23. 23)
      • 23. Rahman, M.M., Chowdhury, A.H., Hossain, M.A.: ‘Improved load frequency control using a fast acting active disturbance rejection controller’, Energies, 2017, 10, pp. 118.
    24. 24)
      • 24. Gupta, E., Saxena, A.: ‘Performance evaluation of antlion optimizer based regulator in automatic generation control of interconnected power system’, J. Eng., 2016, 2016, pp. 114, http://dx.doi.org/10.1155/2016/4570617.
    25. 25)
      • 25. Elgerd, O.I.: ‘Electric energy systems theory – an introduction’ (Tata McGraw-Hill, New York, 2007, 2nd edn.).
    26. 26)
      • 26. Vijaya Chandrakala, K.R.M., Balamurugan, S., Sankaranarayanan, K.: ‘Variable structure fuzzy gain scheduling based load frequency controller for multi area hydro thermal system’, Int. J. Electr. Power Energy Syst., 2013, 53, pp. 375381.
    27. 27)
      • 27. Veerapandiyan, V., Rajeswari, R., Mariammal, T., et al: ‘Load flow analysis using generalized Hopfield neural network’, IET Gener. Transm. Distrib., 2018, 12, (8), pp. 17651773.
    28. 28)
      • 28. Balasubramonian, M., Rajamani, V.: ‘Design and real-time implementation of SHEPWM in single-phase inverter using generalized Hopfield neural network’, IEEE Trans. Ind. Electron., 2014, 61, (11), pp. 111.
    29. 29)
      • 29. Wen, U.-P., Lan, K.-M., Shih, H.-S.: ‘A review of Hopfield neural networks for solving mathematical programming problems’, Eur. J. Oper. Res., 2009, 198, (3), pp. 675787.
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