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Optimisation of controller parameters for grid-tied photovoltaic system at faulty network using artificial neural network-based cuckoo search algorithm

Optimisation of controller parameters for grid-tied photovoltaic system at faulty network using artificial neural network-based cuckoo search algorithm

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This study exhibits the optimum design procedure to tune controller parameters for grid-connected distributed generation system based on cuckoo search algorithm (CSA). To investigate the effectiveness of proposed algorithm, a grid-tied photovoltaic (PV) system consisting of two power electronic converters controlled by five proportional integral (PI) controllers is chosen. Setting proper values for all the PI controllers is a complicated task, notably when the system is non-linear. In this study, response surface methodology (RSM) is used to develop the mathematical design of the PV system which is required to apply the optimisation algorithm. To minimise the design efforts of RSM, an alternate approach based on artificial neural network is introduced to develop the mathematical model of the PV system which is another salient feature of this research. Moreover, two modifications in the CSA are proposed to extract optimum parameters for the controllers which are found suitable in power system applications. Both the transient and dynamic performances of the system with the optimum values obtained through CSA are studied for different types of grid fault conditions using PSCAD/EMTDC. The design values are compared with values obtained through genetic algorithm and bacterial foraging optimisation. Experimental validation is also given for the proposed method.

References

    1. 1)
      • S.S. Rao . (2009)
        1. Rao, S.S.: ‘Engineering optimization: theory and practice’ (John Wiley & Sons, 2009, 4th edn.).
        .
    2. 2)
      • S. Kannan , S.M.R. Slochanal , N.P. Padhy .
        2. Kannan, S., Slochanal, S.M.R., Padhy, N.P.: ‘Application and comparison of metaheuristic techniques to generation expansion planning problem’, IEEE Trans. Power Syst., 2005, 20, pp. 466475.
        . IEEE Trans. Power Syst. , 466 - 475
    3. 3)
      • S. Milner , C. Davis , H. Zhang .
        3. Milner, S., Davis, C., Zhang, H., et al: ‘Nature-inspired self-organization, control, and optimization in heterogeneous wireless networks’, IEEE Trans. Mob. Comput., 2012, 11, pp. 12071222.
        . IEEE Trans. Mob. Comput. , 1207 - 1222
    4. 4)
      • T. Niknam , F. Golestaneh .
        4. Niknam, T., Golestaneh, F.: ‘Enhanced bee swarm optimization algorithm for dynamic economic dispatch’, IEEE Syst. J., 2013, 7, pp. 754762.
        . IEEE Syst. J. , 754 - 762
    5. 5)
      • Y.H. Wang , Y.Y. Wang , K.W. Chan .
        5. Wang, Y.H., Wang, Y.Y., Chan, K.W., et al: ‘Dynamic voltage security constrained optimal coordinated voltage control using enhanced particle swarm optimisation’, IET. Gener. Transm. Distrib., 2011, 5, pp. 239248.
        . IET. Gener. Transm. Distrib. , 239 - 248
    6. 6)
      • X.S. Yang . (2010)
        6. Yang, X.S.: ‘Engineering optimization: an introduction with metaheuristic applications’ (John Wiley & Sons, 2010, 2nd edn.).
        .
    7. 7)
      • X.S. Yang , A.H. Gandomi , A.H. Alavi .
        7. Yang, X.S., Gandomi, A.H., Alavi, A.H.: ‘Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems’, Eng. Comput., 2011.
        . Eng. Comput.
    8. 8)
      • X.S. Yang , S. Deb .
        8. Yang, X.S., Deb, S.: ‘Cuckoo search via Levy flights’. Proc. of World Congress on Nature and Biologically Inspired Computing (NaBIC 2009), December 2009, India. (IEEE Publications, USA, 2009), pp. 210214.
        . Proc. of World Congress on Nature and Biologically Inspired Computing (NaBIC 2009), December 2009, India. (IEEE Publications , 210 - 214
    9. 9)
      • A.H. Gandomi , X.S. Yang , A.H. Alavi .
        9. Gandomi, A.H., Yang, X.S., Alavi, A.H.: ‘Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems’, Eng. Comput., 2013, 29, (1), pp. 1735.
        . Eng. Comput. , 1 , 17 - 35
    10. 10)
      • M.K. Maricela , T. Prabaharan , X.S. Yang .
        10. Maricela, M.K., Prabaharan, T., Yang, X.S.: ‘Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan’, Appl. Soft Comput., 2014, 19, pp. 93101.
        . Appl. Soft Comput. , 93 - 101
    11. 11)
      • T.T. Nguyen , A.V. Truong .
        11. Nguyen, T.T., Truong, A.V.: ‘Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm’, Int. J. Electr. Power Energy Syst., 2015, 68, pp. 233242.
        . Int. J. Electr. Power Energy Syst. , 233 - 242
    12. 12)
      • T.T. Nguyen , A.V. Truong , T.A. Phung .
        12. Nguyen, T.T., Truong, A.V., Phung, T.A.: ‘A novel method based on adaptive cuckoo search for optimal network reconfiguration and distributed generation allocation in distribution network’, Int. J. Electr. Power Energy Syst., 2016, 78, pp. 801815.
        . Int. J. Electr. Power Energy Syst. , 801 - 815
    13. 13)
      • 13. ‘Renewable power generation costs in 2014’. Available at http://www.irena.org.
        .
    14. 14)
      • M.B. Delghavi , S. Shoja-Majidabad , A. Yazdani .
        14. Delghavi, M.B., Shoja-Majidabad, S., Yazdani, A.: ‘Fractional-order sliding-mode control of islanded distributed energy resource systems’, IEEE Trans. Sustain. Energy, 2016, 7, (4), pp. 14821491.
        . IEEE Trans. Sustain. Energy , 4 , 1482 - 1491
    15. 15)
      • A. Rubaai , M.J. Castro-Sitiriche , A.R. Ofoli .
        15. Rubaai, A., Castro-Sitiriche, M.J., Ofoli, A.R.: ‘Design and implementation of parallel fuzzy PID controller for high-performance brushless motor drives: an integrated environment for rapid control prototyping’, IEEE Trans. Ind. Appl., 2008, 44, (4), pp. 10901098.
        . IEEE Trans. Ind. Appl. , 4 , 1090 - 1098
    16. 16)
      • C. Guo , Q. Song , W. Cai .
        16. Guo, C., Song, Q., Cai, W.: ‘A neural network assisted cascade control system for air handling unit’, IEEE Trans. Ind. Electron., 2007, 54, (1), pp. 620628.
        . IEEE Trans. Ind. Electron. , 1 , 620 - 628
    17. 17)
      • A. Rubaai , P. Young .
        17. Rubaai, A., Young, P.: ‘EKF-based PI-/PD-like fuzzy-neural-network controller for brushless drives’, IEEE Trans. Ind. Electron., 2011, 47, (6), pp. 23912401.
        . IEEE Trans. Ind. Electron. , 6 , 2391 - 2401
    18. 18)
      • C.C. Hang , K.J. Astrom , W.K. Ho .
        18. Hang, C.C., Astrom, K.J., Ho, W.K.: ‘Refinements of the Ziegler-Nichols tuning formula’, IEE Proc. D – Control Theory Appl., 1991, 138, (2), pp. 111118.
        . IEE Proc. D – Control Theory Appl. , 2 , 111 - 118
    19. 19)
      • S. Deo , C. Jain , B. Singh .
        19. Deo, S., Jain, C., Singh, B.: ‘A PLL-Less scheme for single-phase grid interfaced load compensating solar PV generation system’, IEEE Trans. Ind. Inf., 2015, 11, (3), pp. 692699.
        . IEEE Trans. Ind. Inf. , 3 , 692 - 699
    20. 20)
      • K.F. Krommydas , A.T. Alexandridis .
        20. Krommydas, K.F., Alexandridis, A.T.: ‘Modular control design and stability analysis of isolated PV-source/battery-storage distributed generation systems’, IEEE J. Emerging Sel. Top. Circuits Syst., 2015, 5, (3), pp. 372382.
        . IEEE J. Emerging Sel. Top. Circuits Syst. , 3 , 372 - 382
    21. 21)
      • Z.L. Gaing .
        21. Gaing, Z.L.: ‘A particle swarm optimization approach for optimum design of PID controller in AVR system’, IEEE Trans. Energy Convers., 2004, 19, (2), pp. 384391.
        . IEEE Trans. Energy Convers. , 2 , 384 - 391
    22. 22)
      • V. Mukherjee , S.P Ghoshal .
        22. Mukherjee, V., Ghoshal, S.P: ‘Intelligent particle swarm optimized fuzzy PID controller for AVR system’, Electr. Power Syst. Res., 2007, 77, (12), pp. 16891698.
        . Electr. Power Syst. Res. , 12 , 1689 - 1698
    23. 23)
      • R.N. Kalaam , H.M. Hasanien , A. Al-Durra .
        23. Kalaam, R.N., Hasanien, H.M., Al-Durra, A., et al: ‘Optimal design of cascaded control scheme for PV system using BFO algorithm’. Int. Conf. on Renewable Energy Research and Applications (ICRERA – 2015), pp. 907912.
        . Int. Conf. on Renewable Energy Research and Applications (ICRERA – 2015) , 907 - 912
    24. 24)
      • H.M. Hasanien , A.S. Abd-Rabou , S.M. Sakr .
        24. Hasanien, H.M., Abd-Rabou, A.S., Sakr, S.M.: ‘Design optimization of transverse flux linear motor for weight reduction and performance improvement using response surface methodology and genetic algorithms’, IEEE Trans. Energy Convers., 2010, 25, (3), pp. 598605.
        . IEEE Trans. Energy Convers. , 3 , 598 - 605
    25. 25)
      • H.O. Bansal , R. Sharma , P.R Shreeraman .
        25. Bansal, H.O., Sharma, R., Shreeraman, P.R: ‘PID controller tuning techniques: a review’, J. Control Eng. Technol., 2012, 2, (4), pp. 168176.
        . J. Control Eng. Technol. , 4 , 168 - 176
    26. 26)
      • A. Witek-Krowiak , K. Chojnacka , D. Podstawczyk .
        26. Witek-Krowiak, A., Chojnacka, K., Podstawczyk, D., et al: ‘Application of response surface methodology and artificial neural network methods in modelling and optimization of biosorption process’, Bioresour. Technol., 2014, 160, pp. 150160.
        . Bioresour. Technol. , 150 - 160
    27. 27)
      • J.P. Maran , V. Sivakumar , K. Thirugnanasambandham .
        27. Maran, J.P., Sivakumar, V., Thirugnanasambandham, K., et al: ‘Artificial neural network and response surface methodology modeling in mass transfer parameters predictions during osmotic dehydration of Carica papaya L. Alexandria’, Eng. J., 2013, 52, (3), pp. 507516.
        . Eng. J. , 3 , 507 - 516
    28. 28)
      • M.S. El Moursi , W. Xiao , J.L. Kirtley .
        28. El Moursi, M.S., Xiao, W., Kirtley, J.L.: ‘Fault ride through capability for grid interfacing large scale PV power plants’, IET. Gener. Transm. Distrib., 2013, 7, (9), pp. 10271036.
        . IET. Gener. Transm. Distrib. , 9 , 1027 - 1036
    29. 29)
      • D. Zeng , G. Wang , G. Pan .
        29. Zeng, D., Wang, G., Pan, G., et al: ‘Fault ride-through capability enhancement of PV system with voltage support control strategy’, Open J. Appl. Sci., 2013, 3, (02), p. 3034.
        . Open J. Appl. Sci. , 2 , 30 - 34
    30. 30)
      • X.S. Yang . (2014)
        30. Yang, X.S.: ‘Nature-inspired optimization algorithms’ (Elsevier, 2014).
        .
    31. 31)
      • M.G. Villalva , J.R. Gazoli , E Ruppert Filho .
        31. Villalva, M.G., Gazoli, J.R., Ruppert Filho, E: ‘Comprehensive approach to modeling and simulation of photovoltaic arrays’, IEEE Trans. Power Electron., 2009, 24, (5), pp. 11981208.
        . IEEE Trans. Power Electron. , 5 , 1198 - 1208
    32. 32)
      • M. Starke .
        32. Starke, M.: ‘DC Distribution with Fuel Cells as Distributed Energy Resources’. PhD thesis, Harvard University, 1993.
        .
    33. 33)
      • 33. ‘Cables for photovoltaic solar installations’. Available at www.elesis.gr/php/download.php?file=solar_cables_presentation.pdf.
        .
    34. 34)
      • B.I. Crăciun , T. Kerekes , D. Séra .
        34. Crăciun, B.I., Kerekes, T., Séra, D., et al: ‘Overview of recent grid codes for PV power integration’. 2012 13th Int. Conf. on Optimization of Electrical and Electronic Equipment (OPTIM), Brasov, 2012, pp. 959965.
        . 2012 13th Int. Conf. on Optimization of Electrical and Electronic Equipment (OPTIM) , 959 - 965
    35. 35)
      • H.M. Hasanien .
        35. Hasanien, H.M.: ‘Particle swarm design optimization of transverse flux linear motor for weight reduction and improvement of thrust force’, IEEE Trans. Ind. Electron., 2011, 58, (9), pp. 40484056.
        . IEEE Trans. Ind. Electron. , 9 , 4048 - 4056
    36. 36)
      • M.N. Ambia , H.M. Hasanien , A. Al-Durra .
        36. Ambia, M.N., Hasanien, H.M., Al-Durra, A., et al: ‘Harmony search algorithm-based controller parameters optimization for a distributed-generation system’, IEEE Trans. Power Deliv., 2015, 30, (1), pp. 246255.
        . IEEE Trans. Power Deliv. , 1 , 246 - 255
    37. 37)
      • P.G. Mathews . (2005)
        37. Mathews, P.G.: ‘Design of experiments with MINITAB’ (ASQ Quality Press, 2005).
        .
    38. 38)
      • R.H. Myers , D.C. Montgomery , C.M. Anderson-Cook . (2016)
        38. Myers, R.H., Montgomery, D.C., Anderson-Cook, C.M.: ‘Response surface methodology: process and product optimization using designed experiments’ (John Wiley & Sons, 2016, 4th edn.).
        .
    39. 39)
      • H.M. Hasanien , S.M Muyeen .
        39. Hasanien, H.M., Muyeen, S.M: ‘A Taguchi approach for optimum design of proportional-integral controllers in cascaded control scheme’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 16361644.
        . IEEE Trans. Power Syst. , 2 , 1636 - 1644
    40. 40)
      • S.M. Muyeen , H.M. Hasanien , A. Al-Durra .
        40. Muyeen, S.M., Hasanien, H.M., Al-Durra, A.: ‘Transient stability enhancement of wind farms connected to a multi-machine power system by using an adaptive ANN-controlled SMES’, Energy Convers. Manage., 2014, 78, pp. 412420.
        . Energy Convers. Manage. , 412 - 420
    41. 41)
      • S.M. Muyeen , H.M. Hasanien , J. Tamura .
        41. Muyeen, S.M., Hasanien, H.M., Tamura, J.: ‘Reduction of frequency fluctuation for wind farm connected power systems by an adaptive artificial neural network controlled energy capacitor system’, IET Renew. Power Gener., 2012, 6, (4), pp. 226235.
        . IET Renew. Power Gener. , 4 , 226 - 235
    42. 42)
      • H.M. Hasanien , S.M Muyeen .
        42. Hasanien, H.M., Muyeen, S.M: ‘Speed control of grid-connected switched reluctance generator driven by variable speed wind turbine using adaptive neural network controller’, Electr. Power Syst. Res., 2012, 84, (1), pp. 206213.
        . Electr. Power Syst. Res. , 1 , 206 - 213
    43. 43)
      • H.M. Hasanien .
        43. Hasanien, H.M.: ‘FPGA implementation of adaptive ANN controller for speed regulation of permanent magnet stepper motor drives’, Energy Convers. Manag., 2011, 52, (2), pp. 12521257.
        . Energy Convers. Manag. , 2 , 1252 - 1257
    44. 44)
      • X.S. Yang , S. Deb , M. Karamanoglu .
        44. Yang, X.S., Deb, S., Karamanoglu, M., et al: ‘Cuckoo search for business optimization applications’. National Conf. On Computing And Communication Systems, Durgapur, 2012, pp. 15.
        . National Conf. On Computing And Communication Systems , 1 - 5
    45. 45)
      • R.N. Mantegna .
        45. Mantegna, R.N.: ‘Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes’, Phys. Rev. E, 1994, 49, (5), p. 4677.
        . Phys. Rev. E , 5 , 4677
    46. 46)
      • Y. Xin-She .
        46. Xin-She, Y.: ‘Cuckoo search and firefly algorithm theory and applications’, Stud. Comput Intell., 2013, 516.
        . Stud. Comput Intell.
    47. 47)
      • X.S. Yang . (2010)
        47. Yang, X.S.: ‘Nature-inspired metaheuristic algorithms’ (Springer, UK,, 2010, 1st edn.).
        .
    48. 48)
      • H. Garg .
        48. Garg, H.: ‘An approach for solving constrained reliability-redundancy allocation problems using cuckoo search algorithm’, Beni-Suef Univer. J. Basic Appl. Sci., 2015, 4, pp. 1425.
        . Beni-Suef Univer. J. Basic Appl. Sci. , 14 - 25
    49. 49)
      • X.S. Yang , S. Deb .
        49. Yang, X.S., Deb, S.: ‘Multiobjective cuckoo search for design optimization’, Comput. Oper. Res., 2013, 40, pp. 16161624.
        . Comput. Oper. Res. , 1616 - 1624
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