access icon free Gravitational search algorithm-based optimal control of archimedes wave swing-based wave energy conversion system supplying a DC microgrid under uncertain dynamics

© The Institution of Engineering and Technology
Received 26/07/2016, Accepted 07/12/2016, Revised 04/11/2016, Published 09/12/2016

This study presents a novel application of the gravitational search algorithm (GSA) to optimally control a wave energy conversion (WEC) system under different operating conditions. In the WEC system, the generator side converter controls the d-axis and q-axis current of the generator for minimising the generator power losses and extracting the maximum real power from the WEC system, respectively. The DC–DC converter is implemented to maintain the terminal voltage of the DC microgrid. The control of both converters relies on the proportional–integral controllers, which are optimally designed by the GSA through a simulation-based optimisation approach. In that manner, the integral of squared error criteria is used as an objective function. The validity of the WEC system model is verified by comparing its simulation results with the experimental results that extracted from a field test. The effectiveness of the proposed controller is tested when the system is subjected to different operating conditions such as a DC microgrid load disturbance, a temporary DC fault condition, and an irregular wave condition. Moreover, the effectiveness of the proposed controller is compared with that by using the genetic algorithm. The simulation results of the system are extensively carried out using PSCAD/EMTDC program.

Inspec keywords: power convertors; search problems; PI control; genetic algorithms; distributed power generation; power generation control; optimal control

Other keywords: uncertain dynamics; DC fault condition; generator side converter; PSCAD/EMTDC program; archimedes wave swing-based wave energy conversion system; gravitational search algorithm; proportional–integral controllers; simulation-based optimisation approach; genetic algorithm; DC microgrid load disturbance; DC–DC converter; wave energy conversion system; DC microgrid; gravitational search algorithm-based optimal control; squared error criteria; WEC system

Subjects: Combinatorial mathematics; Combinatorial mathematics; Optimisation techniques; Optimisation techniques; Power convertors and power supplies to apparatus; Distributed power generation; Optimal control; Control of electric power systems

References

    1. 1)
      • 7. Ran, L., Mueller, M.A., Ng, C., et al: ‘Power conversion and control for a linear direct drive permanent magnet generator for wave energy’, IET Renew. Power Gener., 2011, 5, (1), pp. 1–9.
    2. 2)
      • 14. Hasanien, H.M.: ‘Design optimization of PID controller in automatic voltage regulator system using Taguchi combined genetic algorithm method’, IEEE Syst. J., 2013, 7, (4), pp. 825–831.
    3. 3)
      • 6. Wu, F., Ju, P., Zhang, X.-P., et al: ‘Modeling, control strategy, and power conditioning for direct-drive wave energy conversion to operate with power grid’, Proc. IEEE, 2013, 101, (4), pp. 925–941.
    4. 4)
      • 10. Forehand, D.I.M., Kiprakis, A.E., Nambiar, A.J., et al: ‘A fully coupled wave-to-wire model of an array of wave energy converters’, IEEE Trans. Sustain. Energy, 2016, 7, (1), pp. 118–128.
    5. 5)
      • 24. PSCAD/EMTDC Manual, V. 4.5.3, Manitoba HVDC Research Center, 2014, Canada.
    6. 6)
      • 15. Hasanien, H.M., Muyeen, S.M.: ‘Particle swarm optimization-based superconducting magnetic energy storage for low-voltage ride-through capability enhancement in wind energy conversion system’, Electr. Power Compon. Syst., 2015, 43, (11), pp. 1278–1288.
    7. 7)
      • 2. Wu, F., Zhang, X.-P., Ju, P., et al: ‘Modeling and control of AWS-based wave energy conversion system integrated into power grid’, IEEE Trans. Power Syst., 2008, 23, (3), pp. 1196–1204.
    8. 8)
      • 20. Niknam, T., Narimani, M.R., Azizipanah-Abarghooee, R., et al: ‘Multiobjective optimal reactive power dispatch and voltage control: a new opposition-based self-adaptive modified gravitational search algorithm’, IEEE Syst. J., 2013, 7, (4), pp. 742–753.
    9. 9)
      • 11. Tedeschi, E., Santos-Mugica, M.: ‘Modeling and control of a wave energy farm including energy storage for power quality enhancement: the Bimep case study’, IEEE Trans. Power Syst., 2014, 29, (3), pp. 1489–1497.
    10. 10)
      • 17. 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. 246–255.
    11. 11)
      • 19. Duman, S., Sonmez, Y., Guvenc, U., et al: ‘Optimal reactive power dispatch using a gravitational search algorithm’, IET Gener. Transm. Distrib., 2012, 6, (6), pp. 563–576.
    12. 12)
      • 22. Tan, W.S., Hassan, M.Y., Abdul Rahman, H., et al: ‘Multi-distributed generation planning using hybrid particle swarm optimization-gravitational search algorithm including voltage rise issue’, IET Gener. Transm. Distrib., 2013, 7, (9), pp. 929–942.
    13. 13)
      • 3. Ross, D.: ‘Power from the waves’ (Oxford University Press, Oxford, UK, 1995).
    14. 14)
      • 18. Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: ‘GSA: A gravitational search algorithm’, Inf. Sci., 2009, 179, pp. 2232–2248.
    15. 15)
      • 8. Ricci, P., Lopez, J., Santos, M., et al: ‘Control strategies for a wave energy converter connected to a hydraulic power take-off’, IET Renew. Power Gener., 2011, 5, (3), pp. 234–244.
    16. 16)
      • 25. de Sousa Prado, M.G., Gardner, F., Damen, M., et al: ‘Modelling and test results of the Archimedes wave swing’, Proc. Inst. Mech. Eng. A J. Power Energy, 2006, 220, (8), pp. 855–868.
    17. 17)
      • 16. Hasanien, H.M.: ‘Shuffled frog leaping algorithm-based static synchronous compensator for transient stability improvement of a grid-connected wind farm’, IET Renew. Power Gener., 2014, 8, (6), pp. 722–730.
    18. 18)
      • 12. Hasanien, H.M., Muyeen, S.M.: ‘Design optimization of controller parameters used in variable speed wind energy conversion system by genetic algorithms’, IEEE Trans. Sustain. Energy, 2012, 3, (2), pp. 200–208.
    19. 19)
      • 9. Alberdi, M., Amundarain, M., Garrido, A.J., et al: ‘Complementary control of oscillating water column-based wave energy conversion plants to improve the instantaneous power output’, IEEE Trans. Energy Convers., 2011, 26, (4), pp. 1021–1032.
    20. 20)
      • 4. Polinder, H., Damen, M.E.C., Gardner, F.: ‘Linear PM generator system for wave energy conversion in the AWS’, IEEE Trans. Energy Convers., 2004, 19, (3), pp. 583–589.
    21. 21)
      • 21. Bhattacharya, A., Roy, P.K.: ‘Solution of multi-objective optimal power flow using gravitational search algorithm’, IET Gener. Transm. Distrib., 2012, 6, (8), pp. 751–763.
    22. 22)
      • 13. 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. 1636–1644.
    23. 23)
      • 23. Ahmadi, R., Ferdowsi, M.: ‘Improving the performance of a line regulating converter in a converter-dominated DC microgrid system’, IEEE Trans. Smart Grid, 2014, 5, (5), pp. 2553–2563.
    24. 24)
      • 1. Global Wind Energy Council (GWEC): ‘Global wind report annual market update 2013’, available at: http://www.gwec.net.
    25. 25)
      • 5. Wu, F., Zhang, X.P., Ju, P., et al: ‘Optimal control for AWS-based wave energy conversion system’, IEEE Trans. Power Syst., 2009, 24, (4), pp. 1747–1755.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2016.0677
Loading

Related content

content/journals/10.1049/iet-rpg.2016.0677
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading