access icon free Microgrid frequency regulation involving low-wind-speed wind turbine generators based on deep belief network

With the development of low-wind-speed technology, it becomes a trend that low-wind-speed wind turbine generators (LWTGs) are integrated into a microgrid. However, the frequency stability of the microgrid has thereby been challenged since the increased penetration of wind power lowers the inertia of the microgrid. In order to investigate how LWTGs can effectively participate in suppressing the frequency fluctuation of the microgrid, virtual inertia control, over-speed control, as well as droop control, is applied to LWTG. Moreover, the de-loading ratio of over-speed control, along with the control parameters of virtual inertia control and droop control are all optimised under different wind speeds by virtue of the deep belief network, whereas the problem of over-speed control failure with the scheme of fixed de-loading ratio becomes more pronounced under low-wind speeds, which is defined as a blind area problem. To solve this problem, on the one hand, the strategy of the variable de-loading ratio is adopted under low-wind-speeds. On the other hand, the concepts of the minimum and maximum critical wind speed are deduced through theoretical analysis, which greatly restricts the number of feasible solutions of de-loading ratio under different wind speeds so as to improve the optimisation efficiency about 50%.

Inspec keywords: frequency control; angular velocity control; power generation control; wind turbines; distributed power generation; belief networks; turbogenerators

Other keywords: over-speed control failure; optimisation efficiency; microgrid frequency regulation; critical wind speed; deep belief network; droop control; wind power; low-wind-speed technology; virtual inertia control; low-wind-speed wind turbine generators; de-loading ratio

Subjects: Distributed power generation; Knowledge engineering techniques; Power engineering computing; Velocity, acceleration and rotation control; Frequency control; Wind power plants; Control of electric power systems

References

    1. 1)
      • 14. Morren, J., de Haan, S.W.H., Ferreira, J.A.: ‘Contribution of DG units to primary frequency control’. 2005 Int. Conf. on Future Power Systems, Amsterdam, the Netherlands, 2005, p. 6.
    2. 2)
      • 17. Stiebler, M.: ‘Wind energy systems for electric power generation’ (Springer, New York, NY, USA, 2008).
    3. 3)
      • 10. Ramtharan, G., Ekanayake, J.B., Jenkins, N.: ‘Frequency support from doubly fed induction generator wind turbines’, IET Renew. Power Gener., 2007, 1, (1), pp. 39.
    4. 4)
      • 9. Vidyanandan, K.V., Senroy, N.: ‘Primary frequency regulation by deloaded wind turbines using variable droop’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 837846.
    5. 5)
      • 3. Engleitner, R., Nied, A., Cavalca, M.S.M., et al: ‘Dynamic analysis of small wind turbines frequency support capability in a low-power wind-diesel microgrid’, IEEE Trans. Ind. Appl., 2018, 54, (1), pp. 102111.
    6. 6)
      • 6. Arani, M.F.M., El-Saadany, E.F.: ‘Implementing virtual inertia in DFIG-based wind power generation’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 13731384.
    7. 7)
      • 12. Wang, R., Xie, Y., Zhang, H., et al: ‘Dynamic power flow algorithm considering frequency regulation of wind power generators’, IET Renew. Power Gener., 2017, 11, (8), pp. 12181225.
    8. 8)
      • 22. Liu, W., Wang, Z., Liu, X., et al: ‘A survey of deep neural network architectures and their applications’, Neurocomputing, 2016, 234, pp. 1126.
    9. 9)
      • 8. Zhao, J., Lyu, X., Fu, Y., et al: ‘Coordinated microgrid frequency regulation based on DFIG Variable coefficient using virtual inertia and primary frequency control’, IEEE Trans. Energy Convers., 2016, 31, (3), pp. 833845.
    10. 10)
      • 21. Shim, H.-M., Lee, S.: ‘Multi-channel electromyography pattern classification using deep belief networks for enhanced user experience’, J. Central South Univ., 2015, 22, pp. 18011808.
    11. 11)
      • 30. Liu, J.S.: ‘Monte Carlo strategies in scientific computing’ (Springer, New York, NY, USA, 2001).
    12. 12)
      • 7. Kerdphol, T., Rahman, F.S., Watanabe, M., et al: ‘Robust virtual inertia control of a low inertia microgrid considering frequency measurement effects’, IEEE Access, 2019, 7, pp. 5755057560.
    13. 13)
      • 23. He, Y., Deng, J., Li, H.: ‘Short-term power load forecasting with deep belief network and copula models’. 2017 9th Int. Conf. on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, People's Republic of China, 2017, pp. 191194.
    14. 14)
      • 24. Neo, Y.Q., Teo, T.T., Woo, W.L., et al: ‘Forecasting of photovoltaic power using deep belief network’. TENCON 2017 – 2017 IEEE Region 10 Conf., Penang, Malaysia, 2017, pp. 11891194.
    15. 15)
      • 31. Hinton, G.E.: ‘Training products of experts by minimizing contrastive divergence’ (MIT Press, MA, USA, 2002).
    16. 16)
      • 27. Fischer, A., Igel, C.: ‘An introduction to restricted Boltzmann machines’, Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Buenos Aires, Argentina, 2012.
    17. 17)
      • 5. Keung, P., Li, P., Banakar, H., et al: ‘Kinetic energy of wind-turbine generators for system frequency support’, IEEE Trans. Power Syst., 2009, 24, (1), pp. 279287.
    18. 18)
      • 18. Chang, -C., Lin, W., Yin, Y.: ‘Enhancing frequency response control by DFIGs in the high wind penetrated power systems’, IEEE Trans. Power Syst., 2011, 26, (2), pp. 710718.
    19. 19)
      • 28. Fischer, A., Igel, C.: ‘Training restricted Boltzmann machines: an introduction’, Pattern Recognit., 2014, 47, (1), pp. 2539.
    20. 20)
      • 15. Ekanayake, J., Jenkins, N.: ‘Comparison of the response of doubly fed and fixed-speed induction generator wind turbines to changes in network frequency’, IEEE Trans. Energy Convers., 2004, 19, (4), pp. 800802.
    21. 21)
      • 26. Hinton, G.E., Salakhutdinov, R.R.: ‘Reducing the dimensionality of data with neural networks’, Science, 2006, 313, (5786), pp. 504507.
    22. 22)
      • 16. Morren, J., de Haan, S.W.H., Kling, W.L., et al: ‘Wind turbines emulating inertia and supporting primary frequency control’, IEEE Trans. Power Syst., 2006, 21, (1), pp. 433434.
    23. 23)
      • 4. Rana, R., Singh, M., Mishra, S.: ‘Design of modified DRCler for frequency support in microgrid using fleet of electric vehicles’, IEEE Trans. Power Syst., 2017, 32, (5), pp. 36273636.
    24. 24)
      • 29. Hua, Y.M., Guo, J.H., Zhao, H.: ‘Deep belief networks and deep learning’. Proc. 2015 Int. Conf. on Intelligent Computing and Internet of Things, Harbin, People's Republic of China, 2015, pp. 14.
    25. 25)
      • 13. Chen, X., Zhang, J., Zhang, G., et al: ‘Research on frequency control strategy of variable speed wind turbine’. 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conf. (ITOEC), Chongqing, People's Republic of China, 2017, pp. 267271.
    26. 26)
      • 1. Fakhari Moghaddam Arani, M., Mohamed, Y.A.I.: ‘Dynamic droop control for wind turbines participating in primary frequency regulation in microgrids’, IEEE Trans Smart Grid, 2018, 9, (6), pp. 57425751.
    27. 27)
      • 25. Zhang, B., Xu, X., Xing, H., et al: ‘A deep learning based framework for power demand forecasting with deep belief networks’. 2017 18th Int. Conf. on Parallel and Distributed Computing, Applications and Technologies (PDCAT), Taipei, Taiwan, 2017, pp. 191195.
    28. 28)
      • 19. Chang-Chien, L., Yin, Y.: ‘Strategies for operating wind power in a similar manner of conventional power plant’, IEEE Trans. Energy Convers., 2009, 24, (4), pp. 926934.
    29. 29)
      • 20. Chang-Chien, L., Hung, C., Yin, Y.: ‘Dynamic reserve allocation for system contingency by DFIG wind farms’, IEEE Trans. Power Syst., 2008, 23, (2), pp. 729736.
    30. 30)
      • 2. Ali, H., Magdy, G., Li, B., et al: ‘A new frequency control strategy in an islanded microgrid using virtual inertia control-based coefficient diagram method’, IEEE Access, 2019, 7, pp. 1697916990.
    31. 31)
      • 11. Zhang, X., Zha, X., Yue, S., et al: ‘A frequency regulation strategy for wind power based on limited over-speed de-loading curve partitioning’, IEEE Access, 2018, 6, pp. 2293822951.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2019.1161
Loading

Related content

content/journals/10.1049/iet-gtd.2019.1161
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading