Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Parameters identification of reduced governor system model for diesel-engine generator by using hybrid particle swarm optimisation

This study presents an approach to build a reduced-order model (ROM) for the governor control systems of diesel-engine generators in an island power system. The hybrid particle swarm optimisation (PSO) is used in the parameter identification of the ROM. The reduced-order governor system model could be a useful and feasible model in the stability analysis of the island power system by using power system simulator for engineering. The results of the ROM and a sixth-order model have been compared. It is found that the ROM with the parameter values identified using the hybrid PSO is robust. Moreover, real-case validation of the ROM shows that it is usable to analyse stability and contingency in the power system.

References

    1. 1)
      • 11. Wang, H., Li, Y.: ‘Hybrid teaching-learning-based PSO for trajectory optimisation’, Electron. Lett., 2017, 53, (12), pp. 777779.
    2. 2)
      • 13. Stavrakakis, G.S., Kariniotakis, G.N.: ‘A general simulation algorithm for the accurate assessment of isolated diesel-wind turbines systems interaction. I. A general multimachine power system model’, IEEE Trans. Energy Convers., 1995, 10, (3), pp. 577583.
    3. 3)
      • 7. Goldberg, D.E.: ‘Genetic algorithms in search optimization and machine learning’ (Addison-Wesley, Reading, MA, 1989).
    4. 4)
      • 8. Valle, Y.D., Venayagamoorthy, G.K., Mohagheghi, S., et al: ‘Particle swarm optimization: basic concepts, variants and applications in power systems’, IEEE Trans. Evol. Comput., 2008, 12, pp. 171195.
    5. 5)
      • 6. Jain, A.K., Mao, J., Mohiuddin, K.M.: ‘Artificial neural networks: a tutorial’, Computer, 2002, 29, pp. 3144.
    6. 6)
      • 12. Cao, B., Zhao, J., Lv, Z., et al: ‘Distributed parallel particle swarm optimization for multi-objective and many-objective large-scale optimization’, IEEE Access, 2017, 5, pp. 82148221.
    7. 7)
      • 4. Tsai, C.C., Lee, W.J., Nashawati, E., et al: ‘PMU based generator parameter identification to improve the system planning and operation’. 2012 IEEE Power and Energy Society General Meeting, San Diego, CA, USA, 2012.
    8. 8)
      • 14. Kuang, B., Wang, Y., Tan, Y.L.: ‘An H controller design for diesel engine systems in power system technology’. Int. Conf. Proc. PowerCon 2000 Int. Conf. Power System Technology, Perth, WA, Australia, December 2000, pp. 6166.
    9. 9)
      • 10. Yang, T., Feng, Y., Yang, T., et al: ‘Parameter identification of steam turbine speed governor system’. Power and Energy Engineering Conf. (APPEEC), Shanghai, China, September 2012.
    10. 10)
      • 16. PSS/E documentation version 32.0.5.
    11. 11)
      • 3. Jalilvand, A.: ‘PSO algorithm-based optimal tuning of PSS for damping improvement of power systems’, Int. J. Eng. Technol., 2010, 2, (6), pp. 558562.
    12. 12)
      • 1. Kosterev, D., Taylor, C., Mittelstadt, W.: ‘Model validation for the August 10, 1996 WSCC system outage’, IEEE Trans. Power Syst., 1999, 14, (3), pp. 967979.
    13. 13)
      • 9. Lin, C.J.: ‘Study on generator-parameter identification technique through dynamic simulations’. MSc thesis, National Cheng Kung University, Taiwan, June 2014.
    14. 14)
      • 5. Hu, B., Sun, J., Ding, L., et al: ‘Dynamic equivalent modeling for small and medium hydropower generator group based on measurements’, Energies, 2016, 9, (5), p. 362.
    15. 15)
      • 17. Su, W.: ‘Microgrid modeling, planning and operation’. MSc thesis, Virginia Polytechnic Institute and State University, America, November 2009.
    16. 16)
      • 2. 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.
    17. 17)
      • 18. da Graca Boa Esperanca, H., Chen, C.S.: ‘Stability and economic impact of interconnecting a utility-scale PV generation system to the power system of Sao Tome Island’. MSc Thesis, National Sun Yat-sen University, Taiwan, June 2015.
    18. 18)
      • 15. Roy, S., Malik, O. P., Hope, G. S.: ‘A least-squares based model-fitting identification technique for diesel prime-movers with unknown dead-time’, IEEE Trans. Energy Convers., 1991, 6, (2), pp. 251256.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-epa.2017.0851
Loading

Related content

content/journals/10.1049/iet-epa.2017.0851
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address