Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 11–16 (2017)
Abstract. This paper deals with the process of identifying the parameters of the dynamic equivalent (DE) load model of an active distribution system (ADN) simulated in RTDS using mean-variance mapping optimization (MVMO) algorithm. MVMO is an emerging variant of population-based, evolutionary optimization algorithm whose features include evolution of its solutions through a unique search mechanism within a normalized range of the sample space. Due to the prominent large-scale integration of DG in low and medium voltage networks, it is important to develop equivalent models that are suitable for representing the resulting active distribution network in dynamic studies of large power systems. This would significantly reduce the computational demands and simulation time. Moreover, only a defined portion of a system is usually studied, which means that the external system can be substituted with DE thereby allowing the detailed modelling of the focus area. The IEEE 34-Bus distribution system was modified and used as the reference network where measurement data were gathered for identification of the parameters of its developed DE. An optimization-enabled simulation involving MATLAB, which host the MVMO algorithm and RTDS, which simulates the models was established. The reactions of the detailed network and the DE were compared upon subjecting them to different disturbances in the retained system. The effectiveness of the MVMO algorithm in identifying DE parameters based on its unique mapping function is reflected through the results of the response comparison.
- IEEE Transactions on Power Systems, “Load representation for dynamic performance analysis of power systems,” IEEE Journal 0885-8950, vol. 8, pp. 472-482, May 1993
- IEEE Transactions on Power Systems, “Standard load models for power flow and dynamic performance simulation,” IEEE Trans 0885-8950, vol. 10, pp. 1302-1313, August 1995
- L. Wang, M. Klein, S. Yirga and P. Kundur, “Dynamic reduction of large power systems for stability studies,” IEEE Trans 0885-8950,vol. 12, pp. 889-895, May 1997 [IEEE Transactions on Power Systems]
- K. Yamashita and S. Djokic and J. Matevosyan and Resende, F. O. and Korunovic, L. M. and Dong, Z. Y. and Milanovic, J. V., “Modelling and aggregation of loads in flexible power networks - Scope and status of the work of CIGRE WG C4.605,” IFAC Proceedings Volumes (IFAC-PapersOnline)|IFAC Proc. Vol. 8, pp. 405–410, 2012.
- K. Yamashita, S. M. Villanueva, and J. V. Milanovic, “Initial results of international survey on industrial practice on power system load modelling conducted by CIGRE WG C4.605,” in Proc. CIGRE Symp., Bologna, Italy, 2011, vol. C4-333.
- J. C. Cepeda, J. L. Rueda and I. Erlich, “Identification of dynamic equivalents based on heuristic optimization for smart grid applications,” 2012 IEEE Congress on Evolutionary Computation, Brisbane, QLD, 2012, pp. 1-8. http://dx.doi.org/10.1109/CEC.2012.6256493
- Matevosyan J. et al., “Aggregated models of wind-based generation and active distribution network cells for power system studies - literature overview,” PowerTech, 2011 IEEE Trondheim, Trondheim, 2011, pp. 1-8.
- Jin Ma, Renmu He and D. J. Hill, “Composite load modeling via measurement approach,” IEEE Power Engineering Society General Meeting, Montreal, Que., 2006, pp. 1, http://dx.doi.org/10.1109/PES.2006.1708962
- A. M. Azmy and I. Erlich, “Identification of dynamic equivalents for distribution power networks using recurrent ANNs,” IEEE PES Power Systems Conference and Exposition, 2004., pp. 348-353 vol.1. doi: 10.1109/PSCE.2004.1397544
- C. Kwon and S. D. Sudhoff, “Genetic algorithm-based induction machine characterization procedure with application to maximum torque per amp control,” IEEE PES Power Systems Conference and Exposition, pp. 405-415 vol.21., June 2006. http://dx.doi.org/10.1109/TEC.2006.874224
- A. Karimi and M. A. Choudhry and A. Feliachi, “PSO-based Evolutionary Optimization for Parameter Identification of an Induction Motor,” 39th North American Power Symposium, pp. 659-664, Sept 2007. http://dx.doi.org/10.1109/NAPS.2007.4402380
- E. Polykarpou and E. Kyriakides, “Parameter estimation for measurement-based load modeling using the Levenberg-Marquardt algorithm,” 18th Mediterranean Electrotechnical Conference (MELECON), pp. 1-6, April 2016. http://dx.doi.org/10.1109/MELCON.2016.7495363
- J. L. Rueda, I. Erlich, “MVMO for bound constrained single-objective computationally expensive numerical optimization,” IEEE Congress 1089–778X, pp. 1011–1017, May 2015 [IEEE Congress on Evolutionary Computation (CEC)]
- IEEE 34 Node Test Feeder, “Distribution System Analysis Subcommittee of the IEEE Power Engineering Society,” http://www.ewh.ieee.org/soc/pes/dsacom/testfeeders/, Accessed: 2016-12-30
- O. Nzimako and A. Rajapakse, “Real time simulation of a microgrid with multiple distributed energy resources,” International Conference on Cogeneration, Small Power Plants and District Energy (ICUE), Bangkok, 2016, pp. 1-6. http://dx.doi.org/10.1109/COGEN.2016.7728945