Application of DG-RBFNN in Power Flow Calculation of Distribution Network with DG

Abstract:

With the injection of distributed generation (DG) into distribution network, complexity and uncertainty of power flow in distribution network follow. In order to calculate power flow more simply and accurately, considering the characteristics of distribution network with DG and the flexible applicability of RBF neural network, a special adaptive dynamic clustering RBFNN (DG-RBFNN) method, which clusters the input samples only according to the parameters associated with DG, has been used in this paper. Therefore, the results are more approximate to the real condition and the calculation process is simpler compared with conventional back/forward (B/F) method meanwhile the calculation scale is also relatively smaller compared with ordinary adaptive dynamic clustering RBFNN. Finally, according to a 21-bus 66 kV distribution network of Shenyang, Liaoning province simulation experiment, the availability of DG-RBFNN method is proved.

• Periodical: Advanced Materials Research (Volumes 614-615)
• Pages: 862-865
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.614-615.862
• Online since: December 2012
• Authors: Qing Qi Zhao, Xue Chen, Yi Yang, Ji Guang Xue
• Keywords: DG, Power Flow Calculation, RBF Neural Network

References

[1] M.-C. Alvarez-Herault, D. Picault, R. Caire, B. Raison, N. HadjSaid and W. Bienia: A Novel Hybrid Network Architecture to Increase DG Insertion in Electrical Distribution Systems. IEEE Transactions on Power Systems. Vol. 26 (May 2011), pp.905-914.

DOI: 10.1109/tpwrs.2010.2071887

[2] X.Y. Yang, J.D. Duan, X.Z. Wan and Y. Fan: Power Flow Calculation in Distribution Networks Ccontaining Distributed Ggeneration. Electricity Distribution (2008), pp.1-5

[3] H. Victor, M. Quezada, J.R. Abbad and S.R. Tomas Gomez: Assessment of Energy Distribution Losses for Increasing Penetration of Distributed Generation. IEEE Transactions on Power Systems. Vol. 21 (2006), pp.533-540.

DOI: 10.1109/tpwrs.2006.873115

[4] H.Y. Chen, J.F. Chen and X.Z. Duan: Study on Power Flow of Distribution System with DGs. Automation of Electric Power Systems (2006), pp.35-40.

[5] S. Naka, T. Genji, Y. Fukuyama: Practical Equipment Models for Fast Distribution Power Flow Considering Interconnection of Distributed Generators. Power Engineering Society Summer Meeting, IEEE. Vol. 2 (July 2001), pp.1007-1012.

DOI: 10.1109/pess.2001.970196

[6] Y. Zhu and K. Tomsovic: Adaptive Power Flow Method for Distribution Systems With Dispersed Generation. IEEE Transaction on Power delivery. Vol. 17 (July 2002), pp.822-827.

DOI: 10.1109/tpwrd.2002.1022810

[7] H. Zayandehroodi, et al: Automated Fault Location in a Power System with Distributed Generations Using Radial Basis Function Neural Networks. International Journal of Applied Sciences. Vol. 10 (2010), pp.3032-3041.

DOI: 10.3923/jas.2010.3032.3041

[8] Z.Z. Zeng, J. Jiang: Study of RBF Neural Network Based on Improved OLS Algorithm. Intelligent Computation Technology and Automation (2011), pp.244-247.

DOI: 10.1109/icicta.2011.70

[9] A. Rathinam, S. Padmini and V. Ravikumar: Application of Supervised Learning Artificial Neural Networks [CPNN, BPNN] for Solving Power Flow Problem. Information and Communication Technology in Electrical Sciences (2007), pp.156-160.

DOI: 10.1049/ic:20070603