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 JPEE  Vol.2 No.9 , September 2014
Study on Identification of Inductive-Motors Load Partition Based on Coherence
Abstract: A new inductive motors load equivalence algorithm based on coherence is proposed in this paper. In order to partite motors load rapidly and accurately, fuzzy c-means clustering along with particle swarm optimization (PSO-FCM) algorithm is proposed to identify coherent motors base on its physical essence of fuzziness. The merits of PSO algorithm are independent to initial value and convergent to optimum value rapidly, and the validity function is constructed to assess clustering validity. The test on IEEE 39-Bus System is presented to evaluate the effectiveness of the new algorithm, the membership matrix definite not only coherence group of motors but also correlation value of coherence between motors. The algorithm can be used to partite motor load based on coherency in dynamic equivalence with power system operating on different modes.
Cite this paper: Xia, C. , Zhou, Y. , Men, K. and Xie, Y. (2014) Study on Identification of Inductive-Motors Load Partition Based on Coherence. Journal of Power and Energy Engineering, 2, 162-169. doi: 10.4236/jpee.2014.29023.
References

[1]   Shenghu, L., Zhengkai, L., Xinjie, H. and Shusen, J. (2010) Dynamic Equivalence to Induction Generators and Wind Turbines for Power System Stability Analysis. International Symposium on Power Electronics for Distributed Generation Systems, Hefei, 16-18 June 2010, 887-892.

[2]   Baozhen, Z., Yao, Z., Qing, Z. and Zhaobin, D. (2011) Application of Dynamic Equivalence Based on Coherency in South China Power Grid. Power and Energy Engineering Conference, Wuhan, 25-28 March 2011, 1-4.

[3]   Resende, F.O., Matevosyan, J. and Milanovic, J.V. (2013) Application of Dynamic Equivalence Techniques to Derive Aggregated Models of Active Distribution Network Cells and Microgrids. PowerTech IEEE Grenoble, Grenoble, 16-20 June 2013, 1-6.

[4]   Haiqiang, Zh., Ping, J., Hui, Y. and Ran, S. (2010) Dynamic Equivalent Method of Interconnected Power Systems with Consideration of Motor Loads. Science China Technological Sciences, 40, 902-908. http://dx.doi.org/10.1007/s11431-010-0110-8

[5]   He, R., Ma, J. and Hill, D.J. (2006) Composite Load Modeling via Measurement Approach. IEEE Transactions on Power Systems, 21, 663-672. http://dx.doi.org/10.1109/TPWRS.2006.873130

[6]   Ma, J., Han, D. and He, R. (2007) Research on Identifiability of Equivalent Motor in Composite Load Model. Power Tech IEEE Lausanne, Lausanne, 1-5 July 2007, 1015-1020.

[7]   Kundur, P. (2001) Power System Stability and Control. 1st Edition, McGraw Hill Inc., New York.

[8]   Wang, J., Han, M. and Ma, J. (2010) A New Identification Strategy for Improving Convergence Stability of Load Model Parameters. International Conference on Electrical and Control Engineering, Wuhan, 25-27 June, 145-148.

[9]   Pal, N.R., Pal, K. and Keller, J.M. (2005) A Possibilistic Fuzzy c-Means Clustering Algorithm. IEEE Transactions on Fuzzy Systems, 13, 2005. http://dx.doi.org/10.1109/TFUZZ.2004.840099

[10]   Chunjuan, Y. (2011) Derivatives of Fuzzy C-Means Method and Their Application Comparisons. International Conference on Computer Science and Service System, Nanjing, 27-29 June 2011, 326-329.

[11]   Fengrui, Z., Jianshu, C. and Zhenhui, X. (2013) An Improved Particle Swarm Optimization Particle Filtering Algorithm. International Conference on Circuits and Systems, Chengdu, 15-17 November 2013, 173-177.

[12]   Lin, L., Qi, L., Junyong, L. and Chuan, L. (2008) An Im-proved Particle Swarm Optimization Algorithm. IEEE International Conference on Granular Computing, Hangzhou, 26-28 August 2008, 486-490.

[13]   Yi, S., Yunfeng, B. and Mingxin, Y. (2009) A Novel Chaos Particle Swarm Optimization (PSO) and Its Application in Pavement Maintance Decision. IEEE Conference on Industrial Electronics and Applications, Xi’an, 25-27 May 2009, 3521-3526.

[14]   Lingkui, M. and Chunchun, H. (2007) Cluster Validity Index Based on Measure of Fuzzy Partition. Computer Engineering, 33, 15-17.

 
 
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