Dynamic Equivalent Modeling of Winds and Generators in Wind Farms based on Neural Regression and Clustering

Authors

Faculty of Electrical and Computer Engineering, Jundi-shapur University of Technology, Dezful, Iran

Abstract

As the size of wind farms and therefor the wind speed variety and number of generators is increased, it is of interest to work with equivalent models for wind and generators to avoid complexity in calculation and time consuming simulations. In this paper, an interval of wind inputs will be considered and with the suggestion of the neural regression and with the creation of its structure, it will be shown that how much the input winds affect the output power and its importance for feature space in the clustering, too. Normally, due to the complexity of dynamic relationship between output power and wind speed traditional regression methods become more complex. After finishing regression, with suggestion of a formula to calculate the entries of the feature space matrix, fuzzy clustering algorithm will be proposed and applied on the feature space. In each cluster the equivalent model for the wind is determined as well as the aggregated parameters are calculated based on specific formulas. The fuzzy clustering is not fallen easily in to local optimums. Strong regression as well as very closeness between equivalent and detailed models are shown as the benefits of using the proposed approach in this paper.

Keywords

References

[1] L. M. Fernandez, F. Jurado and J. R. Saenz, “Aggregated dynamic model for wind farms with doubly fed induction generator wind turbines,” Renewable Energy, vol. 33, no. 1, pp. 129-140, 2008.
[2] L. M. Fernandez, C. A. Garcia and J. R. Saenz  “Equivalent models of wind farms by using aggregated wind turbines and equivalent winds,” Energy Conversion and Management, vol. 50, no. 3, pp. 691–704, 2009.
[3] H. Q. Zhou, Z. P. Song, J. P. Wang and Y. Xue, “A Review on Dynamic Equivalent Methods for Large Scale Wind Farms,” Proceedings of the 2011 Asia-Pacific Power and Energy Engineering Conference (APPEEC), pp.1-7, 2011.
[4] J. Zou, C. Peng, Y. Yan, H. Zheng  Hong and Y. Li, “A survey of dynamic equivalent modeling for wind farm,” Renewable and Sustainable Energy Reviews, vol. 40, pp. 956–963, 2014.
[5] P. M. Anderson and B Anjan, “Stability simulation of wind turbine systems,” IEEE Transactions on Power Apparatus and Systems, vol. PAS-102, no. 12, pp. 3791–3795, 1983.
[6] Manitoba HVDC Research Centre Inc. PSCAD Users Guide [Z]. Manitoba”, Canada, 2003.
[7] H. Ya-juan, Study on the whole model of wind farms based on the measured data. Jilin:Northeast Dianli University, 2007.
[8] L. M. Fernandez, C. A. Garcia and J. R. Saenz  “Equivalent models of wind farms by using aggregated wind turbines and equivalent winds,” Energy Conversion and Management, vol. 50, no. 3, pp. 691–704, 2009.
[9] J. Brochu, C. Larose and R Gagnon, “Validation of single and multiple-machine equivalents for modeling wind power plants [J],” IEEE Transactions on Energy Conversion, vol. 26, no. 2, pp. 532–541, 2011.
[10] V. Akhmatov and H. Knudsen, “An aggregate model of a grid-connected, large-scale offshore wind farm for power stability investigations: importance of Windmill Mechanical System,” international journal of Electrical Power and Energy Systems, no. 24, pp. 709–717, 2002.
[11] H. Zareipour, D. Huang and W. Rosehart, “Wind power ramp events classification and forecasting: A data mining approach,” in Proceedings of IEEE Power Energy Society General Meeting, pp. 1–3,  San Diego, CA, USA, 2011.[1]
[12] R. Sevlian and R. Rajagopal, “Detection and statistics of wind power ramps,” IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 3610–3620, 2013.
[13] Y. Feng and S. M. Ryan,“Scenario Reduction for Stochastic Unit Commitment with Wind Penetration,” IEEE conference and exposition, PES General meeting, 2014.
[14] Q. Zhu, M. Ding and P. Han, “Equivalent modeling of DFIG-based wind power plant considering crowbar protection,” Mathematical Problems in Engineering,  2016.
[15] H. Ye, W. Pei, Z. Qi, “Analytical modeling of inertial and droop responses from a wind farm for short-term frequency regulation in power systems,” IEEE Transactions on Power Systems, vol. 31, no. 5, pp. 3414–3423, 2016.
[16] محمدرضا رحیمی و محسن اسماعیلی، «طراحی کنترل‌کننده توان و بهبود میرایی نوسانات پیچشی در توربین بادی DFIG-710 kW نصب‌شده در سایت بینالود»، مجله مهندسی برق دانشگاه تبریز، دوره 46، شماره 4، صفحه 124-134، 1395.
[17] میلاد دلالی و علیرضا جلیلیان، «محاسبه آلودگی هارمونیکی و میان هارمونیکی ژنراتورهای القائی دو سو تغذیه بادی با استفاده از یک روش ترکیبی»، مجلهمهندسیبرقدانشگاهتبریز، دوره 42، شماره 2، صفحه 25-37، 1391.
[18] M. N. S. Swamy and K. L. Du, Neural Networks and Statistical Learning. 2014th ed, 2014.
[19] H. Quan, D. Srinivasan and A. Khosravi, “Short-Term load and wind power Forecasting Using Neural Network-Based Prediction Intervals, ” IEEE Transaction on neural networks and learning systems, vol. 25, no. 2, pp. 303-315, 2014.
[20] H. A. Mohammad pour and E. Santi,“Modeling and Control of Gate-Controlled Series Capacitor Interfaced With a DFIG-Based Wind Farm,” IEEE transaction on industrial electronics, vol. 62, no. 2, pp. 1022-1033, 2015.
[21] L. M. Fernandez, J. R Saenz and F. Jurado, “Dynamic models of wind farms with fixed speed wind turbines,” Renewable Energy, vol. 8, no. 31, pp. 1203–1230, 2006.
[22] M. P. Aoller and S. Achilles, “Aggregated wind park models for analyzing power system dynamics”, In Proceedings of the 4th international workshop on large- scale integration of wind power and transmission networks for offshore wind farms, Billund, Denmark, 2003.
 

Files

Share

How to cite

Statistics