تحلیل احتمالاتی پایداری سیگنال کوچک سیستم قدرت و تنظیم هماهنگ PSSها و TCSC با در نظر گرفتن عدم قطعیت تولید مزرعه بادی
محورهای موضوعی : مهندسی برق و کامپیوتر
1 - دانشگاه تربیت مدرس
2 - دانشگاه تربیت مدرس
کلید واژه: الگوریتم ژنتیک پایداری سیگنال کوچک عدم قطعیت مزرعه بادی PSS TCSC,
چکیده مقاله :
با کاهش منابع سوخت‌های فسیلی و افزایش آلودگی محیط زیست، استفاده از انرژی‌های تجدیدپذیر روز به روز در حال افزایش است. از سوی دیگر، وقوع تجدید ساختار در صنعت برق موجب حضور هرچه بیشتر منابع تولید پراکنده در بازار برق رقابتی شده و در چنین شرایطی، فضا برای حضور مزارع بادی و تأمین بخشی از توان سیستم کاملاً مساعد می‌باشد. اما توان تولیدی مزرعه بادی وابسته به سرعت باد بوده و این عدم قطعیت در تولید موجب افزایش نگرانی‌ها در مورد اتصال این منابع به سیستم و بهره‌برداری از آنها شده است. از این رو در این مقاله روشی احتمالاتی برای مطالعه پایداری سیگنال کوچک سیستم با در نظر گرفتن عدم قطعیت تولید مزارع بادی با استفاده از روش PCM ارائه شده است. روش PCM بر پایه چندجمله‌ای‌های متعامد استوار می‌باشد که یک مدل خطی از خروجی مطلوب فراهم می‌آورد. با تغییر مداوم نقطه کار ناشی از تغییرات توان خروجی مزرعه بادی، پارامترهای تجهیزات کنترلی باید دوباره و بر اساس شرایط بهره‌برداری جدید تنظیم گردند. بدین منظور از الگوریتم ژنتیک و مدل‌های تقریبی به دست آمده برای توابع چگالی احتمال مقادیر ویژه بحرانی از روش PCM استفاده شده است. به منظور اعتبارسنجی روش پیشنهادی، از دو سیستم 10 ماشین و 16 ماشین IEEE استفاده شده است.
With the decreasing of the fossil fuels and increasing of the environmental pollution, using of renewable energy resources is growing rapidly. On the other hand, the restructured electricity industry causes to cooperation of the distributed generation resources in the competitive electricity market. In such situation, the presence of the wind farms in the power system in order to provide the system loads is quite favorable. However, wind farm generation depends on the wind speed and the uncertainty in the generation cause to some concerns about the connection and operation of the power system. So, in this paper, a probabilistic approach for small signal stability analysis with considering the wind farm generation uncertainty based on PCM method is proposed. The PCM method is based on the orthogonal polynomials which provide a linear model for desired output. The continuous changes of the wind farm generation level cause to variation on the operating point that the control equipment parameters should be adjusted based on the new operation conditions. Therefore, genetic algorithm and the approximate models which are obtained from the PCM method are used. In order to validate the proposed method, the IEEE 10-machine and IEEE 16-machine test system are used.
[1] J. Slootweg and W. Kling, "The impact of large scale wind power generation on power system oscillations," Electric Power Systems Research, vol. 67, no. 1, pp. 9-20, Oct. 2003.
[2] P. Ledesma and C. Gallardo, "Contribution of variable-speed wind farms to damping of power system oscillations," in Proc. IEEE Power Tech, pp. 190-194, Lausanne, Switzerland, 1-5 Jul. 2007.
[3] Z. Chen, "Issues of connecting wind farms power systems," in Proc. IEEE Conf. and Exhibition Asia and Pacific, 6 pp., 2005.
[4] R. Billinton, Reliability Assessment of Electric Power Systems Using Monte Carlo Methods, New York: Plenum Press, 1984.
[5] J. L. Rueda, D. G. Colome, and I. Erlich, "Assessment and enhancement of small signal stability considering uncertainties," IEEE Trans. Power System, vol. 24, no. 1, pp. 198-207, Feb. 2009.
[6] L. Meiyan, M. Jin, and Z. Y. Dong, "Uncertainties analysis of load models in small signal stability," in Proc. Int. Conf. Sustainable Power Generation and Supply, SUPERGEN'09, 6 pp, 6-7 Apr. 2009.
[7] C. Wang, et al., "Modeling analysis in power system small signal stability considering uncertainty of wind generation," in Proc. IEEE Power and Energy Society General Meeting, 7 pp., 25-29 Jul. 2010.
[8] J. Liang, G. K. Venayagamoorthy, and R. G. Harley, "Wide-area measurement based dynamic stochastic optimal power flow control for smart grids with high variability and uncertainty," IEEE Trans., vol. 3, no. 1, pp. 59-69, Mar. 2012.
[9] M. A. Tatang, Direct Incorporation of Uncertainty in Chemical and Environmental Systems, Ph.D. Thesis, Massachusetts Institute of Technology, 1995.
[10] M. Webster, M. A. Tatang, and G. J. McRae, "Application of the probabilistic collocation method for an uncertainty analysis of a simple ocean model," Tech. Rep. 4, Joint Program on the Sience and Policy of Global Change, MIT, Cambridge, MA, Jan. 1996.
[11] B. C. Lesieutre and J. R. Hockenberry, "Uncertainty analysis of power system simulations and ATC calculations using the probabilistic collocation method," in Proc. Bulk Power Systems Dynamics and Control IV-Restructuring, Santorini, Greece, Aug. 1998.
[12] J. R. Hockenberry and C. Lesieutre, "Evaluation of uncertainty in dynamic simulations of power system models: the probabilistic collocation method," IEEE Trans. on Power Systems, vol. 19, no. 3, pp. 1483-1491, Aug. 2004.
[13] L. J. Cai and I. Erlich, "Simultaneous coordinated tuning of PSS and FACTS damping controllers in large power systems," IEEE Trans. on Power Systems, vol. 20, no. 1, pp. 294-300, Feb. 2005.
[14] J. J. Sanchez-Gasca and J. H. Chow, "Power system reduction to simplify the design of damping controllers for inter-area oscillations," IEEE Trans. Power Syst., vol. 11, no. 3, pp. 1342-1349, Aug. 1996.
[15] P. Pourbeik and M. J. Gibbard, "Simultaneous coordination of power system stabilizers and FACTS device stabilizers in a multi-machine power system for enhancing dynamic performance," IEEE Trans. Power Syst., vol. 13, no. 2, pp. 473-479, May 1998.
[16] X. Lei, E. N. Lerch, and D. Povh, "Optimization and coordination of damping controls for improving system dynamic performance," IEEE Trans. Power Syst., vol. 16, no. 3, pp. 473-480, Aug. 2001.
[17] P. Kundur, Power System Stability and Control, the EPRI Power System Engineering Series Ed. New York: McGraw-Hill, 1994.
[18] J. R. Hockenberry, Evaluation of Uncertainty in Dynamic, Reduced-Order Power System Models, Ph.D. Thesis, Massachusetts Institute of Technology, 2000.
[19] P. M. Anderson and A. A. Fouad, Power System Control and Stability, IEEE Press, Piscataway, NJ, 1994.
[20] P. Sauer and M. Pai, Power System Dynamics and Stability, Prentice Hall, Upper Saddle River, NJ, 1998.
[21] Y. L. Abdel-Magid and M. A. Abido, "Optimal multi-objective design of robust power system stabilizers using genetic algorithms," IEEE Trans. Power System, vol. 18, no. 3, pp. 1125-1132, Aug. 2003.
[22] C. Y. Chung, L. Wang, F. Howell, and P. Kundur, "Generation rescheduling methods to improve power transfer capability constrained by small signal stability," IEEE Trans. Power Syst., vol. 19, no. 1, pp. 534-530, Feb. 2004.
[23] D. Yang and V. Ajjarapu, "Critical eigenvalues tracing for power system analysis via continuation of invariant subspaces and projected Arnoldi method," IEEE Trans. Power Syst., vol. 22, no. 1, pp. 324-332, Feb. 2007.
[24] K. A. Nigim and P. Parker, "Heuristic and probabilistic wind power availability estimation procedures: improved tools for technology and site selection," Renewable Energy, vol. 32, no. 4, pp. 638-648, Apr. 2007.
[25] ﻫ. احمدی، بهبود پایداری سیگنال کوچک سیستم با در نظر گرفتن عدم قطعیت تولید مزارع بادی با ظرفیت بالا با تنظیم هماهنگ PSS و TCSC، پايان‌نامه كارشناسي ارشد، دانشگاه تربیت مدرس، 1391.
