基于马尔可夫链蒙特卡罗方法的电力系统暂态稳定概率评估

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Abstract In this paper, a power system probabilistic transient stability assessment was studied, and Markov Chain Monte Carlo method to emulation load level was put forward. Taking the relativity of random samples into account, this method was more suitable for actual power system. During simulation, transient stability assessment method is proposed based on AdaBoost-DT and took fault information as input features. The simulation of New England 39 bus test system shows Markov Chain Monte Carlo Method converges faster than traditional Monte Carlo method. At the same time, AdaBoost-DT can dramatically reduce emulation time and effectively forecast transient stability.

Key words： Transient stability assessment Markov Chain Monte Carlo method decision tree AdaBoost

Received: 09 March 2010 Published: 20 March 2014

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TM712

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	Articles by authors
	Ye Shengyong
	Wang Xiaoru
	Zhou Shu
	Liu Zhigang
	Qian Qingquan

Cite this article:

Ye Shengyong,Wang Xiaoru,Zhou Shu等. Power System Probabilistic Transient Stability Assessment Based on Markov Chain Monte Carlo Method[J]. Transactions of China Electrotechnical Society, 2012, 27(6): 168-174.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2012/V27/I6/168

[1] 王梅义. 大电网事故分析与技术应用[M]. 北京: 中国电力出版社, 2008.
[2] Anders G J. Probability concepts in electric power systems[M]. New York: John Wiley & Sons, 1990.
[3] 付川, 余贻鑫, 王东涛. 电力系统暂态稳定概率[J]. 电力系统自动化, 2006, 30(1): 24-28, 40.
Fu Chuan, Yu Yixin, Wang Dongtao. Transient stability probability of power system[J]. Automation of Electric Power System, 2006, 30(1): 24-28, 40.
[4] 王东涛, 余贻鑫, 付川. 基于实用动态安全域的输电系统概率动态安全评估[J]. 中国电机工程学报, 2007, 27(7): 29-33.
Wang Dongtao, Yu Yixin, Fu Chuan. Practical dynamic security region based probabilistic dynamic security assessment of power transmission system[J]. Proceedings of the CSEE, 2007, 27(7): 29-33.
[5] 丁明, 戴仁昶, 刘亚成, 等. 概率稳定性的蒙特卡罗仿真[J]. 清华大学学报(自然科学版), 1999, 39(3): 79-83.
Ding Min, Dai Renchang, Liu Yacheng, et al. Monte-Carlo simulation approach to probabilistic stability[J]. Journal of Tsinghua University(Sci & Tech), 1999, 39(3): 79-83.
[6] 崔凯, 房大中, 钟德成. 电力系统暂态稳定性概率评估方法研究[J]. 电网技术, 2005, 29(1): 44-49.
Cui Kai, Fang Dazhong, Zhong Decheng. Study on probabilistic assessment method for power system transient stability[J]. Power System Technology, 2005, 29(1): 44-49.
[7] 李文沅, 卢继平. 暂态稳定概率评估的蒙特卡罗方法[J]. 中国电机工程学报, 2005, 25(10): 18-23.
Li Wenyuan, Lu Jiping. Monte Carlo method for probabilistic transient stability assessment[J]. Proceedings of the CSEE, 2005, 25(10): 18-23.
[8] 吴红斌, 丁明, 李生虎, 等. 发电机和负荷模型对暂态稳定性影响的概率分析[J]. 电网技术, 2004, 28(1): 19-21, 47.
Wu Hongbin, Ding Ming, Li Shenghu, et al. Probabilistic analysis on influences of generation model and load model on transient stability[J]. Power System Technology, 2004, 28(1): 19-21, 47.
[9] 石文辉, 别朝红, 王锡凡. 大型电力系统可靠性评估中的马尔可夫链蒙特卡洛方法[J]. 中国电机工程学报, 2008, 28(4): 9-15.
Shi Wenhui, Bie Zhaohong, Wang Xifan. Application of Markov chain Monte Carlo in large-scale system reliability evaluation[J]. Proceedings of the CSEE, 2008, 28(4): 9-15.
[10] Gilks W R, Richardson S, Spiegelhalter D J. Markov chain Monte Carlo in practice[M]. New York: Chapman & Hall, 1996.
[11] Rong C, Liu J S, Xiaodong W. Convergence analyses and comparisons of Markov chain Monte Carlo algorithms in digital communications[J]. IEEE Transactions on Signal Processing, 2002, 50(2): 255-270.
[12] 李文沅. 电力系统风险评估: 模型、方法和应用[M]. 周家启, 卢继平, 译. 北京: 科学出版社, 2006.
[13] 周玉兰, 王俊永, 舒治淮, 等. 2002年全国电网继电保护与安全自动装置运行情况[J]. 电网技术, 2003, 27(9): 55-60.
Zhou Yulan, Wang Junyong, Shu Zhihuai, et al. Statistic and analysis of operation situation of protective relaying and automation devices of power systems in China in 2002[J]. Power System Technology, 2003, 27(9): 55-60.
[14] Freund Y, Schapire R E. Experiments with a New Boosting Algorithm[C]. The Thirteenth International Conference on Machine Learning, 1996: 148-156.
[15] Vapnik. 统计学习理论的本质[M]. 张学工, 译. 北京: 清华大学出版社, 2000.
[16] 卢锦玲, 朱永利, 赵洪山, 等. 提升型贝叶斯分类器在电力系统暂态稳定评估中的应用[J]. 电工技术学报, 2009, 24(5): 177-182.
Lu Jinling, Zhu Yongli, Zhao Hongshan, et al. Power system transient stability assessment based on boosting Bayesian classifier[J]. Transactions of China Electrotechnical Society, 2009, 24(5): 177-182.
[17] Duda R O, Hart P E, Stork D G. Pattern Classification[M]. New York: Wiley-Interscience, 2001.
[18] Quinlan J R. C4. 5: Programs for Machine Learning[M]. Morgan Kaufmann Publishers, 1993.
[19] Pai M A. Energy function analysis for power system stability[M]. Boston: Kluwer Academic Publishers, 1989.
[20] Witten I H, Frank E. Data Mining: Practical machine learning tools and techniques[M]. 2nd ed. Singapore: Elsevier, 2005.