基于集成OS-ELM的暂态稳定评估方法

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Abstract In order to overcome the defect of the lack of on-line learning ability in existing pattern recognition-based transient stability assessment methods, a new online learning mechanism for transient stability assessment based on an ensemble of online sequential extreme learning machine (OS-ELM) model is proposed. First, OS-ELM based on incremental learning is used as a weak classifier. Then, an online boosting algorithm is employed as an ensemble learning algorithm for OS-ELM models. In this way, the stability and generalization ability of OS-ELM models is greatly improved. The effectiveness of the proposed method is validated by the simulation results on the New England 39-bus system.

Key words： Transient stability assessment extreme learning machine online learning ensemble learning wide-area measurement systems

Received: 10 August 2014 Published: 08 September 2015

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TM712

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	Li Yang
	Li Guoqing
	Gu Xueping
	Zhang Yanjun
	Han Zijiao

Cite this article:

Li Yang,Li Guoqing,Gu Xueping等. Transient Stability Assessment of Power Systems Based on Ensemble OS-ELM[J]. Transactions of China Electrotechnical Society, 2015, 30(14): 412-418.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2015/V30/I14/412

[1] Anderson P M, Fouad A A. Power system control and stability[M]. 2nd Edition. Piscataway, NJ：IEEE,2003：4-12.
[2] Amjady N, Banihashemi S A. Transient stability prediction of power systems by a new synchronism status index and hybrid classifier[J]. IET Generation, Transmission & Distribution, 2010, 4(4)：509-518.
[3] 叶圣永, 王晓茹, 刘志刚, 等．基于受扰严重机组特征及机器学习方法的电力系统暂态稳定评估[J]．中国电机工程学报,2011,31(1)：46-51． Ye Shengyong, Wang Xiaoru, Liu Zhigang, et al. Power system transient stability assessment based on severely disturbed generator attributes and machine learning method[J]．Proceedings of the CSEE, 2011, 31(1)：46-51.
[4] Gomez F R, Rajapakse A D, Annakkage U D, et al. Support vector machine-based algorithm for post- fault transient stability status prediction using synchronized measurements[J]. IEEE Transactions on Power Systems, 2011, 26(3)：1474-1483.
[5] 顾雪平, 李扬, 吴献吉．基于局部学习机和细菌群体趋药性算法的电力系统暂态稳定评估[J]．电工技术学报,2013,28(10)：271-279. Gu Xueping, Li Yang,Wu Xianji. Transient stability assessment of power systems based on local learning machine and bacterial colony chemotaxis algorithm[J]. Transactions of China Electrotechnical Society, 2013, 28(10)：271-279.
[6] 李扬,顾雪平. 基于改进最大相关最小冗余判据的暂态稳定评估特征选择[J]．中国电机工程学报, 2013, 33(34)：179-186. Li Yang, Gu Xueping. Feature selection for transient stability assessment based on improved maximal relevance and minimal redundancy criterion[J]. Proceedings of the CSEE, 2013, 33(34)：179-186.
[7] Li Y, Gu X P. Application of Online SVR in Very Short-Term Load Forecasting[J]. International Review of Electrical Engineering, 2013, 8(1): 277-282.
[8] Liang N-Y, Huang G-B, Saratchandran P, et al. A fast and accurate online sequential learning algorithm for feedforward networks[J]. IEEE Transactions on Neural Networks, 2006, 17(6): 1411-1423.
[9] Ditterrich T G. Machine learning research: four current direction[J]. Artificial Intelligence Magzine, 1997, 18(4): 97-136.
[10] Bishop C M. Pattern recognition and machine learning[M]. New York：Springer, 2006.
[11] Schapire R E. The strength of weak learnability[J]. Machine learning, 1990, 5(2): 197-227.
[12] Kearns M, Valiant L. Cryptographic limitations on learning Boolean formulae and finite automata[J]. Journal of the ACM (JACM), 1994, 41(1): 67-95.
[13] Freund Y, Schapire R E. A decision-theoretic generalization of on-line learning and an application to boosting[J]. Journal of computer and system sciences, 1997, 55(1): 119-139.
[14] Grabner H, Bischof H. On-line boosting and vision [A]. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C]. USA, IEEE Press, 2006: 260-267.
[15] Lan Y, Soh Y C, Huang G-B. Ensemble of online sequential extreme learning machine[J]. Neurocompu- ting, 2009, 72(13): 3391-3395.
[16] 张春霞, 张讲社. 选择性集成学习算法综述[J]. 计算机学报, 2011, 34(8): 1399-1410.
[17] Pai M A. Energy function analysis for power system stability[M]. Boston, MA：Kluwer Academic Publisher,1989.
[18] Yu X H, Chen G A. Efficient backpropagation learning using optimal learning rate and momentum[J]. Neural Networks, 1997, 10(3): 517-527.
[19] Huang G-B, Saratchandran P, Sundararajan N. An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks[J]．IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2004, 34(6): 2284-2292.
[20] Yingwei L, Sundararajan N, Saratchandran P. A sequential learning scheme for function approxima- tion using minimal radial basis function neural networks[J]. Neural computation, 1997, 9(2): 461- 478.
[21] Yingwei L, Sundararajan N, Saratchandran P. Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm[J]. IEEE Transactions on Neural Networks, 1998, 9(2): 308-318.