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基于集成OS-ELM的暂态稳定评估方法

, PP. 412-418

Keywords: 暂态稳定评估,极限学习机,在线学习,集成学习,广域测量系统

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Abstract:

针对现有基于模式识别的暂态评估方法无法在线学习的不足,本文研究了一种基于集成在线序贯极限学习机(OS-ELM)的暂态稳定评估方法。首先,使用基于增量式学习的OS-ELM作为弱分类器,然后采用在线Boosting算法进行集成进一步提高评估模型的稳定性和泛化能力,实现评估模型的在线更新。基于新英格兰39节点系统的算例结果验证了所提方法的有效性。

References

[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.

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