基于集成OS-ELM的暂态稳定评估方法

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摘要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。

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	李扬
	李国庆
	顾雪平
	张艳军
	韩子娇

关键词 ：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

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

收稿日期: 2014-08-10 出版日期: 2015-09-08

PACS:

TM712

基金资助:2014国网公司科技项目“柔性环网控制器关键技术研究及示范应用”(2014GW-05)资助

作者简介: 李扬男,1980年生,博士,讲师,主要研究方向为电力系统运行、分析与控制、智能技术在电力系统中的应用。

引用本文:

李扬,李国庆,顾雪平,张艳军,韩子娇. 基于集成OS-ELM的暂态稳定评估方法[J]. 电工技术学报, 2015, 30(14): 412-418. Li Yang,Li Guoqing,Gu Xueping,Zhang Yanjun,Han Zijiao. Transient Stability Assessment of Power Systems Based on Ensemble OS-ELM. Transactions of China Electrotechnical Society, 2015, 30(14): 412-418.

链接本文:

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

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