Transient Stability Assessment of Power Systems Based on Ensemble OS-ELM
Li Yang1,Li Guoqing1,Gu Xueping2,Zhang Yanjun3,Han Zijiao3
1. Northeast Dianli University Jilin 132012 China; 2. North China Electric Power University Baoding 071003 China; 3. State Grid Liaoning Electric Power Company Limited Shenyang 110006 China
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.
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.
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