Unsupervised situational assessment for power grid voltage stability monitoring based on siamese autoencoder and k-Means clustering | |
Bai, Xiwei1,2; Tan, Jie1 | |
2020 | |
会议日期 | July 17-20, 2020 |
会议地点 | Hohhot, China |
英文摘要 | Accurate situational assessment and severity rating are of great importance to the voltage stability of power grid. Traditional approaches depend heavily on the network parameters and component models, which restrict their applications. In this paper, an unsupervised situational assessment scheme is proposed to achieve a voltage stability margin-based, three-class situation categorization via the knowledge-aided siamese autoencoder and k-Means clustering. The distribution characteristic of voltage stability margin is utilized to provide support for searching optimal feature subspace that enables k-Means to minimize intra-class and maximize inter-class differences through the siamese architecture. Experiments on IEEE-39 system prove that the proposed scheme outperforms classical approaches in multiple indicators, which proves it a useful situational assessment tool for power grid voltage stability monitoring. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/39272] |
专题 | 自动化研究所_综合信息系统研究中心 |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Bai, Xiwei,Tan, Jie. Unsupervised situational assessment for power grid voltage stability monitoring based on siamese autoencoder and k-Means clustering[C]. 见:. Hohhot, China. July 17-20, 2020. |
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