标题:Fast Estimation of Total Transfer Capability Considering both Load and Source Uncertainties
作者:Liu, Wenfeng ;Liu, Yutian ;Zhang, Liudong ;Wu, Haiwei ;Zhang, Qibing ;Yang, Ming
作者机构:[Liu, Wenfeng ;Liu, Yutian ] Shandong University, Key Laboratory of Power System, Intelligent Dispatch and Control of Ministry of Education, Jinan, Ch 更多
会议名称:2019 IEEE Sustainable Power and Energy Conference, iSPEC 2019
会议日期:21 November 2019 through 23 November 2019
来源:iSPEC 2019 - 2019 IEEE Sustainable Power and Energy Conference: Grid Modernization for Energy Revolution, Proceedings
出版年:2019
页码:1291-1296
DOI:10.1109/iSPEC48194.2019.8975281
关键词:deep learning; multi-point estimation; Nataf transformation; power system; Total transfer capability
摘要:With the development of smart grid and new energy technologies, both load and source uncertainties need be considered in the calculation of total transfer capability (TTC) of power system transmission. Based on deep learning technology and the improved multi-point estimation method, a fast TTC estimation method considering uncertainties of load power, demand response and wind power generation is proposed. The uncertainties are represented by probability distribution of prediction error and Nataf transformation is introduced to deal with the non-normal probability distributions and their correlations. The stacked denoising autoencoder is employed to estimate TTCs of operating scenarios generated by Nataf transform and the improved multi-point estimation method is used to obtain their probability. Simulation results demonstrate that the fast estimation method is able to consider both load and source uncertainties effectively and calculate TTC fast and accurately. © 2019 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079496354&doi=10.1109%2fiSPEC48194.2019.8975281&partnerID=40&md5=d003af122455f2d4bf3baa0565d34913
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