电力系统自动化2019，Vol.43Issue(1)：32-39,8.DOI:10.7500/AEPS20180628009
基于SDAE特征提取的含风电电网可用输电能力计算
Available Transfer Capability Calculation in Power System with Wind Power Based on SDAE Feature Extraction
摘要
Abstract
The uncertainty of wind power makes available transfer capability (ATC) calculation more difficult.A fast ATC calculation method under certain limit violation probability request is proposed based on the point estimate method, GramCharlier series expansion theory and deep learning.The types of constraints considered include static security, static voltage stability and transient stability constraints.Assuming the probability distribution function of wind power is known, the cumulative distribution function of total transfer capability (TTC) is approximated through the TTC results of two deterministic operation scenes according to two-point estimate method and Gram-Charlier series expansion.In order to calculate TTC of the deterministic operation scene fast and accurately, a fast TTC calculation model based on stacked denoising autoencoder (SDAE) is developed.After the cumulative distribution function of TTC is known, the limit violation probability is defined as the probability that the power of flowgate is more than TTC and the calculation formula of ATC under certain limit violation probability is derived.Experiment results of a real power system demonstrate that the proposed method is able to consider multiple security and stability constraints effectively and calculate ATC under different limit violation probability requests fast and accurately.关键词
可用输电能力/风电功率/深度学习/堆叠降噪自动编码器/Gram-Charlier级数Key words
available transfer capability/wind power/deep learning/stacked denoising autoencoder (SDAE)/Gram-Charlier series引用本文复制引用
闫炯程,李常刚,刘玉田..基于SDAE特征提取的含风电电网可用输电能力计算[J].电力系统自动化,2019,43(1):32-39,8.基金项目
国家重点研发计划资助项目(2017YFB0902600) （2017YFB0902600）
国家电网公司科技项目(SGJS0000DKJS1700840) This work is supported by National Key R&D Program of China (No. 2017YFB0902600) and State Grid Corporation of China (No. SGJS0000DKJS1700840). （SGJS0000DKJS1700840）
