电力系统自动化2019，Vol.43Issue(1)：102-109,241,9.DOI:10.7500/AEPS20180324001
采用长短期记忆网络与压缩感知实现电物理量轻型化方法
Lightening Realization Method of Electric Physical Quantity Based on Long-short-term Memory Network and Compressed Sensing
摘要
Abstract
To further improve the performance of the informationalized power grid, a long-short-term memory (LSTM) network and compressed sensing (CS) method are put forward to realize the lightening of electric physical quantity.The sampled data of time series changes in electrical physical quantities are used as the input of the LSTM model, and the stable results are used as output.LSTM model parameters are trained by back propagation trough time (BPTT) algorithm.After training, the model can make full use of the characteristics of the cyclic structure for pattern recognition.According to LSTM pattern recognition results, the signal selects atom library for CS to determine the measurement sampling frequency.The simulation results show that the LSTM+CS method has lower sampling frequency and less transmission parameters than traditional methods, so it can significantly save storage capacity and reduce the network traffic.关键词
电力系统/轻型化/深度学习/长短期记忆网络/压缩感知Key words
power systems/lightening/deep learning/long-short-term memory (LSTM) network/compressed sensing (CS)引用本文复制引用
周学斌,李晓明,李雷,甘凌霞..采用长短期记忆网络与压缩感知实现电物理量轻型化方法[J].电力系统自动化,2019,43(1):102-109,241,9.基金项目
国家自然科学基金资助项目(51277134) This work is supported by National Natural Science Foundation of China (No. 51277134). （51277134）
