Probabilistic Deep Autoencoder for Power System Measurement Outlier Detection and Reconstruction
Probabilistic Deep Autoencoder for Power System Measurement Outlier Detection and Reconstruction
复制标题
DOI：
10.1109/tsg.2019.2937043
复制
发表时间：
2020-03
影响因子：
9.6
通讯作者：
You Lin;Jianhui Wang
中科院分区：
文献类型：
--
作者：
You Lin;Jianhui Wang
本文提出了一种用于电力系统测量数据重构的概率深度自动编码器，可用于野值检测和重构。采用非参数分布估计方法获取测量数据的不确定性信息。从估计分布中提取测量数据的估计可信区间，并将其用作第一层神经网络的输入。通过多层编码和解码过程，重构测量间隔，并将其用于检测和替换孤立点。仿真结果验证了该方法的有效性。
A probabilistic deep autoencoder is proposed to reconstruct power system measurements in this paper, which can be utilized in outlier detection and reconstruction. A nonparametric distribution estimation method is employed to capture the uncertainty information of the measured data. The estimated confidence intervals of the measured data are extracted from the estimated distribution and used as input to the first layer of neural networks. Through multilayer encoding and decoding processes, the intervals of measurements are reconstructed, which are further applied to detect and replace outliers. Simulation results verify the effectiveness of the proposed method.