Dynamic State Estimation of New Energy Power Systems Considering Multi-Level False Data Identification Based on LSTM-CNN
With the increase of new energy integration, it is difficult to identify the measured data and false data in power system when they are mixed into cyber network. If false data with error information is utilized in the power system state estimation, the accuracy of state estimation will be reduced. T...
Main Authors: | Zhengnan Gao, Shubo Hu, Hui Sun, Jinsong Liu, Yuanqing Zhi, Jun Li |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9580888/ |
Similar Items
-
A CNN-LSTM Ship Motion Extreme Value Prediction Model
by: ZHAN Ke, ZHU Renchuan
Published: (2023-08-01) -
Fast Prediction of Urban Flooding Water Depth Based on CNN−LSTM
by: Jian Chen, et al.
Published: (2023-04-01) -
CNN and LSTM-Based Emotion Charting Using Physiological Signals
by: Muhammad Najam Dar, et al.
Published: (2020-08-01) -
Prediction of Water Level and Water Quality Using a CNN-LSTM Combined Deep Learning Approach
by: Sang-Soo Baek, et al.
Published: (2020-12-01) -
Arrhythmia Classification Based on CNN and Bidirectional LSTM
by: LI Xingxiu, TANG Jianjun, HUA Jing
Published: (2021-12-01)