Open AccessDynamic State Estimation of New Energy Power Systems Considering Multi-Level False Data Identification Based on LSTM-CNN
Author(s) -
Zhengnan Gao,
Shubo Hu,
Hui Sun,
Jinsong Liu,
Yuanqing Zhi,
Jun Li
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3121420
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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. The inaccurate estimation results will lead to wrong control decisions by operators. This paper proposes an improved dynamic state estimation method based on multi-level false data identification. This method uses innovation vector for the first-level identification, long-short term memory neural network for the second-level temporal identification, and convolution neural network for the third-level spatial identification. Through the identification, the mutation data are distinguished as fluctuant real data and false data. The identification results can provide precise operation information for power system, dynamically correct the filtering direction of state estimation and improve the accuracy of state estimation. The method is verified by IEEE-57 power system with actual operating data. The results show that the improved method can not only resist false data injection attacks, but also maintain high estimation accuracy in new energy power systems with strong data volatility.