| Title | Deep Neural Network Based Efficient Data Fusion Model for False Data Detection in Power System |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Su, Xiangjing, Zhu, Zheng, Xiao, Shiqu, Fu, Yang, Wu, Yi |
| Conference Name | 2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2) |
| Keywords | composability, data fusion, Data integration, Data models, Deep Learning, False Data Detection, feature engineering, Human Behavior, Neural networks, power system, power system stability, pubcrawl, resilience, Resiliency, security assessment, system integration, Termination of employment |
| Abstract | Cyberattack on power system brings new challenges on the development of modern power system. Hackers may implement false data injection attack (FDIA) to cause unstable operating conditions of the power system. However, data from different power internet of things usually contains a lot of redundancy, making it difficult for current efficient discriminant model to precisely identify FDIA. To address this problem, we propose a deep learning network-based data fusion model to handle features from measurement data in power system. Proposed model includes a data enrichment module and a data fusion module. We firstly employ feature engineering technique to enrich features from power system operation in time dimension. Subsequently, a long short-term memory based autoencoder (LSTM-AE) is designed to efficiently avoid feature space explosion problem during data enriching process. Extensive experiments are performed on several classical attack detection models over the load data set from IEEE 14-bus system and simulation results demonstrate that fused data from proposed model shows higher detection accuracy with respect to the raw data. |
| DOI | 10.1109/EI256261.2022.10117117 |
| Citation Key | su_deep_2022 |
Deep Neural Network Based Efficient Data Fusion Model for False Data Detection in Power System