| Title | Detection of Bad Data and False Data Injection Based on Back-Propagation Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Shiqi, Li, Yinghui, Han |
| Conference Name | 2022 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia) |
| Date Published | nov |
| Keywords | Asia, Back-propagation, bad data, composability, False Data Detection, false data injection attack, Human Behavior, Neural Network, Neural networks, Power systems, pubcrawl, resilience, Resiliency, simulation, Smart grids, state estimation, Topology, Training, Training data |
| Abstract | Power system state estimation is an essential tool for monitoring the operating conditions of the grid. However, the collected measurements may not always be reliable due to bad data from various faults as well as the increasing potential of being exposed to cyber-attacks, particularly from data injection attacks. To enhance the accuracy of state estimation, this paper presents a back-propagation neural network to detect and identify bad data and false data injections. A variety of training data exhibiting different statistical properties were used for training. The developed strategy was tested on the IEEE 30-bus and 118-bus power systems using MATLAB. Simulation results revealed the feasibility of the method for the detection and differentiation of bad data and false data injections in various operating scenarios. |
| DOI | 10.1109/ISGTAsia54193.2022.10003501 |
| Citation Key | shiqi_detection_2022 |
Detection of Bad Data and False Data Injection Based on Back-Propagation Neural Network