| Title | Detection of False Data Injection Attack in Automatic Generation Control System with Wind Energy based on Fuzzy Support Vector Machine |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Chen, Ziyu, Zhu, Jizhong, Li, Shenglin, Luo, Tengyan |
| Conference Name | IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society |
| Date Published | oct |
| Keywords | attack vectors, automatic generation control, Automatic Generation Control System, Data models, False Data Detection, false data injection attack, fuzzy theory, Human Behavior, Mathematical model, Power system dynamics, pubcrawl, resilience, Resiliency, Scalability, support vector machine, Support vector machines, Wind speed, Wind Storage System, wind turbines |
| Abstract | False data injection attack (FDIA) destroys the automatic generation control (AGC) system and leads to unstable operation of the power system. Fast and accurate detection can help prevent and disrupt malicious attacks. This paper proposes an improved detection method, which is combined with fuzzy theory and support vector machine (SVM) to identify various types of attacks. The impacts of different types of FDIAs on the AGC system are analyzed, and the reliability of the method is proved by a large number of experimental data. This experiment is simulated on a single-area LFC system and the effects of adding a wind storage system were compared in a dynamic model. Simulation studies also show a higher accuracy of fuzzy support vector machine (FSVM) than traditional SVM and fuzzy pattern trees (FPTs). |
| DOI | 10.1109/IECON43393.2020.9255020 |
| Citation Key | chen_detection_2020 |
Detection of False Data Injection Attack in Automatic Generation Control System with Wind Energy based on Fuzzy Support Vector Machine