Author(s)

Can Yang¹, Yong Wang¹, Yuhao Zhou², Jiongming Ruan², Wei Liu³

¹Department of Information Security, Shanghai University of Electric Power, Shanghai, China.
²National Energy Distributed Energy Technology Research and Development (Experimental) Center, Huadian Electric Power Research Institute Co., LTD., Hangzhou, China.
³Shanghai Cloud Information Technology Co., LTD., Shanghai, China.

False data injection attacks (FIDAs) against state estimation in power system are a problem that could not be effectively solved by traditional methods. In this paper, we use four outlier detection methods, namely one-Class SVM, Robust covariance, Isolation forest and Local outlier factor method from machine learning area in IEEE14 simulation platform for test and compare their performance. The accuracy and precision were estimated through simulation to observe the classification effect.

FIDA, Machine Learning, Outlier Detection, Unsupervised Learning

Yang, C. , Wang, Y. , Zhou, Y. , Ruan, J. and Liu, W. (2018) False Data Injection Attacks Detection in Power System Using Machine Learning Method. Journal of Computer and Communications, 6, 276-286. doi: 10.4236/jcc.2018.611025.