Visible to the public Identification of Smart Grid Attacks via State Vector Estimator and Support Vector Machine Methods

TitleIdentification of Smart Grid Attacks via State Vector Estimator and Support Vector Machine Methods
Publication TypeConference Paper
Year of Publication2020
AuthorsFei, Wanghao, Moses, Paul, Davis, Chad
Conference Name2020 Intermountain Engineering, Technology and Computing (IETC)
Date PublishedOct. 2020
ISBN Number978-1-7281-4291-3
Keywordsattack vectors, Cyber Attacks, Human Behavior, machine learning, Power systems, pubcrawl, reliability, resilience, Resiliency, Scalability, Smart grid, Smart grids, state estimator, Support vector machines, Topology, Training

In recent times, an increasing amount of intelligent electronic devices (IEDs) are being deployed to make power systems more reliable and economical. While these technologies are necessary for realizing a cyber-physical infrastructure for future smart power grids, they also introduce new vulnerabilities in the grid to different cyber-attacks. Traditional methods such as state vector estimation (SVE) are not capable of identifying cyber-attacks while the geometric information is also injected as an attack vector. In this paper, a machine learning based smart grid attack identification method is proposed. The proposed method is carried out by first collecting smart grid power flow data for machine learning training purposes which is later used to classify the attacks. The performance of both the proposed SVM method and the traditional SVE method are validated on IEEE 14, 30, 39, 57 and 118 bus systems, and the performance regarding the scale of the power system is evaluated. The results show that the SVM-based method performs better than the SVE-based in attack identification over a much wider scale of power systems.

Citation Keyfei_identification_2020