1. FARIMA model‐based communication traffic anomaly detection in intelligent electric power substations
- Author
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Qiang Yang, Leijiao Ge, Wei Ruan, Fujian Chi, and Weijie Hao
- Subjects
lcsh:Computer engineering. Computer hardware ,Computer Networks and Communications ,Computer science ,020209 energy ,Real-time computing ,protocols ,lcsh:TK7885-7895 ,02 engineering and technology ,operational performance ,lcsh:QA75.5-76.95 ,collected SCN data traffic ,IEC 61850 ,intelligent electric substations ,Artificial Intelligence ,Moving average ,voltage 110.0 kV ,0202 electrical engineering, electronic engineering, information engineering ,telecommunication traffic ,Electrical and Electronic Engineering ,Protocol (object-oriented programming) ,substations ,power engineering computing ,SCN traffic flow ,SCN performance ,020208 electrical & electronic engineering ,security of data ,Traffic flow ,Telecommunications network ,intelligent electric power substations ,Computer Science Applications ,Power (physics) ,Anomaly detection ,cyber-attacks ,lcsh:Electronic computers. Computer science ,Electric power ,data traffic patterns ,FARIMA model-based communication traffic anomaly detection ,substation communication network ,anomaly detection solution ,Information Systems - Abstract
The technological advances of intelligent electric substations have significantly improved the operational performance of power utilities by incorporating advanced monitoring and control functionalities. The data traffic patterns in substation communication network (SCN) need to be better understood to improve the SCN performance against different forms of cyber-attacks. To this end, this study presents a fractional auto-regressive integrated moving average (FARIMA)-based threshold model to characterise the SCN traffic flow based on the IEC 61850 protocol and carry out anomaly detection. The performance of the proposed anomaly detection solution is assessed and validated through numerical analysis under the condition of the cyber storm based on the collected SCN data traffic from a real 110 kV substation, and the numerical results clearly confirmed its effectiveness.
- Published
- 2018