PNNL

Abstract

Communication networks in power systems are a major part of the smart grid paradigm. It enables and facilitate the automation of power grid operation as well as self-healing in contingencies. Such dependencies on communication networks though create a roam for cyber-threats. An adversary can launch an attack on the communication network which in turn reflects on power grid operation. Attacks could be in the form of false data injection into system measurements, flooding the communication channels with unnecessary data or intercepting messages. Using machine learning-based processing on data gathered from communication network and power grid is a promising solution for detecting cyber-threats. In this paper, a co-simulation on cyber-security for cross-layer strategy is presented. The advantage of such framework is the augmentation of valuable data that enhances the detection as well as identifying of anomalies in the operation of power grid. The framework is implemented on IEEE 118-bus system. The system is constructed in Mininet to simulate communication network and obtain data for analysis. The performance of the framework is investigated under various type of communication attacks.