A causal event graph for cyber-power system events using synchrophasor

The probability of occurrence of cyber-power events is ever increasing along with the operational challenges. Increases in electricity demand with inadequate delivery infrastructure and cyber-physical attack threats due to technological interdependencies introduce more complex events. Accurate and timely power system event identification and detection have great significance in system operation, monitoring, and control processes. Insufficient and slow event information may lead to catastrophes. Significant research effort is being made toward developing robust monitoring systems using advanced state of the art technologies such as synchrophasor technologies. This paper extends our previous work [1] and proposes a model that leverages the causal relationship between devices in cyber physical systems. Causal event graphs (CEG), a modified version of Bayesian networks, are used to create unique paths that model deterministic signatures of each cyber-physical event. The CEG and Bayesian network can be incorporated to event detection systems (EDS) to classify cyber physical events. A causal event graph for each scenario can be created using a sequence of power system events, measureable states, and logs from different sources. A modified IEEE 9 bus system with multi zone distance protection schemes for two transmission lines is used to demonstrate the proposed concept. A total of 22 cyber-physical scenarios are mapped into CEG and Bayesian networks to define rules for cyber-power system events.