Graph-theoretic algorithms for cyber-physical vulnerability analysis of power grid with incomplete information

A key focus recently has been on assessing therisk of a coordinated cyber-physical attack and minimizing the impact of a successful attack. Most of the cyberattackers will have limited system information and conventional power grid N-1 security analysis cannot be extended to assess the risk. Centrality measures are widely used in the network science and an attacker with incomplete information can use it to identify power system vulnerabilities by defining the system as a complex network but without real-time system measurements. This paper presents a graph theory based centrality indices for vulnerability assessment of the power system due to various bus and branch contingencies using limited system information and provides a preliminary defense mechanism to prevent such an attack. Proposed work answers the fundamental question of possible attack scenarios by balancing risk(limited information with low risk to get caught orhigh risk attack to access more system information) and impact(identifying contingencies with maximal impact on system operation). Statistical comparisons are made between the graph theory measures compared to the corresponding DC power flow based N-X linear sensitivity measures. A unified N-X centrality based performance index is proposed and validated against the AC power flow based performance index by doing the real-time simulations of an N-3 attack scenario. Defensive mechanisms using topology-based performance indices are also presented.

Data-driven failure diagnosis in transmission protection system with multiple events and data anomalies

To guarantee a reliable power supply,the expected operation of all the components in the power system is critical.Distance protection system is primarily responsible of isolating the faulty section from the healthy part for the grid.Failure in protection devices can result in multiple conflicting alarms at the power grid operation center and complex event analysis to manually find the root cause of the observed system state.If not handled in time,it may lead to the propagation of the faults/failures to the adjacent transmission lines and components.With availability of the synchronized measurements from phasor measurement units(PMUs),real-time system monitoring and automated failure diagnosis are feasible.With multiple adverse events and possible data anomalies,the complexity of the problem will be escalated.In this paper,a PMUbased algorithm is presented and discussed to detect the root cause of the failure in transmission protection system based on the observed state,e.g.multiple line tripping andbreaker failures.The failure diagnosis algorithm is further enhanced to come up with the fully functional version of the failure diagnosis tool,which is tailored for the cases in which the PMU anomalies are present.In the developed algorithm,the validity of the PMU data is critical.However,such causes as communication errors or cyber-attacks might lead to the PMU data anomalies.This issue is welladdressed in this paper and some major types of anomaly detection methods suitable for PMU data are discussed.Results show that the ensemble approach has some distinct advantages in data anomaly detection compared to the previously used standalone algorithms.Additionally,the enhanced failure diagnosis method is developed to clean the inaccurate data in case of the anomaly in measured voltage magnitudes.Finally,both original and enhanced versions of the tool are tested on 96-bus test system using the real-time OPAL-RT simulator.The results show the accuracy of the enhanced tool and its advantages over the primary version of the tool.