Biblio

A successful Smart Grid system requires purpose-built security architecture which is explicitly designed to protect customer data confidentiality. In addition to the investment on electric power infrastructure for protecting the privacy of Smart Grid-related data, entities need to actively participate in the NIST interoperability framework process; establish policies and oversight structure for the enforcement of cyber security controls of the data through adoption of security best practices, personnel training, cyber vulnerability assessments, and consumer privacy audits.

To ensure reliable and predictable service in the electrical grid it is important to gauge the level of trust present within critical components and substations. Although trust throughout a smart grid is temporal and dynamically varies according to measured states, it is possible to accurately formulate communications and service level strategies based on such trust measurements. Utilizing an effective set of machine learning and statistical methods, it is shown that establishment of trust levels between substations using behavioral pattern analysis is possible. It is also shown that the establishment of such trust can facilitate simple secure communications routing between substations.

Exhaustive enumeration of a S-select-k problem for hypothesized substations outages can be practically infeasible due to exponential growth of combinations as both S and k numbers increase. This enumeration of worst-case substations scenarios from the large set, however, can be improved based on the initial selection sets with the root nodes and segments. In this paper, the previous work of the reverse pyramid model (RPM) is enhanced with prioritization of root nodes and defined segmentations of substation list based on mean-time-to-compromise (MTTC) value that is associated with each substation. Root nodes are selected based on the threshold values of the substation ranking on MTTC values and are segmented accordingly from the root node set. Each segmentation is then being enumerated with S-select-k module to identify worst-case scenarios. The lowest threshold value on the list, e.g., a substation with no assignment of MTTC or extremely low number, is completely eliminated. Simulation shows that this approach demonstrates similar outcome of the risk indices among all randomly generated MTTC of the IEEE 30-bus system.

Nowadays is increasingly used process bus for communication of equipments in substations. In addition to signaling various statuses of device using GOOSE messages it is possible to transmit measured values, which can be used for diagnostic of system or other advanced functions. Transmission of such values via Ethernet is well defined in protocol IEC 61850-9-2. Paper introduces a tool designed for verification of sampled values generated by various devices using this protocol.
 

Modern power systems heavily rely on the associated cyber network, and cyber attacks against the control network may cause undesired consequences such as load shedding, equipment damage, and so forth. The behaviors of the attackers can be random, thus it is crucial to develop novel methods to evaluate the adequacy of the power system under probabilistic cyber attacks. In this study, the external and internal cyber structures of the substation are introduced, and possible attack paths against the breakers are analyzed. The attack resources and vulnerability factors of the cyber network are discussed considering their impacts on the success probability of a cyber attack. A procedure integrating the reliability of physical components and the impact of cyber attacks against breakers are proposed considering the behaviors of the physical devices and attackers. Simulations are conducted based on the IEEE RTS79 system. The impact of the attack resources and attack attempt numbers are analyzed for attackers from different threats groups. It is concluded that implementing effective cyber security measures is crucial to the cyber-physical power grids.