Biblio

Power system automation and communication standards are spearheading the power system transition towards a smart grid. IEC 61850 is one such standard, which is widely used for substation automation and protection. It enables real-time communication and data exchange between critical substation automation and protection devices within digital substations. However, IEC 61850 is not cyber secure. In this paper, we demonstrate the dangerous implications of not securing IEC 61850 standard. Cyber attacks may exploit the vulnerabilities of the Sampled Values (SV) and Generic Object-Oriented Substation Event (GOOSE) protocols of IEC 61850. The cyber attacks may be realised by injecting spoofed SV and GOOSE data frames into the substation communication network at the bay level. We demonstrate that such cyber attacks may lead to obstruction or tripping of multiple protective relays. Coordinated cyber attacks against the protection system in digital substations may cause generation and line disconnections, triggering cascading failures in the power grid. This may eventually result in a partial or complete blackout. The attack model, impact on system dynamics and cascading failures are veri ed experimentally through a proposed cyber-physical experimental framework that closely resembles real-world conditions within a digital substation, including Intelligent Electronic Devices (IEDs) and protection schemes. It is implemented through Hardware-in-the-Loop (HIL) simulations of commercial relays with a Real-Time Digital Simulator (RTDS).

Major blackouts are due to cascading failures in power systems. These failures usually occur at vulnerable links of the network. To identify these, indicators have already been defined using complex network theory. However, most of these indicators only depend on the topology of the grid; they fail to detect the weak links. We introduce a new metric to identify the vulnerable lines, based on the load-flow equations and the grid geometry. Contrary to the topological indicators, ours is built from the electrical equations and considers the location and magnitude of the loads and of the power generators. We apply this new metric to the IEEE 118-bus system and compare its prediction of weak links to the ones given by an industrial software. The agreement is very well and shows that using our indicator a simple examination of the network and its generator and load distribution suffices to find the weak lines.

A process of critical transmission lines identification in presented here. The criticality is based on network flow, which is essential for power grid connectivity monitoring as well as vulnerability assessment. The proposed method can be utilized as a supplement of traditional situational awareness tool in the energy management system of the power grid control center. At first, a flow network is obtained from topological as well as functional features of the power grid. Then from the duality property of a linear programming problem, the maximum flow problem is converted to a minimum cut problem. Critical transmission lines are identified as a solution of the dual problem. An overall set of transmission lines are identified from the solution of the network flow problem. Simulation of standard IEEE test cases validates the application of the method in finding critical transmission lines of the power grid.

The power outages of the last couple of years around the world introduce the indispensability of technological development to improve the traditional power grids. Early warnings of imminent failures represent one of the major required improvements. Costly blackouts throughout the world caused by the different severe incidents in traditional power grids have motivated researchers to diagnose and investigate previous blackouts and propose a prediction model that enables to prevent power outages. Although, in the new generation of power grid, the smart grid's (SG) real time data can be used from smart meters (SMs) and phasor measurement unit sensors (PMU) to prevent blackout, it demands high reliability and stability against power outages. This paper implements a proactive prediction model based on deep-belief networks that can predict imminent blackout. The proposed model is evaluated on a real smart grid dataset. Promising results are reported in the case study.

Cascading outages in power networks cause blackouts which lead to huge economic and social consequences. The traditional form of load shedding is avoidable in many cases by identifying optimal load control actions. However, if there is a change in the system topology (adding or removing loads, lines etc), the calculations have to be performed again. This paper addresses this problem by providing a workflow that 1) generates system models from IEEE CDF specifications, 2) identifies a collection of blackout causing contingencies, 3) dynamically sets up an optimization problem, and 4) generates a table of mitigation strategies in terms of minimal load curtailment. We demonstrate the applicability of our proposed methodology by finding load curtailment actions for N-k contingencies (k = 1, 2, 3) in IEEE 14 Bus system.

As societies are becoming more dependent on the power grids, the security issues and blackout threats are more emphasized. This paper proposes a new graph model for online visualization and assessment of power grid security. The proposed model integrates topology and power flow information to estimate and visualize interdependencies between the lines in the form of line dependency graph (LDG) and immediate threats graph (ITG). These models enable the system operator to predict the impact of line outage and identify the most vulnerable and critical links in the power system. Line Vulnerability Index (LVI) and Line Criticality Index (LCI) are introduced as two indices extracted from LDG to aid the operator in decision making and contingency selection. This package can be useful in enhancing situational awareness in power grid operation by visualization and estimation of system threats. The proposed approach is tested for security analysis of IEEE 30-bus and IEEE 118-bus systems and the results are discussed.