Identification of the K-most Vulnerable Entities in a Smart Grid System

Abstract

A smart grid system can be considered as a multilayered network with power network in one layer and communication network in the other. The entities in both the layers exhibit complex intra-and-interdependencies between them. A reliable decision making by the smart grid operator is contingent upon correct analysis of such dependencies between its entities and also on accurate identification of the most critical entities in the system. The Modified Implicative Interdependency Model (MIIM) [1] successfully captures such dependencies using multi-valuedBoolean Logic based equations called Interdependency Relations (IDRs) after most of the existing models made failed attempts in doing that. In this paper, for any given integer K, this model is used to identify the K-most vulnerable entities in a smart grid, failure of which can maximize the network damage. Owing to the problem being NP complete, an Integer Linear Programming (ILP) based solution is given here. Validation of the model [1] and the results of the ILP based solution is done by simulating a smart grid system of IEEE 14-Bus using MATPOWER and Java Network Simulator (JNS). Simulation results prove that not only the model MIIM [1] is correct but also it can predict the network damage for failure of K-most vulnerable entities more accurately than its predecessor Implicative Interdependency Model (IIM) [2].

A Self-Updating K-Contingency List for Smart Grid System

“…The problem of finding K-Contingency List is NP complete, which is already proved in [3]. Therefore, an ILP based solution for the problem is given in [2] and a faster heuristic solution is given in Section IV of this paper. Also, validation of the results is done by comparing the ILP based and heuristic solution results with the simulation results.…”

“…In both IIM [2] and MIIM [1], the smart grid system can be viewed as a multilayer network, represented as a set 𝐽(𝐸, 𝐹(𝐸))…”

“…However, in order to identify the contingency list, (i) the design of each layer of network should be clear to the researchers, and (ii) there should be an appropriate model that can represent the complex intraand-interdependencies between the entities in the smart grid. In [2], a simplistic and easy Boolean Logic based dependency model, termed as the Implicative Interdependency Model (IIM) is proposed which very accurately captures the complex interdependencies between the entities in a critical infrastructure. Yet, IIM also has drawbacks like (i) ignoring the effects of failure of entities in the Information and Communication Technology (ICT) layer, (ii) lack of ICT network design information and (iii) binary nature; that means an entity can either fail or operate only and it does not take into account any reduced level of operation for the entities in the system.…”

“…In this paper, a novel method to generate a selfupdating K-contingency list at for a smart grid is proposed. It is already proved in [2] that the problem of identifying the K-contingency list is NP-complete. Therefore, an Integer Linear Programming (ILP) based solution for the problem is provided in this paper using the MIIM IDRs.…”

Identification and Mitigation of False Data Injection using Multi State Implicative Interdependency Model (MSIIM) for Smart Grid

“…These PMUs sample current and voltage phases at the rate of 48 samples per cycle and send 30 samples per second. CCs use these PMU data to perform a number of analytical tasks like state estimation, to estimate voltage stability margin, to validate generator model, generate a contingency list [3] for the network and so on. This brings the need for a fast and secure communication network to carry the PMU data from the substations to the CCs with minimum delay and in a secure manner.…”