Sparse Malicious False Data Injection Attacks and Defense Mechanisms in Smart Grids

This paper discusses malicious false data injection attacks on the wide area measurement and monitoring system in smart grids. Firstly, methods of constructing sparse stealth attacks are developed for two typical scenarios: random attacks in which arbitrary measurements can be compromised and targeted attacks in which specified state variables are modified. It is already demonstrated that stealth attacks can always exist if the number of compromised measurements exceeds a certain value. In this paper it is found that random undetectable attacks can be accomplished by modifying only a much smaller number of measurements than this value. It is well known that protecting the system from malicious attacks can be achieved by making a certain subset of measurements immune to attacks. An efficient greedy search algorithm is then proposed to quickly find this subset of measurements to be protected to defend against stealth attacks. It is shown that this greedy algorithm has almost the same performance as the brute-force method but without the combinatorial complexity. Thirdly, a robust attack detection method is discussed. The detection method is designed based on the robust principal component analysis problem by introducing element-wise constraints. This method is shown to be able to identify the real measurements as well as attacks even when only partial observations are collected. The simulations are conducted based on IEEE test systems

Rule Induction-Based Knowledge Discovery for Energy Efficiency

Rule induction is a practical approach to knowledge discovery. Provided that a problem is developed, rule induction is able to return the knowledge that addresses the goal of this problem as if-then rules. The primary goals of knowledge discovery are for prediction and description. The rule format knowledge representation is easily understandable so as to enable users to make decisions. This paper presents the potential of rule induction for energy efficiency. In particular, three rule induction techniques are applied to derive knowledge from a dataset of thousands of Irish electricity customers’ time-series power consumption records, socio-demographic details, and other information, in order to address the following four problems: 1) discovering mathematically interesting knowledge that could be found useful; 2) estimating power consumption features for customers, so that personalized tariffs can be assigned; 3) targeting a subgroup of customers with high potential for peak demand shifting; and 4) identifying customer attitudes that dominate energy conservation