Biblio

State estimation (SE) plays a vital role in the reliable operation of modern power systems, gives situational awareness to the operators, and is employed in different functions of the Energy Management System (EMS), such as Optimal Power Flow (OPF), Contingency Analysis (CA), power market mechanism, etc. To increase SE's accuracy and protect it from compromised measurements, Bad Data Detection (BDD) algorithm is employed. However, the integration of Information and Communication Technologies (ICT) into the modern power system makes it a complicated cyber-physical system (CPS). It gives this opportunity to an adversary to find some loopholes and flaws, penetrate to CPS layer, inject false data, bypass existing BDD schemes, and consequently, result in security and stability issues. This paper employs a semi-supervised learning method to find normal data patterns and address the False Data Injection Attack (FDIA) problem. Based on this idea, the Probability Distribution Functions (PDFs) of measurement variations are derived for training and test data sets. Two distinct indices, i.e., Absolute Distance (AD) and Relative Entropy (RE), a concept in Information Theory, are utilized to find the distance between these two PDFs. In case an intruder compromises data, the related PDF changes. However, we demonstrate that AD fails to detect these changes. On the contrary, the RE index changes significantly and can properly detect FDIA. This proposed method can be used in a real-time attack detection process where the larger RE index indicates the possibility of an attack on the real-time data. To investigate the proposed methodology's effectiveness, we utilize the New York Independent System Operator (NYISO) data (Jan.-Dec. 2019) with a 5-minute resolution and map it to the IEEE 14-bus test system, and prepare an appropriate data set. After that, two different case studies (attacks on voltage magnitude ( Vm), and phase angle (θ)) with different attack parameters (i.e., 0.90, 0.95, 0.98, 1.02, 1.05, and 1.10) are defined to assess the impact of an attack on the state variables at different buses. The results show that RE index is a robust and reliable index, appropriate for real-time applications, and can detect FDIA in most of the defined case studies.

On-Load Tap Changing transformers are a widely used voltage regulation device. In the context of modern or smart grids, the control signals, i.e., the tap change commands are sent through SCADA channels. It is well known that the power system SCADA networks are prone to attacks involving injection of false data or commands. While false data injection is well explored in existing literature, attacks involving malicious control signals/commands are relatively unexplored. In this paper, an algorithm is developed to detect a stealthily introduced malicious tap change command through a compromised SCADA channel. This algorithm is based on the observation that a stealthily introduced false data or command masks the true estimation of only a few state variables. This leaves the rest of the state variables to show signs of a change in system state brought about by the attack. Using this observation, an index is formulated based on the ratios of injection or branch currents to voltages of the terminal nodes of the tap changers. This index shows a significant increase when there is a false tap command injection, resulting in easy classification from normal scenarios where there is no attack. The algorithm is computationally light, easy to implement and reliable when tested extensively on several tap changers placed in an IEEE 118-bus system.

Gaussian random attacks that jointly minimize the amount of information obtained by the operator from the grid and the probability of attack detection are presented. The construction of the attack is posed as an optimization problem with a utility function that captures two effects: firstly, minimizing the mutual information between the measurements and the state variables; secondly, minimizing the probability of attack detection via the Kullback-Leibler (KL) divergence between the distribution of the measurements with an attack and the distribution of the measurements without an attack. Additionally, a lower bound on the utility function achieved by the attacks constructed with imperfect knowledge of the second order statistics of the state variables is obtained. The performance of the attack construction using the sample covariance matrix of the state variables is numerically evaluated. The above results are tested in the IEEE 30-Bus test system.

A technique of finding a set of sequential circuit nodes in which Trojan Circuits (TC) may be implanted is suggested. The technique is based on applying the precise (not heuristic) random estimations of internal node observability and controllability. Getting the estimations we at the same time derive and compactly represent all sequential circuit full states (depending on input and state variables) in which of that TC may be switched on. It means we obtain precise description of TC switch on area for the corresponding internal node v. The estimations are computed with applying a State Transition Graph (STG) description, if we suppose that TC may be inserted out of the working area (out of the specification) of the sequential circuit. Reduced Ordered Binary Decision Diagrams (ROBDDs) for the combinational part and its fragments are applied for getting the estimations by means of operations on ROBDDs. Techniques of masking TCs are proposed. Masking sub-circuits overhead is appreciated.