Yihai Zhu

Revealing Cascading Failure Vulnerability in Power Grids using Risk-Graph

Security issues related to power grid networks have attracted the attention of researchers in many fields. Recently, a new network model that combines complex network theories with power flow models was proposed. This model, referred to as the extended model, is suitable for investigating vulnerabilities in power grid networks. In this paper, we study cascading failures of power grids under the extended model. Particularly, we discover that attack strategies that select target nodes (TNs) based on load and degree do not yield the strongest attacks. Instead, we propose a novel metric, called the risk graph, and develop novel attack strategies that are much stronger than the load-based and degree-based attack strategies. The proposed approaches and the comparison approaches are tested on IEEE 57 and 118 bus systems and Polish transmission system. The results demonstrate that the proposed approaches can reveal the power grid vulnerability in terms of causing cascading failures more effectively than the comparison approaches.

Multi-Contingency Cascading Analysis of Smart Grid Based on Self-Organizing Map

In the study of power grid security, the cascading failure analysis in multi-contingency scenarios has been a challenge due to its topological complexity and computational cost. Both network analyses and load ranking methods have their own limitations. In this paper, based on self-organizing map (SOM), we propose an integrated approach combining spatial feature (distance)-based clustering with electrical characteristics (load) to assess the vulnerability and cascading effect of multiple component sets in the power grid. Using the clustering result from SOM, we choose sets of heavy-loaded initial victims to perform attack schemes and evaluate the subsequent cascading effect of their failures, and this SOM-based approach effectively identifies the more vulnerable sets of substations than those from the traditional load ranking and other clustering methods. As a result, this new approach provides an efficient and reliable technique to study the power system failure behavior in cascading effect of critical component failure.

Robust Classification Method of Tumor Subtype by Using Correlation Filters

Tumor classification based on Gene Expression Profiles (GEPs), which is of great benefit to the accurate diagnosis and personalized treatment for different types of tumor, has drawn a great attention in recent years. This paper proposes a novel tumor classification method based on correlation filters to identify the overall pattern of tumor subtype hidden in differentially expressed genes. Concretely, two correlation filters, i.e., Minimum Average Correlation Energy (MACE) and Optimal Tradeoff Synthetic Discriminant Function (OTSDF), are introduced to determine whether a test sample matches the templates synthesized for each subclass. The experiments on six publicly available data sets indicate that the proposed method is robust to noise, and can more effectively avoid the effects of dimensionality curse. Compared with many model-based methods, the correlation filter-based method can achieve better performance when balanced training sets are exploited to synthesize the templates. Particularly, the proposed method can detect the similarity of overall pattern while ignoring small mismatches between test sample and the synthesized template. And it performs well even if only a few training samples are available. More importantly, the experimental results can be visually represented, which is helpful for the further analysis of results.

Resilience Analysis of Power Grids Under the Sequential Attack

The modern society increasingly relies on electrical service, which also brings risks of catastrophic consequences, e.g., large-scale blackouts. In the current literature, researchers reveal the vulnerability of power grids under the assumption that substations/transmission lines are removed or attacked synchronously. In reality, however, it is highly possible that such removals can be conducted sequentially. Motivated by this idea, we discover a new attack scenario, called the sequential attack, which assumes that substations/transmission lines can be removed sequentially, not synchronously. In particular, we find that the sequential attack can discover many combinations of substation whose failures can cause large blackout size. Previously, these combinations are ignored by the synchronous attack. In addition, we propose a new metric, called the sequential attack graph (SAG), and a practical attack strategy based on SAG. In simulations, we adopt three test benchmarks and five comparison schemes. Referring to simulation results and complexity analysis, we find that the proposed scheme has strong performance and low complexity.

Survey of gait recognition

Gait recognition, the process of identifying an individual by his /her walking style, is a relatively new research area. It has been receiving wide attention in the computer vision community. In this paper, a comprehensive survey of video based gait recognition approaches is presented. And the research challenges and future directions of the gait recognition are also discussed.

Joint Substation-Transmission Line Vulnerability Assessment Against the Smart Grid

Power grids are often run near the operational limits because of increasing electricity demand, where even small disturbances could possibly trigger major blackouts. The attacks are the potential threats to trigger large-scale cascading failures in the power grid. In particular, the attacks mean to make substations/transmission lines lose functionality by either physical sabotages or cyber attacks. Previously, the attacks were investigated from substation-only/transmission-line-only perspectives, assuming attacks can occur only on substations/transmission lines. In this paper, we introduce the joint substation-transmission line perspective, which assumes attacks can happen on substations, transmission lines, or both. The introduced perspective is a nature extension to substation-only and transmission-line-only perspectives. Such extension leads to discovering many joint substation-transmission line vulnerabilities. Furthermore, we investigate the joint substation-transmission line attack strategies. In particular, we design a new metric, the component interdependency graph (CIG), and propose the CIG-based attack strategy. In simulations, we adopt IEEE 30 bus system, IEEE 118 bus system, and Bay Area power grid as test benchmarks, and use the extended degree-based and load attack strategies as comparison schemes. Simulation results show the CIG-based attack strategy has stronger attack performance.

Risk-aware vulnerability analysis of electric grids from attacker's perspective

Electric grid is one of the largest interconnected networks on the earth, and is vital to the operation of modern society. Within recent decades, the occurrence of several large scale power blackouts raised many concerns from different aspects. For example, the most recent India power blackout in July 2012 affected 620 Million people. Investigating the vulnerability of electric grids becomes increasingly important and urgent. In this paper, we study the vulnerability of electric grids from attackers point of view. First, the extended model based on DC power flow analysis is adopted to simulate cascading failures in electric grids; then a novel metric, called the risk graph, is proposed to reflect the hidden relationship among substations in terms of vulnerability; finally a practical multiple-node attack strategy is developed and proved to be stronger than the traditional load based approach on IEEE 57 and 118 bus systems. This work provided a new point of view toward understanding cascading failures in electric systems.

The Sequential Attack against Power Grid Networks

The vulnerability analysis is vital for safely running power grids. The simultaneous attack, which applies multiple failures simultaneously, does not consider the time domain in applying failures, and is limited to find unknown vulnerabilities of power grid networks. In this paper, we discover a new attack scenario, called the sequential attack, in which the failures of multiple network components (i.e., links/nodes) occur at different time. The sequence of such failures can be carefully arranged by attackers in order to maximize attack performances. This attack scenario leads to a new angle to analyze and discover vulnerabilities of grid networks. The IEEE 39 bus system is adopted as test benchmark to compare the proposed attack scenario with the existing simultaneous attack scenario. New vulnerabilities are found. For example, the sequential failure of two links, e.g., links 26 and 39 in the test benchmark, can cause 80% power loss, whereas the simultaneous failure of them causes less than 10% power loss. In addition, the sequential attack is demonstrated to be statistically stronger than the simultaneous attack. Finally, several metrics are compared and discussed in terms of whether they can be used to sharply reduce the search space for identifying strong sequential attacks.

Revealing temporal features of attacks against smart grid

Protecting smart grid against malicious attacks is a major task for the power and energy community. While different initial failures could trigger cascading effects resulting in same or close final impact, the pattern of intermediate cascading process varies significantly. In this paper we propose an approach to analyze temporal features and predict critical intermediate stages to help prevent a massive cascading failure. Initial victims from most vulnerable set of nodes are tested in a topological cascading model under different fault tolerances, and the results reveal that they are usually followed by a dramatic increase of failed components at some critical point. By analyzing the processes of failure propagation, we identify important temporal features of cascading failure and predict critical moments to allow quick and proper response at an early stage. This work provides informative decision support for defense against large blackouts caused either by random contingencies or attack schemes.

Smart Grid Vulnerability under Cascade-Based Sequential Line-Switching Attacks

Recently, the sequential attack, where multiple malignant contingencies are launched by attackers sequentially, has revealed power grid vulnerability under cascading failures. This paper systematically analyzes properties and features of N-k cascaded- based sequential line-switching attacks using a DC power flow based cascading failure simulator (DC- CFS). This paper first explains the key factors behind cascade-based attacks, then compares three adopted metrics with an original line-margin metric to compute vulnerability indexes and design sequential attacks. Two target search schemes, i.e., offline and online target search in sequential attacks, are also presented. Simulation results of N-2 to N-4 line-switching attacks have suggested that the proposed line margin metric produces stronger sequential attacks, and online target search is more effective than offline search. Reasons behind counter-intuitive load loss resulting from different metrics are also analyzed to facilitate future study on the risk of sequential attacks.