Fast Algorithm for N-2 Contingency Problem

We present a novel selection algorithm for N-2 contingency analysis problem. The algorithm is based on the iterative bounding of line outage distribution factors and successive pruning of the set of contingency pair candidates. The selection procedure is non-heuristic, and is certified to identify all events that lead to thermal constraints violations in DC approximation. The complexity of the algorithm is O(N^2) comparable to the complexity of N-1 contingency problem. We validate and test the algorithm on the Polish grid network with around 3000 lines. For this test case two iterations of the pruning procedure reduce the total number of candidate pairs by a factor of almost 1000 from 5 millions line pairs to only 6128.Comment: HICC

Fast selection of N-2 contingencies for online security assessment

We propose a novel algorithm for selection of dangerous N-2 contingencies associated with line or generator failures. The algorithm is based on iterative filtering of the set of all possible double contingencies. It is certified to identify all the dangerous contingencies, and has the complexity comparable to the N-1 contingency screening. Tests performed on realistic model of Polish power grid with about 3000 buses show that only two iterations of algorithm allow one to certify the safety of 99.9% of all double contingencies, leaving only 0.1% of the most dangerous ones for direct analysis.Comment: Was accepted to 2013 IEEE PES General Meeting conferenc

Cascading Failures and Contingency Analysis for Smart Grid Security

The modern electric power grid has become highly integrated in order to increase the reliability of power transmission from the generating units to end consumers. In addition, today’s power system are facing a rising appeal for the upgrade to a highly intelligent generation of electricity networks commonly known as Smart Grid. However, the growing integration of power system with communication network also brings increasing challenges to the security of modern power grid from both physical and cyber space. Malicious attackers can take advantage of the increased access to the monitoring and control of the system and exploit some of the inherent structural vulnerability of power grids. Therefore, determining the most vulnerable components (e.g., buses or generators or transmission lines) is critically important for power grid defense. This dissertation introduces three different approaches to enhance the security of the smart grid. Motivated by the security challenges of the smart grid, the first goal of this thesis is to facilitate the understanding of cascading failure and blackouts triggered by multi-component attacks, and to support the decision making in the protection of a reliable and secure smart grid. In this work, a new definition of load is proposed by taking power flow into consideration in comparison with the load definition based on degree or network connectivity. Unsupervised learning techniques (e.g., K-means algorithm and self-organizing map (SOM)) are introduced to find the vulnerable nodes and performance comparison is done with traditional load based attack strategy. Second, an electrical distance approach is introduced to find the vulnerable branches during contingencies. A new network structure different than the original topological structure is formed based on impedance matrix which is referred as electrical structure. This structure is pruned to make it size compatible with the topological structure and the common branches between the two different structures are observed during contingency analysis experiments. Simulation results for single and multiple contingencies have been reported and the violation of line limits during single and multiple outages are observed for vulnerability analysis. Finally, a cyber-physical power system (CPS) testbed is introduced as an accurate cyber-physical environment in order to observe the system behavior during malicious attacks and different disturbance scenarios. The application areas and architecture of proposed CPS testbed have been discussed in details. The testbed’s efficacy is then evaluated by conducting real-time cyber attacks and exploring the impact in a physical system. The possible mitigation strategies are suggested for defense against the attack and protect the system from being unstable

Reinforcement Learning and Game Theory for Smart Grid Security

This dissertation focuses on one of the most critical and complicated challenges facing electric power transmission and distribution systems which is their vulnerability against failure and attacks. Large scale power outages in Australia (2016), Ukraine (2015), India (2013), Nigeria (2018), and the United States (2011, 2003) have demonstrated the vulnerability of power grids to cyber and physical attacks and failures. These incidents clearly indicate the necessity of extensive research efforts to protect the power system from external intrusion and to reduce the damages from post-attack effects. We analyze the vulnerability of smart power grids to cyber and physical attacks and failures, design different gametheoretic approaches to identify the critical components vulnerable to attack and propose their associated defense strategy, and utilizes machine learning techniques to solve the game-theoretic problems in adversarial and collaborative adversarial power grid environment. Our contributions can be divided into three major parts:Vulnerability identification: Power grid outages have disastrous impacts on almost every aspect of modern life. Despite their inevitability, the effects of failures on power grids’ performance can be limited if the system operator can predict and identify the vulnerable elements of power grids. To enable these capabilities we study machine learning algorithms to identify critical power system elements adopting a cascaded failure simulator as a threat and attack model. We use generation loss, time to reach a certain percentage of line outage/generation loss, number of line outages, etc. as evaluation metrics to evaluate the consequences of threat and attacks on the smart power grid.Adversarial gaming in power system: With the advancement of the technologies, the smart attackers are deploying different techniques to supersede the existing protection scheme. In order to defend the power grid from these smart attackers, we introduce an adversarial gaming environment using machine learning techniques which is capable of replicating the complex interaction between the attacker and the power system operators. The numerical results show that a learned defender successfully narrows down the attackers’ attack window and reduce damages. The results also show that considering some crucial factors, the players can independently execute actions without detailed information about each other.Deep learning for adversarial gaming: The learning and gaming techniques to identify vulnerable components in the power grid become computationally expensive for large scale power systems. The power system operator needs to have the advanced skills to deal with the large dimensionality of the problem. In order to aid the power system operator in finding and analyzing vulnerability for large scale power systems, we study a deep learning technique for adversary game which is capable of dealing with high dimensional power system state space with less computational time and increased computational efficiency. Overall, the results provided in this dissertation advance power grids’ resilience and security by providing a better understanding of the systems’ vulnerability and by developing efficient algorithms to identify vulnerable components and appropriate defensive strategies to reduce the damages of the attack

Computational and Analytical Tools for Resilient and Secure Power Grids

Enhancing power grids' performance and resilience has been one of the greatest challenges in engineering and science over the past decade. A recent report by the National Academies of Sciences, Engineering, and Medicine along with other studies emphasizes the necessity of deploying new ideas and mathematical tools to address the challenges facing the power grids now and in the future. To full this necessity, numerous grid modernization programs have been initiated in recent years. This thesis focuses on one of the most critical challenges facing power grids which is their vulnerability against failures and attacks. Our approach bridges concepts in power engineering and computer science to improve power grids resilience and security. We analyze the vulnerability of power grids to cyber and physical attacks and failures, design efficient monitoring schemes for robust state estimation, develop algorithms to control the grid under tension, and introduce methods to generate realistic power grid test cases. Our contributions can be divided into four major parts: Power Grid State Prediction: Large scale power outages in Australia (2016), Ukraine (2015), Turkey (2015), India (2013), and the U.S. (2011, 2003) have demonstrated the vulnerability of power grids to cyber and physical attacks and failures. Power grid outages have devastating effects on almost every aspect of modern life as well as on interdependent systems. Despite their inevitability, the effects of failures on power grids' performance can be limited if the system operator can predict and understand the consequences of an initial failure and can immediately detect the problematic failures. To enable these capabilities, we study failures in power grids using computational and analytical tools based on the DC power flow model. We introduce new metrics to efficiently evaluate the severity of an initial failure and develop efficient algorithms to predict its consequences. We further identify power grids' vulnerabilities using these metrics and algorithms. Power Grid State Estimation: In order to obtain an accurate prediction of the subsequent effects of an initial failure on the performance of the grid, the system operator needs to exactly know when and where the initial failure has happened. However, due to lack of enough measurement devices or a cyber attack on the grid, such information may not be available directly to the grid operator via measurements. To address this problem, we develop efficient methods to estimate the state of the grid and detect failures (if any) from partial available information. Power Grid Control: Once an initial failure is detected, prediction methods can be used to predict the subsequent effects of that failure. If the initial failure is causing a cascade of failures in the grid, a control mechanism needs to be applied in order to mitigate its further effects. Power Grid Islanding is an effective method to mitigate cascading failures. The challenge is to partition the network into smaller connected components, called islands, so that each island can operate independently for a short period of time. This is to prevent the system to be separated into unbalanced parts due to cascading failures. To address this problem, we introduce and study the Doubly Balanced Connected graph Partitioning (DBCP) problem and provide an efficient algorithm to partition the power grid into two operating islands. Power Grid Test Cases for Evaluation: In order to evaluate algorithms that are developed for enhancing power grids resilience, one needs to study their performance on the real grid data. However, due to security reasons, such data sets are not publicly available and are very hard to obtain. Therefore, we study the structural properties of the U.S. Western Interconnection grid (WI), and based on the results we present the Network Imitating Method Based on LEarning (NIMBLE) for generating synthetic spatially embedded networks with similar properties to a given grid. We apply NIMBLE to the WI and show that the generated network has similar structural and spatial properties as well as the same level of robustness to cascading failures. Overall, the results provided in this thesis advance power grids' resilience and security by providing a better understanding of the system and by developing efficient algorithms to protect it at the time of failure