Application of Complex Network Theory in Power System Security Assessment

The power demand increases every year around the world with the growth of population and the expansion of cities. Meanwhile, the structure of a power system becomes increasing complex. Moreover, increasing renewable energy sources (RES) has linked to the power network at different voltage levels. These new features are expected to have a negative impact on the security of the power system. In recent years, complex network (CN) theory has been studied intensively in solving practical problems of large-scale complex systems. A new direction for power system security assessment has been provided with the developments in the CN field. In this thesis, we carry out investigations on models and approaches that aim to make the security assessment from an overview system level with CN theory. Initially, we study the impact of the renewable energy (RE) penetration level on the vulnerability in the future grid (FG). Data shows that the capacity of RE has been increasing over by 10% annually all over the world. To demonstrate the impact of unpredictable fluctuating characteristics of RES on the power system stability, a CN model given renewable energy integration for the vulnerability analysis is introduced. The numerical simulations are investigated based on the simplified 14-generator model of the South Eastern Australia power system. Based on the simulation results, the impact of different penetrations of RES and demand side management on the Australian FG is discussed. Secondly, the distributed optimization performance of the communication network topology in the photovoltaic (PV) and energy storage (ES) combined system is studied with CN theory. A Distributed Alternating Direction Method of Multipliers (D-ADMM) is proposed to accelerate the convergence speed in a large dimensional communication system. It is shown that the dynamic performance of this approach is highly-sensitive to the communication network topology. We study the variation of convergence speed under different communication network topology. Based on this research, guidance on how to design a relatively more optimal communication network is given as well. Then, we focus on a new model of vulnerability analysis. The existing CN models usually neglect the detailed electrical characteristics of a power grid. In order to address the issue, an innovative model which considers power flow (PF), one of the most important characteristics in a power system, is proposed for the analysis of power grid vulnerability. Moreover, based on the CN theory and the Max-Flow theorem, a new vulnerability index is presented to identify the vulnerable lines in a power system. The comparative simulations between the power flow model and existing models are investigated on the IEEE 118-bus system. Based on the PF model, we improve a power system cascading risk assessment model. In this research the risk is defined by the consequence and probabilities of the failures in the system, which is affected by both power factors and the network structure. Furthermore, a cascading event simulation module is designed to identify the cascading chain in the system during a failure. This innovation can form a better module for the cascading risk assessment of a power system. Finally, we argue that the current cyber-physical network model have their limitations and drawbacks. The existing “point-wise” failure model is not appropriate to present the interdependency of power grid and communication network. The interactions between those two interdependent networks are much more complicated than they were described in some the prior literatures. Therefore, we propose a new interdependency model which is based on earlier research in this thesis. The simulation results confirm the effectiveness of the new model in explaining the cascading mechanism in this kind of networks

Intelligent Novel Methods for Identifying Critical Components and Their Combinations for Hypothesized Cyber-physical Attacks Against Electric Power Grids

As a revolutionary change to the traditional power grid, the smart grid is expected to introduce a myriad of noteworthy benefits by integrating the advanced information and communication technologies in terms of system costs, reliability, environmental impacts, operational flexibility, etc. However, the wider deployment of cyber networks in the power grid will bring about important issues on power system cyber security. Meanwhile, the power grid is becoming more vulnerable to various physical attacks due to vandalism and probable terrorist attacks. In an envisioned smart grid environment, attackers have more entry points to various parts of the power grid for launching a well-planned and highly destructive attack in a coordinated manner. Thus, it is important to address the smart grid cyber-physical security issues in order to strengthen the robustness and resiliency of the smart grid in the face of various adverse events. One key step of this research topic is to efficiently identify the vulnerable parts of the smart grid. In this thesis, from the perspective of smart grid cyber-physical security, three critical component combination identification methods are proposed to reveal the potential vulnerability of the smart grid. First, two performance indices based critical component combination recognition methods are proposed for more effectively identifying the critical component combinations in the multi-component attack scenarios. The optimal selection of critical components is determined according to the criticality of the components, which can be modeled by various performance indices. Further, the space-pruning based enumerative search strategy is investigated to comprehensively and effectively identify critical combinations of multiple same or different types of components. The pruned search space is generated based on the criticality of potential target component which is obtained from low-order enumeration data. Specifically, the combinatorial line-generator attack strategy is investigated by exploring the strategy for attacking multiple different types of components. Finally, an effective, novel approach is proposed for identifying critical component combinations, which is termed search space conversion and reduction strategy based intelligent search method (SCRIS). The conversion and reduction of the search space is achieved based on the criticality of the components which is obtained from an efficient sampling method. The classic intelligent search algorithm, Particle Swarm Optimization (PSO), is improved and deployed for more effectively identifying critical component combinations. MATLAB is used as the simulation platform in this study. The IEEE 30, 39, 118 and Polish 2383-bus systems are adopted for verifying the effectiveness of the proposed attack strategies. According to the simulation results, the proposed attack strategies turn out to be effective and computationally efficient. This thesis can provide some useful insight into vulnerability identification in a smart grid environment, and defensive strategies can be developed in view of this work to prevent malicious coordinated multi-component attacks which may initiate cascading failures in a cyber-physical environment

Real-time Prediction of Cascading Failures in Power Systems

Blackouts in power systems cause major financial and societal losses, which necessitate devising better prediction techniques that are specifically tailored to detecting and preventing them. Since blackouts begin as a cascading failure (CF), an early detection of these CFs gives the operators ample time to stop the cascade from propagating into a large-scale blackout. In this thesis, a real-time load-based prediction model for CFs using phasor measurement units (PMUs) is proposed. The proposed model provides load-based predictions; therefore, it has the advantages of being applicable as a controller input and providing the operators with better information about the affected regions. In addition, it can aid in visualizing the effects of the CF on the grid. To extend the functionality and robustness of the proposed model, prediction intervals are incorporated based on the convergence width criterion (CWC) to allow the model to account for the uncertainties of the network, which was not available in previous works. Although this model addresses many issues in previous works, it has limitations in both scalability and capturing of transient behaviours. Hence, a second model based on recurrent neural network (RNN) long short-term memory (LSTM) ensemble is proposed. The RNN-LSTM is added to better capture the dynamics of the power system while also giving faster responses. To accommodate for the scalability of the model, a novel selection criterion for inputs is introduced to minimize the inputs while maintaining a high information entropy. The criteria include distance between buses as per graph theory, centrality of the buses with respect to fault location, and the information entropy of the bus. These criteria are merged using higher statistical moments to reflect the importance of each bus and generate indices that describe the grid with a smaller set of inputs. The results indicate that this model has the potential to provide more meaningful and accurate results than what is available in the previous literature and can be used as part of the integrated remedial action scheme (RAS) system either as a warning tool or a controller input as the accuracy of detecting affected regions reached 99.9% with a maximum delay of 400 ms. Finally, a validation loop extension is introduced to allow the model to self-update in real-time using importance sampling and case-based reasoning to extend the practicality of the model by allowing it to learn from historical data as time progresses

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