Static Var Generators (SVG) have been widely adopted in renewable power systems to improve voltage stability. The capacity and location of SVG installation in a renewable power system both impact the improvement effect. In this paper, we investigate the optimal SVG configuration that cost-effectively improves the voltage stability of power systems with high renewable energy penetration. First, recognizing the short-circuit ratio as a key and simple indicator of system voltage stability levels, a multi-objective optimization model is established, with objectives of improving the short-circuit ratio of renewable generators and minimizing the overall SVG installation cost. Then, we propose a modified NSGA-II algorithm to solve this model, thus obtaining the number and location for installing the SVGs in a power system. Experiments conducted on the SG118 system, which integrates renewable energy, validate the effectiveness of our proposed method. Our work presents power system operators with an effective measure to maintain the voltage stability of power systems with high penetrations of renewable energy.

The increasing integration of inverter-based resou- rces is a prominent trend in power systems, which may increase the risk of large-scale blackouts. This paper proposes a complex network-based cascading failure model that incorporates the failure mechanism of power electronics-based (PE-based) nodes. This model facilitates the evaluation of cascading failure vulnerability in power electronics-penetrated (PE-penetrated) power systems, accounting for the impact of an increasing penetration level of inverter-based resources. Focusing on generation nodes with inverter-based resources (PE-based nodes) that may deviate from their stable operating limits during cascading failure and disconnect from the grid, we introduce a novel mechanism to characterize their failure behavior. Through a case study conducted on the IEEE 39-bus system, the model demonstrates its ability to accurately replicate the salient features observed in two real blackout events. Furthermore, simulation outcomes from the IEEE 118-bus system indicate the substantial impact of PE-based node failure on the cascading failure process. These results also underscore the effectiveness of utilizing the proposed model to prevent underestimating power outage risks within PE-penetrated power grids. This work emphasizes the critical importance of considering the penetration of inverter-based resources across different scenarios to safeguard the resilience of evolving power grids.

Pinning control provides an effective approach to controlling large-scale networks and conserving control resources. This article presents a solution to pinning synchronization in directed networks with a precise index that measures the pinning synchronization capability of directed networks, capturing full topological information about the networks. Building upon this index, the article utilizes matrix analysis tools, such as the non-negative matrix theory and strongly connected decomposition to analyze the impact of network structures and controller parameters on the network synchronizability. Specifically, the study investigates the influence of the in-degree of unpinned nodes, the difference between in-degrees and out-degrees of nodes, strong connectivity components, and the linear feedback control gains on the network synchronizability. Moreover, the article addresses the challenge of optimally selecting pinned nodes by using a graph partitioning algorithm and a greedy node selection algorithm, which can be applied to effectively select pinned nodes in a large-scale network. Extensive simulations on a range of real-world directed networks validate the efficiency of the proposed algorithms and demonstrate their superiority over seven baseline algorithms.

Strengthening critical components is considered one of the most essential means to enhance the robustness of power networks against cascading failure. This paper proposes an iterative method to strengthen the critical power network branches identified from a tailor-made failure propagation graph. To construct the failure propagation graph, we generate numerous cascading failure trees, capturing both temporal and spatial features of failure propagation processes from cascading failure simulations. The constructed graph is a weighted and directed graph that is able to characterize failure propagation patterns in a power network. By employing weighted eigenvector centrality to assess node criticality, we iterate through the graph to identify the most significant nodes and subsequently determine the critical power network branches to be strengthened. Simulation results in the IEEE 118 bus system demonstrate the effectiveness and efficiency of our strategy in mitigating cascading failure compared to existing methods.

With the increasing deployment of electric vehicles, transportation networks have been more closely coupled with power networks via charging stations. The functionality of the coupled system can be significantly damaged by extreme events, like man-made attacks and natural disasters. In this paper, we propose a metric to quantify network resilience and a method to dispatch electric vehicles to suitable charging stations in power grid-coupled transportation networks after being disrupted by extreme events. The emergent dispatching problem is expressed by a linear programming model which is computationally tractable. The optimization variables are binary and represent the charging stations selected for electric vehicles. The optimization objective is to minimize the sum of queue time and travel time of electric vehicles. Simulation results conducted on a synthesized power-transportation coupling system that is composed by a modified real-world transportation network and the IEEE 39 Bus test case demonstrate the efficacy of the proposed method in enhancing network resilience. Our work contributes to the advancement of more resilient modern transportation networks under extreme events.

Complex networks are vulnerable to cascading failure through which a few initial failure events lead to catastrophic network damages. Quick and accurate failure cascade prediction lays a solid foundation for taking effective measures to mitigate cascading failure. In this paper, we investigate predicting cascading failure propagation path in complex networks by using neural networks. A cascading failure simulation model considering the cumulative effect of overloading of components over time is firstly established to generate cascading failure cases in complex networks. Then, a cascading failure prediction model combining an attention mechanism and long and short-term memory (LSTM) neural networks is proposed for failure cascade prediction. We generate cascading failure cases as the ground truth with the cascading failure simulation model and make predictions with the Attention-LSTM neural network in synthesized complex networks. Simulation results show that our proposed method can predict the path of cascading failure propagation quickly and accurately.

The fluctuations of loads and renewable power plants can make the power system operate without meeting the$n-1$criterion. In this paper, we propose a node indispensability estimation (NIE) model based on the Graph Attention Network (GAT) for risk early warning. The existence of the indispensable component for specific operation conditions is predicted whose independent removal causes overloading. Considering the difficulty to place monitoring units on all system components, the state information of a part of the buses is used as input for the NIE model. The GAT is compared with other neural network algorithms such as Graph Convolutional Networks (GCN) and Multilayer Perceptron (MLP). Simulation results in IEEE test systems show that the proposed model based on GAT has sufficiently high prediction accuracies in estimating the node indispensability with partial state observations. Our work provides useful warnings for power operators to improve the system operation condition to secure sufficient safety levels.

Modern power systems utilize information, communication, and computation technologies for control and coordination of the various subsystems and enhancement of the system's operating performance. In this paper, we develop a model to simulate cascading failure in a cyber-coupled power system considering the system-level control provided by the cyber network. In the events of failure of components or subsystems, the states of the power network will be monitored, collected, and transmitted in real time to the control center, where control algorithms are performed to generate commands for controlling the power at each node. We develop an optimization algorithm for the control center that maximizes power generation and avoids power imbalance between generators and loads and overloading on each component. The vulnerability of the cyber-coupled power system to initial component failures is assessed based on the model and cyber faults that damage the state-monitoring and controlling function of the cyber layer are considered. Simulation results demonstrate that a power system coupled with a cyber control system can effectively reduce the cascading failure risk. However, the control dysfunction of the cyber layer leads to catastrophic cascading failure and intensifies the extent of power outage, making the system more vulnerable to cascading failure.

The resilience of a power system refers to its ability to resist and recover from multiple physical failures under extreme operation conditions. In this paper, we study the power grid resilience considering different time scales of recovery strategies and propose a method for identifying critical elements whose physical damage can significantly degrade the resilience of a power system. The maximum power supply (MPS) of the remaining network containing the elements that are not physically damaged is obtained and used to indicate the resilience performance of the system. We identify the critical elements in the grid by iteratively selecting and removing the link with the lowest MPS. We do simulations in the IEEE 118 Bus case to evaluate the grid resilience under various extremes and test the proposed strategy. Simulation results validate the efficacy of our proposed method in the identification of critical elements.

The coupling pattern of a cyber-coupled power system is defined by the way power nodes and cyber nodes are connected. In this paper, we study the effect of coupling patterns on robustness and functionality of cyber-coupled power systems. In a cyber-coupled power system, the cyber nodes to be coupled with power nodes are regarded as decision-making cyber nodes that are selected using a measurement called average propagation latency. Basically, a coupling pattern is formed by ranking the node criticality of the coupled cyber and power nodes, and then connecting them by a specific sequence. We introduce a parameter, called relative coupling correlation coefficient, to quantify the coupling pattern. A lower relative coupling correlation coefficient generally indicates a lower assortativity of coupling, which leads to a degradation of the functionality of coupled systems. Preliminary results show that a coupled system of a lower relative coupling correlation coefficient has better robustness. The finding indicates that increasing coupling assortativity and improving the robustness of a coupled system, are two conflicting objectives. Thus, a multi-objective problem is formulated and the Pareto-optimal solution is derived to balance the two objectives in the optimization of coupling patterns.

Voltage source converters (VSCs) have been increasingly applied in power systems for the past decade, whose interactions with the grid give rise to a variety of complex behaviors. In this paper, we study the low-frequency bifurcation phenomena of weak-grid-tied VSCs considering the phase-locked loop (PLL). The linearized state-space representation of the weak-grid-tied VSC system is firstly obtained, on which the eigenvalue-based analysis is performed. The loci of the eigenvalues show that the weak-grid-tied VSC system loses stability through a Hopf bifurcation and that improper grid parameters-the active power and the equivalent impedance of the AC system seen from the point of common coupling (PCC), can contribute to the occurrence of the bifurcation behavior of the system. Time-domain simulation results validate the correctness of the analysis and conclusions.

With the increasing popularity of blockchain technology, it has also become a hotbed of various cybercrimes. As a traditional way of scam, the phishing scam has new means of scam in the blockchain scenario and swindles a lot of money from users. In order to create a safe environment for investors, an efficient method for phishing detection is urgently needed. In this paper, we propose a three steps framework to detect phishing scams on Ethereum by mining Ethereum transaction records. First, we obtain the labeled phishing accounts and corresponding transaction records from two authorized websites. According to the collected transaction records we build an Ethereum transaction network. Then, a network embedding method node2vec which can extract the latent features of accounts is used for subsequent phishing classification. Finally, to distinguish whether the account is a phishing account, we adopt the one-class support vector machine (SVM) to classify. The experimental result demonstrates that F-score of our phishing detection method can achieve 0.846, which verifies the validity of our model. To the best of our knowledge, this is the first work that investigates the phishing scams on Ethereum based on transaction records.

In the study of power grid security, the cascading failure process has attracted increasing attention in recent years. The robustness of a power grid against cascading failure can be evaluated from both structural and functional perspective. In most previous studies, these two types of robustness were treated separately in spite of the fact that both of them are considered equally important in many scenarios. In our study, we utilize multi-objective optimization to take both aspects of the system robustness against cascading failure into consideration. Based on NSGA-II, we develop an effective attack strategy to localize the critical nodes in the power grids. The variety of our solution is preserved, enabling flexible choices by decision makers. Simulations on a realistic power grid dataset demonstrate the capability of our strategy in serving as a guideline for launching attacks in power grids, for it can damage the power grid to the greatest extent in terms of both metrics.

Modeling and analysis of cascading failures draws wide attention recently due to frequent occurrences of large blackouts all over the world. Models based on complex network theory contribute significantly on analyzing the robustness of power systems and assessing the risk probability, while they fall short of producing the propagation of the cascading failure in exact time points. This paper presents an improved topological model taking timescale into consideration, as well as the relay setting which reveals its operation more accurately according to industrial standards. The paper then validates the model using UIUC 150-bus and IEEE 39-bus test system. In order to assess the vulnerability of a power network, the multi-step attack strategy has been provided. The results demonstrate that timescales and relay settings have a critical impact during the cascading failure and the proposed attack strategy may lead to larger blackouts than normal strategies.

In this paper, we consider the failure propagation in a power system which is coupled with a cyber network. We identify the ratio of the infection rate of malware to the tripping rate of elements in the power network, defined as cyber-physical propagation ratio (CPPR), as a crucial parameter, and show that CPPR affects the propagation pattern. When CPPR is very small, the overloading effect in the power system dominates, and the failure propagation profile shows a typical "jump" or step pattern. Moreover, as CPPR increases, the step magnitude rapidly reduces and becomes less noticeable. Furthermore, we develop a simple approach to distinguish various propagation patterns for different values of CPPR. Results show that CPPR effectively characterizes the significance of cyber coupling and hence the relative impact of malicious attack from the cyber network, and its value determines how cascading failure propagates in a cyber-coupled power network.

In this paper, we develop a stochastic model from the perspective of complex networks to investigate the effects of cyber coupling on cascading failures in coupled power systems. The failure spreading in the coupled system is described by state transition and modeled as a Markov process. We simulate the dynamic profile of the cascading failures caused by the attack of cyber malwares, considering the effects of power overloading, contagion and interdependence between power grids and cyber networks. We study the coupled system created by coupling the UIUC 150 Bus System with synthesized scale-free cyber network. Simulation results present that the dynamic profile of the cascading failures in a coupled system displays a “staircase-like” pattern which can be interpreted as a combined feature of the typical step propagation profile triggered repeatedly by cyber attacks due to cyber network coupling. Results also show that cyber coupling can intensify both the extent and rapidity of power blackouts. Moreover, adopting assortative coupling patten accelerates the failures propagation, in particular under high-degree cyber node targeted attack.

In this paper, we investigate the dynamic profiles of the cascading failure propagations in power systems. We use a circuit-based power flow model and combine it with a stochastic model to describe the uncertain failure time instants. The sequence of failures is determined by power stresses of individual elements which are governed by deterministic circuit equations, while the time durations between failures are described by stochastic processes. The use of stochastic processes here addresses the uncertainties in individual components' physical failure mechanisms which may depend on manufacturing quality and environmental factors. In this model, the element failure rate is related to the extent of overloading. A network-based stochastic model is developed to study the failure propagation dynamics of the entire power network. Simulation results show that our model generates dynamic profiles of cascading failures that contains all salient features displayed in historical blackout data. The proposed model thus offers predictive information about occurrences of large-scale blackouts.

As renewable energy plants are becoming more widely accessible, one trend of power grids' evolution is decentralization which poses many challenges for grid management. In this paper, we propose a generator allocation method, based on community structure detection, for placing decentralized generators. We take node-generator distance (DG) as an indicator of optimal generators' locations and the underlying community structure is detected with a series of iterating steps. Simulation results show that our method can effectively achieve satisfying allocation solutions as well as enhance the robustness of power systems.

In this paper, we study the robustness assessment of power systems. Since the power grid is an electrical network by nature, it can be fully and accurately described by circuit laws. Based on Kirchhoff's laws and the properties of network elements, we propose a complex network model that generates power flow information given the electricity consumption and generation information. It has been widely known that large scale blackouts are results of a series of cascading failures triggered by the malfunctioning of specific critical components. Power systems can be more robust if there are fewer such critical components or the network configuration is suitably designed. The percentage of unserved nodes (PUN) caused by a failed component and the percentage of non-critical links (PNL) that will not cause severe damage are used to provide quantitative indication of a power system's robustness. We study the IEEE 118 Bus, Northern European Grid and other practical power systems. Simulation results show that small-world connection of the grid can significantly degrade a power system's robustness.