This paper investigates the strategic placement of inverter-based generators (IBGs) in power electronics penetrated (PE-penetrated) power systems, aiming to mitigate power blackout risk. Two metrics, namely distance of load to IBGs (DL2IBG) and Gini coefficient (GI), are employed to guide this placement strategy. DL2IBG assesses how concentrated the distribution of IBGs is in the network, while GI evaluates the uniformity of power generation among different IBGs. By setting the network configuration based on these metrics, the relationship between the metrics and the blackout risk evaluated through cascading failure simulation is examined. The simulation results on the IEEE 118-bus system emphasize the crucial role of determining GI across various DL2IBG in mitigating blackout risks, particularly as the penetration levels of IBGs vary. This research offers valuable and practical insights into optimizing the placement of IBGs to enhance the resilience of PE-penetrated power systems.

In this paper, we find that multiple attractors may coexist with the stable equilibrium point in an islanded microgrid containing identical grid-forming converters and a ring power network, which was once believed to be impossible in previous study. The existence of such an attractor separates the microgrid into several clusters, each of which is internally synchronized, but desynchronized with each other. We define a cluster coherence metric to measure the internal coherence of each cluster. Finally, full-circuit cycle-to-cycle simulation is provided for verification.

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.

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.

Graph neural networks (GNNs) have been widely applied to network related tasks in recent years, including node classification, link prediction, community detection, etc. The core idea of GNNs is neighborhood aggregation where nodes in a network can learn adequate representations by aggregating the information from their neighbors. Despite the great success of GNNs, few studies investigate how different types of structure of the network based data, such as random networks, small-world networks, and scale-free networks, affect the performance of GNNs in completing network related tasks. Moreover, recent studies have pointed out that the homophily of labels is one of the key properties that influence the performance of GNNs. In this work, we study the performance of GNNs for different types of network structure at different homophily levels. Comprehensive simulations on synthetic networks show the considerable impact of network structure and homophily on the performance of GNNs in terms of prediction effectiveness in node classification tasks. The findings of this work emphasize the necessary consideration of network structure of datasets in designing reliable GNNs so that the performance of these GNNs will not deviate due to structural change of the underlined data.

Cascading failure modeling and analysis provide convenient tools for assessing and enhancing the robustness of power systems against severe power outages. In this paper, we apply a neural network-based classifier to predict the onset time of cascading failure. Onset time, which has been reported as the time when the number of component failure begins to rapidly increase in the failure propagation, serves as a crucial metric to evaluate the vulnerability of power systems to cascading failure. We formulate the prediction task as a multi-class classification problem and adopt a neural network-based classifier where topological and electrical information of a power system network can be exploited for learning. Experimental results on the UIUC 150-Bus power system demonstrate a high classification accuracy by only leveraging the initial states of power networks and the initial failure sets containing the power components to be tripped at the beginning of cascading failure.

In this paper, we report for the first time that in an islanded microgrid comprising a number of grid-forming converters, several hidden attractors may exist, including periodic orbits, quasi-periodic orbits, and chaotic attractors. The system operating at a stable equilibrium point may be brought to one of these hidden attractors under some transient disturbance. Then, the system will exhibit sustained oscillation, and the grid-forming converters will cease to synchronize. Full-circuit cycle-by-cycle simulations are provided to verify these findings.

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 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.

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.