Failures In Power Grids Research Articles

Vulnerability Assessment of Power Grids Against Cost-Constrained Hybrid Attacks

The frequent occurrences of cascading failures in power grids have been receiving continuous attention in recent years. An urgent task for us is to assess the vulnerability of power grids against various kinds of attacks, which trigger cascading failures. In this brief, we formulate a cost-constrained hybrid attack in power grids, where both nodes and links are targeted with a limited total attack cost. Based on the consequence and cost of removing a component (node or link), we propose an attack centrality metric for components, which can be either local or global depending on the depth of cascading failures. We further propose a greedy hybrid attack and another optimal hybrid attack by applying the attack centrality. Simulation results on IEEE bus test data demonstrate that the optimal attack is more efficient than the greedy one. Furthermore, we find, counterintuitively, that the local centrality based attack algorithms perform better than the global centrality based ones when attack cost is a concern. Our work can help in the robustness optimization of power systems by revealing the worst-case attack vulnerability and most vulnerable components.

Decreased resilience in power grids under dynamically induced vulnerabilities

In this paper, a methodology inspired on bond and site percolation methods is applied to the estimation of the resilience against failures in power grids. Our approach includes vulnerability measures with both dynamical and structural foundations as an attempt to find more insights about the relationships between topology and dynamics in the second-order Kuramoto model on complex networks. As test cases for numerical simulations, we use the real-world topology of the Colombian power transmission system, as well as randomly generated networks with spatial embedding. It is observed that, by focusing the attacks on those dynamical vulnerabilities, the power grid becomes, in general, more prone to reach a state of total blackout, which in the case of node removal procedures it is conditioned by the homogeneity of power distribution in the network.

Bayesian hierarchical methods for modeling electrical grid component failures

This paper presents a probabilistic modeling framework for component failures in electric power grids. Failure probabilities for grid components are often estimated using parametric models informed by past observations of failure. This work formulates a Bayesian hierarchical framework designed to integrate data and domain expertise to understand the failure properties of a regional power system, where variability in the expected performance of individual components gives rise to failure processes that are heterogeneous and uncertain. We use Bayesian methods to fit failure models to failure data generated in simulation. We test our algorithm by evaluating differences between the data-generating model, our Bayesian hierarchical model, and maximum likelihood parameter estimates. We evaluate how well each model can approximate the failure properties of individual components, and of the system overall. Finally, we define an upgrade policy for achieving targeted reductions in risk exposure, and compare the magnitude of upgrades recommended by each model.

Critical Component Analysis in Cascading Failures for Power Grids Using Community Structures in Interaction Graphs

Cascading phenomena have been studied extensively in various networks. Particularly, it has been shown that the community structures in networks impact their cascade processes. However, the role of community structures in cascading failures in power grids have not been studied heretofore. In this paper, cascading failures in power grids are studied using interaction graphs. Key evidence has been provided that the community structures in interaction graphs bear critical information about the cascade process and the role of system components in cascading failures in power grids. Furthermore, a centrality measure based on the community structures is proposed to identify critical components of the system, which their protection can help in containing failures within a community and prevent the propagation of failures to large sections of the power grid. Various criticality evaluation techniques, including data driven, epidemic simulation based, power system simulation based, and graph based, have been used to verify the importance of the identified critical components in the cascade process and compare them with those identified by traditional centrality measures. Moreover, it has been shown that the loading level of the power grid impacts the interaction graph and consequently, the community structure and criticality of the components in the cascade process.

A method for evaluating the uncertainty of short-circuit current in power system with photovoltaic power generation

Short-circuit current of power system with photovoltaic power generation is related to the fault current provided by the photovoltaic power supply. In this paper, based on the analysis of low voltage ride-through uncertain region of photovoltaic power generation, the random model of low voltage off-grid is constructed. Further combining with the system fault information, based on the monte carlo simulation, a probabilistic assessment method of short-circuit current in power system is proposed, which takes into account uncertainty of photovoltaic low voltage ride-through. Finally, an example is given to verify the proposed method. The method in this paper comprehensively considers the random factors of power grid failure and photovoltaic power generation low voltage off-grid, and the short-circuit current result can accurately reflect actual short-circuit current level of the system.

Interaction Graphs for Cascading Failure Analysis in Power Grids: A Survey

Understanding and analyzing cascading failures in power grids have been the focus of many researchers for years. However, the complex interactions among the large number of components in these systems and their contributions to cascading failures are not yet completely understood. Therefore, various techniques have been developed and used to model and analyze the underlying interactions among the components of the power grid with respect to cascading failures. Such methods are important to reveal the essential information that may not be readily available from power system physical models and topologies. In general, the influences and interactions among the components of the system may occur both locally and at distance due to the physics of electricity governing the power flow dynamics as well as other functional and cyber dependencies among the components of the system. To infer and capture such interactions, data-driven approaches or techniques based on the physics of electricity have been used to develop graph-based models of interactions among the components of the power grid. In this survey, various methods of developing interaction graphs as well as studies on the reliability and cascading failure analysis of power grids using these graphs have been reviewed.

Publisher's Note: "Don't go chasing artificial waterfalls: Artificial line limits and cascading failures in power grids" [Chaos 29, 113117 (2019)

Publisher's Note: "Don't go chasing artificial waterfalls: Artificial line limits and cascading failures in power grids" [Chaos 29, 113117 (2019)

Research on Optimization of Robustness for Cascading Failure in Power Grid

When considering the cascading failures or the robust performance of the power network, most of the researches in network science are based on the topology of the power network. However, according to some researches, only using the topology of the power network can not reasonably evaluate the robustness of the power network.Therefore, when considering the cascading failures in the power network, we must combine the electrical characteristics with the network topology to make a better evaluation. From the perspective of network science, the related research on cascading failures has made a lot of progress. In order to optimize the robustness of power network, the optimal topology of power network is designed under the cascading failure model combined with real electrical characteristics.

A Framework for Healthcare Resilience During Widespread Electrical Power Loss

Healthcare delivery systems are vulnerable to a prolonged loss of electric power, particularly an extended regional or multi‐region breakdown in the electric grid. This is attributable to the reliance of acute care and ambulatory facilities on continuous electric supply for both life‐saving and vital facility support functions. We describe how a framework for disaster resilience can be applied to improve healthcare system adaptability to such scenarios. Our recommendations emphasize important preparedness efforts necessary to maintain—as best as possible—healthcare services during a period of extended regional of multi‐region electrical power loss.Specifically, we call for the development and activation of energy resilient disaster resource hospitals, the need for enhanced healthcare coalition (HCC), Medical Reserve Corps, and national disaster medical system involvement during high‐impact electric outage conditions, the necessity for a centralized federal coordination function, and public engagement to facilitate a culture of resilience to health disasters, including the extended loss of power. In order to increase preparedness, we advocate the development and implementation of pilot projects intended to enhance health sector resilience to power grid failure.

Protocol Proxy: An FTE-based covert channel

In a hostile network environment, users must communicate without being detected. This involves blending in with the existing traffic. In some cases, a higher degree of secrecy is required. We present a proof-of-concept format transforming encryption (FTE)-based covert channel for tunneling TCP traffic through protected static protocols. Protected static protocols are UDP-based protocols with variable fields that cannot be blocked without collateral damage, such as power grid failures. We (1) convert TCP traffic to UDP traffic, (2) introduce observation-based FTE, and (3) model interpacket timing with a deterministic Hidden Markov Model (HMM). The resulting Protocol Proxy has a very low probability of detection and is an alternative to current covert channels. We tunnel a TCP session through a UDP protocol and guarantee delivery. Observation-based FTE ensures traffic cannot be detected by traditional rule-based analysis or DPI. A deterministic HMM ensures the Protocol Proxy accurately models interpacket timing to avoid detection by side-channel analysis. Finally, the choice of a protected static protocol foils stateful protocol analysis and causes collateral damage with false positives.

Don't go chasing artificial waterfalls: Artificial line limits and cascading failures in power grids.

Research on cascading failures in power-transmission networks requires detailed data on the capacity of individual transmission lines. However, these data are often unavailable to researchers. Consequently, line limits are often modeled by assuming that they are proportional to some average load. However, there is scarce research to support this assumption as being realistic. In this paper, we analyze the proportional loading (PL) approach and compare it to two linear models that use voltage and initial power flow as variables and are trained on the line limits of a real power network that we have access to. We compare these artificial line-limit methods using four tests: the ability to model true line limits, the damage done during an attack, the order in which edges are lost, and accuracy ranking the relative performance of different attack strategies. We find that the linear models are the top-performing method or are close to the top in all the tests we perform. In comparison, the tolerance value that produces the best PL limits changes depending on the test. The PL approach was a particularly poor fit when the line tolerance was less than two, which is the most commonly used value range in cascading failure research. We also find indications that the accuracy of modeling line limits does not indicate how well a model will represent grid collapse. The findings of this paper provide an understanding of the weaknesses of the PL approach and offer an alternative method of line-limit modeling.

Parallel Control of Converters with Energy Storage Equipment in a Microgrid

The converter in a microgrid uses the active power and reactive power (PQ) control strategy when connected to the grid. In the case of failure of large power grid, the converters are required to be connected in parallel under the condition of island to provide power to the load. In this paper, a new control method for the parallel operation of converters based on V/F control is proposed. The V/F control is used to ensure the output voltages have the same amplitude and frequency, then the converters will only produce circulating current caused by phase angle inconsistency. The phase angle self-synchronization strategy is proposed to make sure the phase angle of output voltage of all converters in the system are consistent. First, a large inductor is added to the end of the converter to ignore the line reactance, through this, the measured voltage at the terminal of the converter roughly equals to the voltage of the load, thus, every converter has the same reference of phase angle. Using the proposed phase angle self-synchronization strategy allows the output voltage of every converter to have the same phase angle, so that there is no circulating current between converters, and the power is evenly distributed among the converters. The simulation verification was carried out on the Power Simulation (PSIM) simulation platform, and the experimental verification was implemented on the hardware experimental platform. Both results demonstrate the effectiveness of the proposed strategy. This method is highly reliable and easy to implement, and the circulating current can be reduced effectively.

Locating the Source of Diffusion in Complex Networks via Gaussian-Based Localization and Deduction

Locating the source that undergoes a diffusion-like process is a fundamental and challenging problem in complex network, which can help inhibit the outbreak of epidemics among humans, suppress the spread of rumors on the Internet, prevent cascading failures of power grids, etc. However, our ability to accurately locate the diffusion source is strictly limited by incomplete information of nodes and inevitable randomness of diffusion process. In this paper, we propose an efficient optimization approach via maximum likelihood estimation to locate the diffusion source in complex networks with limited observations. By modeling the informed times of the observers, we derive an optimal source localization solution for arbitrary trees and then extend it to general graphs via proper approximations. The numerical analyses on synthetic networks and real networks all indicate that our method is superior to several benchmark methods in terms of the average localization accuracy, high-precision localization and approximate area localization. In addition, low computational cost enables our method to be widely applied for the source localization problem in large-scale networks. We believe that our work can provide valuable insights on the interplay between information diffusion and source localization in complex networks.

Control of Communications-Dependent Cascading Failures in Power Grids

The most severe failures in power grids are often characterized as cascading failures where an initial event triggers consequent failures all along the grid often leading to blackouts. Upon identifying a failure and its cascade potential, timely control actions should be performed by the grid operators to mitigate the effect of the cascade. These actions have to be delivered to one or more control devices, creating a dependency between the power grid and its control network. This paper examines the dependency of the operation of the power grid on the control network. Different from literature studies on failure control, our dependency model captures the impact of networking parameters. We formulate an algorithmic model that describes the impact of this dependency on cascade control. Based on this model, we propose an efficient cascade control algorithm using load shedding with consideration of delays in the communication network for power grids. Finally, we evaluate the impact of the power-communication network dependency with uncontrolled grids, ideal/simple control grids and our proposed control scheme. The results demonstrate that the proposed algorithm can significantly reduce the failure of power lines while sustaining larger power demand for users.

Dynamical modeling of cascading failures in the Turkish power grid.

A reliable supply of electricity is critical for our modern society, and any large-scale disturbance of the electrical system causes substantial costs. In 2015, one overloaded transmission line caused a cascading failure in the Turkish power grid, affecting about 75×106 people. Here, we analyze the Turkish power grid and its dynamical and statistical properties. Specifically, we propose, for the first time, a model that incorporates the dynamical properties and the complex network topology of the Turkish power grid to investigate cascading failures. We find that the network damage depends on the load and generation distribution in the network with centralized generation being more susceptible to failures than a decentralized one. Furthermore, economic considerations on transmission line capacity are shown to conflict with stability.

Optimal Configuration of Interdependence Between Communication Network and Power Grid

Smart grid is a cyber-physical system with interdependent power grid and communication network. Such interdependence make the smart grid fragile against cascading failures, where an initial failure in communication network may lead to further failures in power grid, and vice versa. We prevent such inter-network cascading failure by stopping an initial failure from propagating. This is achieved by providing a sufficient number of power-disjoint communication routes between power nodes and control center. The number of power-disjoint routes is a robustness metric. We aim to maximize robustness by configuring the interdependence relationship between power grid and communication networks. Such relationship indicates which power node supplies energy to which communication node, and which communication route is used to connect which power node to the control center. Following Menger's Theorem, we transform such configuration problem into a Maxflow problem and solve it using the simplex algorithm. We have evaluated the proposed method through extensive simulations. The results confirm the optimality and computational efficiency of the proposed method. The number of power-disjoint routes increases with an increase in the network size, but can never exceed the number of node-disjoint routes for a same network.

Revealing Impacts of Cyber Attacks on Power Grids Vulnerability to Cascading Failures

Cascading failures in power grids are typically triggered by initiating contingencies (ICs). Traditional assumption is that those multiple-component ICs have low chances of occurring. However, considering the increasing cyber events in power grids, this brief reveals that smart attackers can target at critical ICs and implement well-designed cyber attacks to highly increase the ICs’ occurrence probabilities by overloading key branches in the grid. This will greatly increase the grid vulnerability to cascading failures. In this brief, such issue is revealed and analyzed using a two-step approach, which consists of a high-probability IC screening and a deterministic cascading testing. The simulation results on the IEEE 118-bus systems verify the effectiveness of the proposed approach and highlight the increased risk imposed by cyber-attacks.

Semi-Active Vibration Control of a Non-Collocated Civil Structure Using Evolutionary-Based BELBIC

A buildings resilience to seismic activity can be increased by providing ways for the structure to dynamically counteract the effect of the Earth’s crust movements. This ability is fundamental in certain regions of the globe, where earthquakes are more frequent, and can be achieved using different strategies. State-of-the-art anti-seismic buildings have, embedded on their structure, mostly passive actuators such as base isolation, Tuned Mass Dampers (TMD) and viscous dampers that can be used to reduce the effect of seismic or even wind induced vibrations. The main disadvantage of this type of building vibration reduction strategies concerns their inability to adapt their properties in accordance to both the excitation signal or structural behaviour. This adaption capability can be promoted by adding to the building active type actuators operating under a closed-loop. However, these systems are substantially larger than passive type solutions and require a considerable amount of energy that may not be available during a severe earthquake due to power grid failure. An intermediate solution between these two extremes is the introduction of semi-active actuators such as magneto–rheological dampers. The inclusion of magneto–rheological actuators is among one of the most promising semi-active techniques. However, the overall performance of this strategy depends on several aspects such as the actuators number and location within the structure and the vibration sensors network. It can be the case where the installation leads to a non-collocated system which presents additional challenges to control. This paper proposes to tackle the problem of controlling the vibration of a non-collocated three-storey building by means of a brain–emotional controller tuned using an evolutionary algorithm. This controller will be used to adjust the stiffness coefficient of a magneto–rheological actuator such that the building’s frame oscillation under earthquake excitation, is mitigated. The obtained results suggest that, using this control strategy, it is possible to reduce the building vibration to secure levels.

Comparative Study on Prediction Algorithms for Power Grid System Access Failure Times

As functions of the power grid system gradually increase and its scale becomes larger, the frequency of access failure times will gradually increase. In order to prevent access failures and make countermeasures in advance, we perform model fitting analysis and fault risk prediction for the time series of power grid failure code. In this paper, we use the failure code 404 as the training data and use the SARIMA algorithm, Fbprophet algorithm, holt-winter algorithm, and GM algorithm respectively to construct a time series prediction model. According to the result of the model building and the calculating, we find that the root mean square error of SARIMA algorithm is 258.85, which is the lowest among these algorithms, and the root mean square errors of Prophet and holt-winter algorithm are 749.288 and 809.89, respectively. However, the root mean square error of GM algorithm reaches 1710.95, which is 6 times as many as the SARIMA algorithm. In conclusion, with algorithm analysis and the comparisons of these four algorithms, we recommend the SARIMA algorithm as a predictive model for the power grid system.

Structural transition in interdependent networks with regular interconnections.

Networks are often made up of several layers that exhibit diverse degrees of interdependencies. An interdependent network consists of a set of graphs G that are interconnected through a weighted interconnection matrix B, where the weight of each intergraph link is a non-negative real number p. Various dynamical processes, such as synchronization, cascading failures in power grids, and diffusion processes, are described by the Laplacian matrix Q characterizing the whole system. For the case in which the multilayer graph is a multiplex, where the number of nodes in each layer is the same and the interconnection matrix B=pI, I being the identity matrix, it has been shown that there exists a structural transition at some critical coupling p^{*}. This transition is such that dynamical processes are separated into two regimes: if p>p^{*}, the network acts as a whole; whereas when p<p^{*}, the network operates as if the graphs encoding the layers were isolated. In this paper, we extend and generalize the structural transition threshold p^{*} to a regular interconnection matrix B (constant row and column sum). Specifically, we provide upper and lower bounds for the transition threshold p^{*} in interdependent networks with a regular interconnection matrix B and derive the exact transition threshold for special scenarios using the formalism of quotient graphs. Additionally, we discuss the physical meaning of the transition threshold p^{*} in terms of the minimum cut and show, through a counterexample, that the structural transition does not always exist. Our results are one step forward on the characterization of more realistic multilayer networks and might be relevant for systems that deviate from the topological constraints imposed by multiplex networks.