Cascading Failures In Power Systems Research Articles

Hierarchical Blocking Control for Mitigating Cascading Failures in Power Systems with Wind Power Integration

The increasing uncertainty of wind power brings greater challenges to the control for mitigation of cascading failures. In order to minimize the risk of cascading failures in large-scale wind power systems at a lower economic cost, a multi-stage blocking control model is proposed based on sensitivity analysis. Firstly, the propagation mechanism of cascading failures in power systems with wind power integration is analyzed, and the propagation path of such failures is predicted. Subsequently, sensitive lines that are prone to failure are identified using the power sensitivity matrix, taking into account the effects of blocking control on the propagation path. By constraining the power flow of these sensitive lines, a multi-stage blocking control model for the predicted cascading failure path is proposed with the objective of minimizing the control cost and cascading failure probability. Based on probabilistic optimal power flow calculations, the constraints related to wind power uncertainty are transformed into opportunity constraints. To validate the effectiveness of the proposed model, the IEEE 39-node system is used as an example, and the results show that the obtained control method is able to balance economy and safety. In addition, the control costs for the same initial failure are higher as the wind power penetration rates and confidence levels increase.

A Method for Variance-Based Sensitivity Analysis of Cascading Failures

Cascading failures of relay operations in power systems are inherently linked with the propagation of wide-area power system blackouts. In this paper, we consider a power system cascading failure as an indicator matrix encoding: what power system relays operated within a cascading failure inherently capturing the component and the sequence of tripping events. We propose that this matrix may then be used with extended forms of variance-based sensitivity estimators to quantitatively rank how sensitive observed power system cascading failures are to power system variables, considering overall system cascading failures as well as cascading failures grouped by network area and relay types. We demonstrate our proposed method by investigating the sensitivity of cascading failures to relay parameters, system conditions, and fault location using a version of the IEEE 39 bus model modified to include protection relays, wind farms, and tap-changing transformers. Input power system variables included: system operational scenario, disturbance location, relay parameters or thresholds. The Case Studies' results confirm the method's utility by successfully generating relative rankings of input variables' importance with respect to cascading failure propagation. The results also show cascading failures' sensitivity to input variables to be high due to non-linear relationships between input variables and cascading failures.

Real‐time risk assessment of cascading failure in power system with high proportion of renewable energy based on fault graph chains

AbstractThe access of a high proportion of renewable energies has deepened the randomness and complexity of cascading failures (CFs) in power systems. In this regard, a real‐time risk assessment method for CFs in power systems with high proportion of new energy is proposed. First, combined with historical statistical data and relevant national standards, a CF simulation model that considers the off‐grid protection action of renewable energy units in the event of a power grid fault is proposed. The model is based on the continuous steady‐state power flow model, which simulates the spread of CFs via continuous power flow calculations. Second, via introducing the concept of a fault graph chain, the electrical and topological characteristics of the continuous dynamic of the power system in the process of CFs can be described. Then, through continuous CF simulation and replay buffer, a data‐driven method is used to calculate the CF risk index corresponding to the fault graph. Finally, a cascaded graph neural network is employed to fit the nonlinear mapping relationship between fault graphs and CF risk indicators. The simulation results in the IEEE 39‐bus system show that the proposed method can accurately and real‐time evaluate the risk of CFs.

A Discrete-Event System Approach for Modeling and Mitigating Power System Cascading Failures

A power system cascading failure can propagate through sequential tripping of components in the network. As a result, a complete or partial shutdown may occur. In this article, we develop a new systematic approach to identify and prevent cascading failures in power systems using supervisory control of discrete-event systems (DESs). We build the DES model for a power system in a modular fashion by first modeling its components as (small) automata and then combining these automata using parallel composition. To overcome state explosion, we use online lookahead control that can significantly reduce the number of states to be considered. Since some events such as line trips cannot be disabled but can be preempted by forcing some forcible events such as load shedding, we extend supervisory control of DES to include forcible events. This extension allows us to control power systems effectively. The proposed control is implemented in an implementation platform that we build in MATLAB. The platform uses MATPOWER to simulate a power system and then control it using the proposed DES controller. Simulation studies are carried out for IEEE 6-, 30-, and 118-bus systems. The results verify the effectiveness of the proposed approach.

Risk assessment of cascading failures in power systems with increasing wind penetration

The ever-growing penetration of renewable based generation is leading to significant increases in the risk of cascading failures in low-inertia, and interconnection-rich, power systems. This paper proposes a framework for quantifying the risk of cascading failures in renewable-rich power systems with fast frequency response (FFR) services. This is achieved by developing a novel time-based dynamic model of cascading failures to capture the transient behaviours of the system response and quantify the risk of blackout. This dynamic cascading failure model is complemented by unit commitment for representative dispatch and includes the provision of FFR. The proposed framework is illustrated using a modified version of the Illinois 200-bus synthetic system. As key outputs, the results emphasize the importance of accurate modelling of ancillary services associated with frequency regulation in cascading failure analysis, and indicate that increased wind penetration can lead to a higher probability of power outages. Adopting FFR services can help to mitigate the cascading risk but may introduce rotor angle instability issues.

Cyberattacks with limited network information leading to transmission line overflow in cyber–physical power systems

Recent cyberattacks on the U.S. power grid revealed how vulnerable smart grids are against cyberattacks. Consequently, it is critical to study cyberattacks that can lead to cascading failures in power systems and develop models to realistically emulate attack scenarios that can bypass existing bad data detection algorithms. In this paper, a mixed-integer linear programming model is developed to accurately model load redistribution attacks via false data injections (FDIs) that are targeted to overflow multiple transmission lines while the attackers have limited access to network topology and information. Compared to the existing research on cyberattacks on transmission line overflow, the proposed model considers that the attackers might have limited access to all measurements and can only target a set of measurements in a region (area) instead of full access to the network information. Numerical results on IEEE 118-bus benchmark revealed the effectiveness of the proposed modeling in overflowing up to four transmission lines with incomplete network information and FDIs limited to 40% of nominal loads.

Searching for Critical Power System Cascading Failures With Graph Convolutional Network

Power system cascading failures become more time variant and complex because of the increasing network interconnection and higher renewable energy penetration. High computational cost is the main obstacle for a more frequent online cascading failure search, which is essential to improve system security. We propose a more efficient search framework with the aid of a graph convolutional network (GCN) to identify as many critical cascading failures as possible with limited attempts. The complex mechanism of cascading failures can be well captured by training a GCN offline. Subsequently, the search for critical cascading failures can be significantly accelerated with the aid of the trained GCN model. We further enable the interpretability of the GCN model by a layerwise relevance propagation algorithm. The proposed method is tested on both the IEEE RTS-79 test system and China's Henan Province power system. The results show that the GCN-guided method can not only accelerate the search of critical cascading failures, but also reveal the reasons for predicting the potential cascading failures.

Identifying disruptive contingencies for catastrophic cascading failures in power systems

Due to the evolving nature and the complicated coupling relationship of power system components, it has been a great challenge to identify the disruptive contingencies that can trigger cascading blackouts. This paper aims to develop a generic approach for identifying the initial disruptive contingencies that can result in the catastrophic cascading failures of power systems. The problem of contingency identification is formulated in the mathematical framework of hybrid differential-algebraic system, and it can be solved by the Jacobian-Free Newton-Krylov method, which allows to circumvent the Jacobian matrix and relieve the computational burden. Moreover, an efficient numerical algorithm is developed to search for the disruptive contingencies that lead to catastrophic cascading failures with the guaranteed convergence accuracy in theory. Finally, case studies are presented to demonstrate the efficacy of the proposed identification approach on the IEEE test systems by using different cascade models.

Community-Based Link-Addition Strategies for Mitigating Cascading Failures in Modern Power Systems

The propagation of cascading failures of modern power systems is mainly constrained by the network topology and system parameter. In order to alleviate the cascading failure impacts, it is necessary to adjust the original network topology considering the geographical factors, construction costs and requirements of engineering practice. Based on the complex network theory, the power system is modeled as a directed graph. The graph is divided into communities based on the Fast–Newman algorithm, where each community contains at least one generator node. Combined with the islanding characteristics and the node vulnerability, three low-degree-node-based link-addition strategies are proposed to optimize the original topology. A new evaluation index combining with the attack difficulty and the island ratio is proposed to measure the impacts on the network under sequential attacks. From the analysis of the experimental results of three attack scenarios, this study adopts the proposed strategies to enhance the network connectivity and improve the robustness to some extent. It is therefore helpful to guide the power system cascading failure mitigation strategies and network optimization planning.

An Optimal Control Approach to Identify the Worst-Case Cascading Failures in Power Systems

Cascading failures in power systems normally occur as a result of initial disturbances or faults on electrical elements, closely followed by situational awareness errors of human operators. It remains a great challenge to systematically trace the source of cascading failures in power systems. In this paper, we develop a mathematical model to describe the cascading outages of transmission lines in power networks. In particular, the direct current power-flow equation is employed to calculate the power flow on the branches. By regarding the disturbances on branches as the control inputs, the problem of identifying the initial disruptive disturbances is formulated with optimal control theory, which provides a systematic approach to explore the most disruptive disturbances that give rise to changes of branch admittance in addition to direct branch outages. Moreover, an iterative search algorithm is proposed to look for the optimal solution leading to the worst-case cascading failures. Theoretical analysis guarantees the asymptotic convergence of the iterative search algorithm. Finally, numerical simulations are carried out on the IEEE test systems to validate the proposed approach.

Identifying critical risks of cascading failures in power systems

Potential critical risks of cascading failures in power systems can be identified by exposing those critical electrical elements on which certain initial disturbances may cause maximum disruption to power transmission networks. In this work, we investigate cascading failures in power systems described by the direct current (DC) power flow equations, while initial disturbances take the form of altering admittance of elements. The disruption is quantified with the remaining transmission power at the end of cascading process. In particular, identifying the critical elements and the corresponding initial disturbances causing the worst-case cascading blackout is formulated as a dynamic optimization problem (DOP) in the framework of optimal control theory, where the entire propagation process of cascading failures is put under consideration. An Identifying Critical Risk Algorithm (ICRA) based on the maximum principle is proposed to solve the DOP. Simulation results on the IEEE 9-Bus and the IEEE 14-Bus test systems are presented to demonstrate the effectiveness of the algorithm.

A complex network theory analytical approach to power system cascading failure-From a cyber-physical perspective.

The modern electric power grid is evolving rapidly into such a state that distributed controllers and two-way energy and information flow are replacing the traditional paradigm of electricity distribution and energy management. Therefore, a power grid coupled with a communication network is playing a pivotal role in establishing modern electric power systems. Previous cascading failure analysis in power systems focused more on the physical network, while falling short of investigations on the coupling effect of interdependency of the integrated electricity and communication networks, i.e., cyber-physical power systems. To address such a pressing issue, this study introduces a novel stochastic cascading failure model, considering the interdependency between the cyber network and power network. A multiagent system and a novel protection relay model are incorporated into the proposed model. To apply the proposed analytical method, a test power system, the IEEE 68-bs power system, is used to study the impacts of a range of interdependencies and cyber network topological structures on the cascading failure. Simulation results show the necessity and effects of consideration of cyber communication network when investigating power system cascading failures. The study also provides useful information on robustness and vulnerability of a particular power grid, given different communication topologies and interdependencies.

Over-Voltage Suppression Methods for the MMC-VSC-HVDC Wind Farm Integration System

In this brief, we study the cascading failure mitigation methods in the system where the remote large-scale wind farm is integrated to the bulk ac power system through high-voltage direct current (HVDC) transmission based on voltage source converter (VSC) in modular multilevel converter (MMC) topology. We first analyze the over-voltage formation mechanism in the MMC-VSC-HVDC wind farm integration system after the short-circuit fault of an ac feed line, and find that the saturation of the integers in the PI controller are the main causes. Based on the analytical conclusions derived, we propose two improvement control strategies implemented in the MMC controller to suppress the over-voltage. We do simulations on a test case with PSCAD. Simulation results verify the efficacy of the proposed methods in the suppression of over-voltage as well as the prevention of cascading failure in power systems.

Failure Localization in Power Systems via Tree Partitions

Cascading failures in power systems propagate non-locally, making the control and mitigation of outages extremely hard. In this work, we use the emerging concept of the tree partition of transmission networks to provide an analytical characterization of line failure localizability in transmission systems. Our results rigorously formalize the well-known intuition that failures cannot cross bridges, and reveal a finer-grained concept that encodes more precise information on failure propagation within tree-partition regions. Specifically, when a non-bridge line is tripped, the impact of this failure only propagates within components of the tree partition defined by the bridges. In contrast, when a bridge line is tripped, the impact of this failure propagates globally across the network, affecting the power flow on all remaining lines. This characterization suggests that it is possible to improve the system robustness by temporarily switching off certain transmission lines, so as to create more, smaller components in the tree partition; thus spatially localizing line failures and making the grid less vulnerable to large outages.

Failure Localization in Power Systems via Tree Partitions

Cascading failures in power systems propagate non-locally, making the control and mitigation of outages extremely hard. In this work, we use the emerging concept of the tree partition of transmission networks to provide an analytical characterization of line failure localizability in transmission systems. Our results rigorously establish the well perceived intuition in power community that failures cannot cross bridges, and reveal a finer-grained concept that encodes more precise information on failure propagations within tree-partition regions. Specifically, when a non-bridge line is tripped, the impact of this failure only propagates within well-defined components, which we refer to as cells, of the tree partition defined by the bridges. In contrast, when a bridge line is tripped, the impact of this failure propagates globally across the network, affecting the power flow on all remaining transmission lines. This characterization suggests that it is possible to improve the system robustness by temporarily switching off certain transmission lines, so as to create more, smaller components in the tree partition; thus spatially localizing line failures and making the grid less vulnerable to large-scale outages. We illustrate this approach using the IEEE 118-bus test system and demonstrate that switching off a negligible portion of transmission lines allows the impact of line failures to be significantly more localized without substantial changes in line congestion.

Quantify the Impact of Line Capacity Temporary Expansion on Blackout Risk by the State-Failure–Network Method

The blackout risks of cascading failures in power systems are notably associated with the failures of transmission lines. Line capacity temporary expansion can reduce blackout risk by decreasing the line failures due to the overloads during the cascading failures. To efficiently quantify the impact of line capacity temporary expansion on blackout risks, we propose a data based state-failure–network method in this paper. The state-failure network, which is formed by the cascading failure data generated by cascading failure simulations, contains the empirical probabilities that correspond to the failure probabilities of lines. Since implementing line capacity temporary expansion to the system can change the failure probabilities of lines and reduce the blackout risk, the empirical probabilities offer the link between line capacity temporary expansion and state-failure network. By updating the values in the state-failure network with changed empirical probabilities, the blackout risk after line capacity temporary expansion is implemented can be efficiently worked out by state-failure network. Thus, the impact of line capacity temporary expansion on blackout risk is quantified by comparing the newly calculated blackout risk with the risk before the line capacity temporary expansion is implemented. The advantage of the proposed method lies in the high accuracy and efficiency of quantifying the impacts of any line capacity temporary expansion schemes once the state-failure network is formed. Case studies verify the accuracy and efficiency of the proposed method.

Robust Testing of Cascading Failure Mitigations Based on Power Dispatch and Quick-Start Storage

We present a formal robust testing method for power system cascading failure mitigations. The approach is model-based, using simulated trajectories of the system and proving that uncertainties, e.g., in the initial states or disturbances, do not perturb the trajectories beyond a robust neighborhood around them. We model power systems as hybrid systems with locations representing different swing dynamics and relay dynamics. We present implementations of our robust testing approach in a three-machine system model from the 2003 Italian Blackout and the IEEE 39-bus system model. We apply the robust testing method in two scenarios for averting the cascading failures: 1) robust testing of safety for various generator mechanical power dispatch schedules and 2) robust testing of safety for postfault remedial actions based on quick-start storage.

Electrical and topological drivers of the cascading failure dynamics in power transmission networks

To systematically study key factors affecting cascading failures in power systems, this paper advances algorithms for generating synthetic power grids with realistic topological and electrical features, while computationally quantifying how such factors influence system performance probabilistically. Key parameters affecting line outages and power losses during cascading failures include line redundancy, load/generator layout and re-dispatch strategies. Our study combines a synthetic power grid generator with a direct current (DC) cascading failure simulator. The impact of each of the factors and their interactions unravel useful insights for interventions aimed at reducing the probabilities of large blackouts on existing and future power systems. Moreover, conclusions drawn from a spectrum of different power grid topologies and electrical configurations offer more generality than typically attained when studying specific test cases. Line redundancy and distributed generation appear as the most efficacious parameters for reducing the probabilities of large power losses and multiple line overloads, although the effect decays with network density. Also, re-dispatch strategies are critical on the distribution of the cascading failure size in terms of line failures. These and related results provide the basis for probabilistic analyses and future design of evolving power transmission systems under uncertainty.

Machine Learning Based on Bayes Networks to Predict the Cascading Failure Propagation

Considering the engineering characteristics of power systems and the concept of machine learning, a model named “ITEPV” was proposed in this paper to investigate the mechanism of cascading failures in power systems. This model tries to simulate a large number of possible cascading failure chains as “experience”, and then to predict the cascading failure propagation with the highest possibility obtained from the “experience”. In order to get the prediction result, the uncertainty of loads and generations is considered to generate numerous random operating conditions, and then implementing “ $N-1$ ” for each operating condition to obtain the “experience”. Based on the “experience”, a Bayes network can be established to predict the cascading failure propagation. The “ITEPV” model was tested on the IEEE Reliability Test System-1996 (RTS-96), and the results were validated by employing different sample sizes of random operating conditions. From this paper, it can be concluded that employing machine learning into electrical engineering not only simplifies the complicated issue but also makes the results more accurate.

Modeling the Dynamics of Cascading Failures in Power Systems

In this paper, we use a circuit-based power flow model to study the cascading failure propagation process, and combine it with a stochastic model to describe the uncertain failure time instants, producing a model that gives a complete dynamic profile of the cascading failure propagation beginning from a dysfunctioned component and developing eventually to a large-scale blackout. The sequence of failures is determined by voltage and current 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. 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 contain all salient features displayed in historical blackout data. The proposed model thus offers predictive information about occurrences of large-scale blackouts. We further plot cumulative distribution of the blackout size to assess the overall system’s robustness. We show that heavier loads increase the likelihood of large blackouts and that small-world network structure would make cascading failure propagate more widely and rapidly than a regular network structure.