Power System Failure Research Articles

Recovery Resiliency Characteristics of Interdependent Critical Infrastructures in Disaster-Prone Areas

When Hurricane Maria struck the island of Puerto Rico in September, 2017, it devastated the island’s critical infrastructures, including the well-documented total loss of electric power systems. The strong interdependencies or associations among critical infrastructures in modern society meant that the failure of power systems propagated to and exacerbated the failure of other infrastructure systems. Moreover, these associations impact systems recovery just as they impact system failure. This study is a follow-up of previous research by the first author on Hurricane Maria. In this research authors extracted and quantified the recovery associations of Hurricane Fiona (September 2022) made landfall in Puerto Rico and inflicted considerable damage to its critical infrastructures. The recovery efforts following the disaster provided an opportunity to follow up on the previous research and examine the recovery associations. Significant money and efforts have gone into upgrading the infrastructures of Puerto Rico to make them more resilient to natural disasters such as hurricanes or tropical storms following Hurricane Maria. This paper explores the new recovery resiliency characteristics of Puerto Rico’s critical infrastructure systems (CISs) that the recovery efforts following Hurricane Fiona illustrate. This research shows that the power systems and other CISs of Puerto Rico are much more resilient when compared to their state of resiliency in 2017. Moreover, examining the recovery interdependencies reveals that some of the CISs are strongly dependent on power systems recovery. Outcomes of this study suggest that CIS relationships based on recovery data from Puerto Rico, are transferable to similar disaster-prone areas such as the Caribbean islands or other island nations, as they have similar characteristics and challenges.

Probabilistic safety assessment of off-site power system under typhoon considering failure correlation between transmission towers

High-intensity typhoons can cause failure to the structures, systems, and components of nuclear power plants (NPPs) and off-site power systems. Instances have occurred where failure of the off-site power system caused by typhoons has affected NPPs. The off-site power system has been in cases where a typhoon failed multiple transmission towers. In Korea, transmission towers have similar design criteria and material properties and experience similar wind characteristics, leading to a correlation in their failure probabilities. Assuming independent or totally dependent failure probabilities among the transmission towers is unrealistic. For a realistic PSA of the off-site power system due to typhoon-induced high winds, it is necessary to consider an appropriate failure correlation. This study performed a PSA of an off-site power system due to typhoon-induced high winds, considering the failure correlation between transmission towers. The failure correlation between transmission towers based on distance was calculated. The wind fragility of the off-site power system was convolved with the typhoon-induced high-wind hazard at the Kori NPP to calculate the probability of the NPP losing its power supply. Thus, applying the failure correlation of typhoon-induced high winds to the PSA of the off-site power system is considered realistic.

Fault Diagnosis of Power Components with Reliability Assessment in Extraterrestrial Microgrids

This research investigates the possible failures caused by aging and other environmental and external factors that could significantly impact the performance of extraterrestrial power systems. Additionally, it presents a reliability assessment model for the space microgrid based on fault tree analysis (FTA). The reliability assessment model developed in this paper represents a tool that can be used by engineers to harden the system design for operational and economic benefits. To improve the reliability of the system, this work provides a broad review of the different fault detection and diagnosis (FDD) algorithms used for power microgrids and space applications. Using data sets from the Habitat Simulator developed through the NASA-funded Resilient Extraterrestrial Habitat Institute, this paper compares the applicability and accuracy of the different FDD methods. The primary FDD approach proposed and assessed in this work is based on the Markov reliability model. It predicts and detects future faults in the space microgrids by using past data samples and categorizing them into different classes. Data-driven-based models such as artificial neural networks are also investigated, tested, and evaluated using simulation data sets. According to the simulation results and the broad FDD algorithm comparison, this study provides the crew or maintenance engineers with a clear methodology to detect and localize power system failures.

Three-stage resilience enhancement via optimal dispatch and reconfiguration for a microgrid

Extreme weather causes an increase in power system failure. Previous studies on system resilience have often overlooked the user-side of a microgrid. This study proposes composite resilience indices (RI) based on the power supply of a large-scale manufacturing campus microgrid to quantify its ability to withstand extreme events. The proposed RI consider load priority, minimum supply load, total energy supplied, and the performance recovery-to-degradation slope ratio in an islanding microgrid. Accordingly, this study presents a three-stage resilience optimal dispatch and reconfiguration strategy, including energy-level scheduling, grid-level reconfiguration, and dynamic-level verification. A multi-objective optimization approach is used for energy scheduling, followed by system reconfiguration via DIgSILENT system modeling to meet the grid code and maximize load supply. Value at Risk methods are used to verify microgrid stability and load shedding requirements, supported by the virtual synchronous generator control of the energy storage system. The test results from a practical large-scale manufacturing campus microgrid validate the proposed approaches for enhancing system resilience considering load values.

The effects of load demand uncertainty on cascading overload failures in power systems

The effects of load demand uncertainty on cascading overload failures in power systems

Powered–coasting–powered multi-phase mission reconfiguration method for launch vehicles under power system failures

For the final stage of a launch vehicle with powered–coasting–powered multi-phase flight, this paper investigates the joint reconfiguration method to determine subsequent flight trajectory and degraded orbit after power system failure in the first powered phase. While power system failures due to mass–flow–rate drop have been thoroughly studied in the existing literature, this paper focuses on the more challenging specific impulse drop failures, which result in loss of total launcher energy. This paper aims to design a fault-tolerant guidance method with onboard potential and to analyze the survivability and characteristics of the launcher in the event of such a failure. In the problem modeling, the strong nonlinearity of the orbital elements as terminal constraints is eliminated by analyzing the orbital properties in the orbital coordinate system to establish the primal trajectory optimization problem. For this multi-phase trajectory optimization problem, a joint optimization algorithm for the degradation orbit and the flight trajectory, based on convex optimization and Radau pseudospectral method, is designed to achieve autonomous mission reconfiguration. The algorithm introduces the relaxation–penalization idea, where the terminal constraints are relaxed and penalized in the performance index, allowing the algorithm to adaptively form an optimal degradation circular or elliptical orbit based on the remaining capacity of the launch vehicle. Simulation experiments demonstrate the effectiveness and superiority of the mission reconfiguration algorithm. Finally, the paper presents the first analysis of the optimal allocation of flight time between the first powered phase, the coasting phase, and the second powered phase in a propellant depletion scenario following the launcher power system failure.

Active power compensation circuit for resonance mitigation and harmonic reduction in microgrid system

The nature and behavior of capacitors, transformers, inductors, active compensators, and non-linear loads can produce power resonance. Unfortunately, the presence of a resonance phenomenon can have a negative impact on system stability and lead to catastrophic power system failures. Therefore, even when using modern or conventional techniques to enhance total harmonic distortion (THD) or improve input power factor (IPF), it is necessary to avoid resonance. An active power compensation circuit (APCC) is proposed and designed to function with two categories of linear/non-linear loads. The APCC has been implemented and regulated using an adjusted pulse width modulation technique. The aim of the suggested APCC is to minimize AC side distortions, improve the IPF, and mitigate harmonics resonance at the same time. The simulation results demonstrate that the proposed APCC investigates the aim function of this study by absorbing harmonics, correcting IPF, and eliminating resonance problems under both transient and steady-state operating conditions. The supply voltage and current THD values for the first power circuit type are reduced by 96.7 % and 96.3 %, respectively, at α=30°. Meanwhile, for the second power circuit, the THD is reduced by 91.92 % and 90.4 %. Also, the IPF changed for the first and second power circuits from 0.72 and 0.86 to almost unity. These results demonstrated the effective performance of the APCC circuit and controller in reducing power harmonics, eliminating power resonance, and modifying power factors.

The Impact of Inverter-Based Resources (IBRs) on Cascading Failures in Power Systems

The Impact of Inverter-Based Resources (IBRs) on Cascading Failures in Power Systems

Risk Assessment and Analysis of New Power System Operation Under Extreme Disasters

Abstract In recent years, new power systems have been developing continuously, and the operational risk assessment of new power systems under extreme disaster conditions has attracted much attention. This paper uses Latin hypercube sampling technology to establish a power system failure model under extreme disasters and proposes a new power system risk assessment method that applies the power system power flow model to establish a comprehensive system risk index. In addition, taking the power grid of a certain province in eastern China as an example, a case analysis is conducted on the IEEE39 node system to verify the effectiveness of the method.

Research on cascading effect analysis of civil aircraft electrical power system failures

Abstract Cascade effect analysis is a qualitative analytical method recommended by SAE ARP 4761A [1] for evaluating complex integrated civil aircraft systems. The Electrical Power System (EPS) is one of the critical onboard systems. It can provide electrical power to the avionics system, flight control system, and other critical systems on the aircraft, and it has a wide range of failure effects. The consequences of EPS failure effects are difficult to determine. This paper applies the cascading effect analysis method to carry out the qualitative safety assessment of the EPS on the aircraft and finally confirms that the architectural design of the EPS met the safety design requirements. It also gives the engineering method (such as test, MBSA) suggestion to further check and verify the correctness and completeness of the theoretical analysis of CEA.

Optimizing the location and configuration of disaster resilience hubs under transportation and electric power network failures

Natural disasters often result in failures of transportation network components and blackouts that imperil the wellbeing of vulnerable populations. In response to these events, resilience hubs have been proposed as a pre-disaster planning strategy to improve access to critical services. This paper introduces an optimization-based approach to locate and configure electric power-generating resilience hubs considering the possibility of failures in transportation and electric power systems. The model’s objective is to identify hub locations and configurations that maximize transportation accessibility to the hubs and maximize the satisfaction of basic energy needs through hub-generated electric power. Besides a budget constraint, the model accounts for limits on the levels of hub energy generation vis-à-vis community energy demands, and on the transportation network distance of communities to hubs. Three heuristics are presented for the proposed planning problem. The first heuristic is a genetic algorithm (GA) with problem-specific solution generation procedures. The other two heuristics implement greedy search techniques. Numerical experiments were conducted, using data from rural Puerto Rico, to illustrate the application of the proposed model and heuristics, and examine their performance. In the numerical experiments, the GA heuristic found better solutions than the greedy heuristics. Additionally, design solutions consisting of spatially dispersed hubs with low energy generation capacity were better than solutions with spatially concentrated high-capacity hubs. Lastly, across a wide range of hub demand scenarios, only a small number of candidate hub locations consistently ranked among the best locations for establishing a hub.

Integrated microcavity electric field sensors using Pound-Drever-Hall detection

Discerning weak electric fields has important implications for cosmology, quantum technology, and identifying power system failures. Photonic integration of electric field sensors is highly desired for practical considerations and offers opportunities to improve performance by enhancing microwave and lightwave interactions. Here, we demonstrate a high-Q microcavity electric field sensor (MEFS) by leveraging the silicon chip-based thin film lithium niobate photonic integrated circuits. Using the Pound-Drever-Hall detection scheme, our MEFS achieves a detection sensitivity of 5.2 μV/(mHz\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{{{{{{{{\\rm{Hz}}}}}}}}}$$\\end{document}), which surpasses previous lithium niobate electro-optical electric field sensors by nearly two orders of magnitude, and is comparable to atom-based quantum sensing approaches. Furthermore, our MEFS has a bandwidth that can be up to three orders of magnitude broader than quantum sensing approaches and measures fast electric field amplitude and phase variations in real-time. The ultra-sensitive MEFSs represent a significant step towards building electric field sensing networks and broaden the application spectrum of integrated microcavities.

Dynamics of cascading failure in cyber-physical power systems from cyber attack

Abstract Through communication network, cyber-physical power systems can effectively monitor and control physical power grid, but this also increases the danger to systems from cyber attack. In this paper, we study the cascading failure triggered by cyber attack, which infects cyber nodes through malware and to endanger physical nodes through coupling links. First, the flow and topology models for cyber-physical power systems are detailed. In communication network, we analyze the mechanism of diffusion and infection among cyber nodes, and differentiate cyber nodes into three types, corresponding to different state of cyber nodes before and after cyber attack. And in physical power grid, the types of physical nodes are also classified. For different state of cyber nodes, we detail their impact on physical nodes and power flows in physical power grid. Simulation analyzes the robustness of systems and dynamics of cascading failure in different attack scenes and topology structure.

Weather-related fragility modelling of critical infrastructure: a power and railway case study

Climate change has led to more frequent and severe extreme weather events, which impact critical infrastructure networks such as railway and power systems. Although infrastructure networks are interdependent, the analysis to understand the impact of weather events on infrastructure systems is usually performed in sector-specific silos. A methodology to examine how the same weather events affect different infrastructure sectors is presented, in order to understand cross-sectoral impact of extreme weather for interconnected regional infrastructure. Fragility modelling was used to examine the impact of temperature and rainfall on power and rail system failures using the West Midlands (in the UK) as a case study. The results demonstrated that the impact of temperature was broadly consistent across both infrastructure networks, showing less impact until specific upper and lower thresholds are passed; these thresholds were found to be similar for the different infrastructure networks evaluated, but railway infrastructure was found to be impacted more by lower temperatures. A growing correlation between the number of faults on power and railway systems was also found for both rainfall and temperature, indicating the value in coordinating preparation and planning efforts. For infrastructure operators and owners, regional resilience forums and other decision makers, this study provides an approach to assess the regional impact of extreme weather across multiple infrastructure sectors. The results give useful insights to inform the allocation of resources in response to extreme weather events.

An Open-Circuit Fault Diagnosis Method for LLC Converters

In electrified transportation systems, power system failures can lead to greater disasters. Therefore, the reliability of converters in transportation systems has been a concern. Fault-tolerant techniques are widely applied to ensure that converters can continue to supply loads under fault conditions. Fault diagnosis as a prerequisite for fault tolerance has also become a research hotspot. This paper proposes a fast method for fault diagnosis of high-frequency LLC converters. The proposed fault diagnosis method is based on the observation of the voltage across the resonant capacitor to determine and locate the faulty power switch, providing a basis for fault tolerance. This diagnosis method requires a voltage sensor, which is also necessary for some control methods. When applying these control methods, the proposed fault diagnosis method can be used without additional sensors, beneficial for cost reduction. A full-bridge LLC converter controlled by a digital signal processor was used as an experimental platform to verify the effectiveness and speed of the proposed diagnostic method. The results show that the proposed fault diagnosis method can achieve the fast diagnosis of high-frequency LLC converters in a short time and with only minimal computational resources.

Verification tests of a novel ferroresonance detection technique

Ferroresonance is a complex electromagnetic phenomenon that affects voltage transformers. The appearance of the ferroresonant phenomenon provokes important oscillations that are responsible for serious thermal and dielectric overstresses, which may lead to catastrophic failures in the electrical power system. This paper analyzes the behaviour of a novel ferroresonance detection technique, based on artificial neural networks, through the results obtained in several verification tests conducted in a real voltage transformer.

Identification of weather patterns and transitions likely to cause power outages in the United Kingdom

Lightning strikes, snow, and wind are common causes of power system failures. Their frequency of occurrence varies depending on weather patterns and seasons. Here, we analyse the occurrence of thirty pre-defined daily weather patterns along with nearly 70,000 power system failures in the United Kingdom between 2010 and 2019 to investigate the predictability of weather-induced power outages. We identify high-risk weather patterns and pattern transitions for all seasons. Winter weather patterns characterized by high wind speeds and high precipitation volumes are responsible for many instances of power outages caused by wind, gale, and lightning strikes. Weather patterns with moderate to high snowfall are often linked to power outages caused by snow and ice. Because high-risk weather patterns can be forecast reliably with valuable lead time, clear links between specific patterns and transitions can significantly improve the preparedness of the United Kingdom and broader European distribution network operators for adverse weather conditions.

A cascading failure model of cyber-coupled power system considering virus propagation

Cyber attacks from the information layer can have a serious impact on the operation of the cyber-coupled power system. In this paper, a practical cascading failure model used for investigating the impact of virus propagation on the cascading failure of cyber-coupled power system is proposed. The model takes into account the actual functionality of cyber-coupled system and the dynamic virus propagation in cyber network, and describes the effects of cyber node failure caused by virus infection and power flow overload on the cascading failure. Moreover, the impact of defense upgrade on the virus propagation is considered. Centralized control based on dispatching center and local regulation based on static power-frequency characteristic are introduced to describe the actual monitoring function of cyber system on the power grid. Then, the effects of defense upgrade strategy and modular community structure on the failure evolution are analyzed. The results indicate that the spread of virus destroys the monitoring function of cyber network on the power network and leads to more serious failure results. The effectiveness of defense upgrade strategies is related to the upgrade probability of information stations. In addition, the stronger community structure of cyber network can improve the robustness of cyber-coupled power system.

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.

The risk of large blackout failures in power systems

The risk of large blackout failures in power systems