Showing papers on "Blackout published in 2015"

Support-Vector-Machine-Based Proactive Cascade Prediction in Smart Grid Using Probabilistic Framework

Abstract: The worldwide major blackout events of power network are highlighting the need for technology upgradation in traditional grid. One of the major upgradations required is in the area of early warning generation in case of any grid disturbances such as line contingency leading to cascade failure. This paper proposes a proactive blackout prediction model for a smart grid early warning system. The proposed model evaluates system performance probabilistically, in steady state and under dynamical (line contingency) state, and prepares a historical database for normal and cascade failure states. A support vector machine (SVM) has been trained with this historical database and is used to predict blackout events in advance. The key contribution of this paper is to capture the essence of the cascading failure using probabilistic framework and integration of SVM machine learning tool to build a prediction rule, which would be able to predict the scenarios of the blackout as early as possible. The proposed model is validated using the IEEE 30-bus test-bed system. Proactive prediction of cascade failure using the proposed model may help in realizing the grid resilience feature of smart grid.

Impact of clustering microgrids on their stability and resilience during blackouts

Abstract: In this paper, the impact of clustering multiple microgrids during blackouts, on their stability and supply availability, will be investigated. Microgrids have the capability of satisfying their emergency loads during blackouts. However, distributed energy resources (DERs)-dominated microgrids are affected by the uncertainty of their input energy supply, e.g. impact of solar irradiance on photovoltaic (PV) output. Moreover, an individual islanded microgrid is prone to instability issues due to large sudden load/generation changes. In order to increase the supply security, and enhance system stability, we propose to use the existing distribution grid infrastructure, if applicable, during blackouts to form microgrid clusters. The paper discusses the required control hierarchy required to manage the microgrid clusters, and communicate with the Distribution Network Operator (DNO). A case study based on the 13-bus standard distribution feeder, and two microgrid models, is presented. Results show that microgrids clustering helps improve their performance and that the microgrid generator inertia has a direct impact on the stability of the microgrid cluster.

An analysis of previous blackouts in the world: Lessons for China׳s power industry

Abstract: Large-scale blackouts have occurred frequently throughout the world. The blackouts that have occurred in recent years not only seriously affect the lives of local residents, but they also cause substantial economic losses. In particular, as modern society increasingly depends on electricity, power outages occurring at any time may result in devastating consequences. Accordingly, identifying the reasons for blackouts and implementing proper restoration measures are of particular importance. This paper analyses the sphere of influence, the scale of the effects, the restoration measures and the causes of previous worldwide blackouts. Based on the findings, suggestions for the development of China׳s power industry are then offered.

Modeling Radio Communication Blackout and Blackout Mitigation in Hypersonic Vehicles

Abstract: A procedure for the modeling and analysis of radio communication blackout of hypersonic vehicles is presented. The weakly ionized plasma generated around the surface of a hypersonic reentry vehicle is simulated using full Navier–Stokes equations in multispecies single fluid form. A seven-species air chemistry model is used to compute the individual species densities in air including ionization: plasma densities are compared with the experiment. The electromagnetic wave’s interaction with the plasma layer is modeled using multifluid equations for fluid transport and full Maxwell’s equations for the electromagnetic fields. The multifluid solver is verified for a whistler wave propagating through a slab. First principles radio communication blackout over a hypersonic vehicle is demonstrated along with a simple blackout mitigation scheme using a magnetic window.

Risk Assessment in Extreme Events Considering the Reliability of Protection Systems

Abstract: This paper presents a new risk assessment method that is applicable to extreme cases in power systems. This paper analyzes the interactions among protection system components and the power grid in extreme events pertaining to simultaneous faults and cascading failures. The hidden failures of protection systems could exacerbate power system conditions if cascading events tend to follow a path to a blackout. The proposed risk assessment considers detailed reliability models of protection system components including circuit breakers (CBs) and protective relays. The failure probability of a CB is formulated considering its component degradation rate and operation times. The failure model of a protective relay is constructed using the dynamic fault tree. The evolution of cascading failures of power systems in extreme conditions, which deteriorates due to protection system malfunctions, is modeled based on the actual physical system behavior. The effectiveness of the proposed risk assessment method is demonstrated using a modified 9-bus system and the IEEE 68-bus system.

Transmission system repair and restoration

Abstract: This paper studies the use of mathematical programming for the repair and restoration of a transmission system after a significant disruption (e.g., a natural disaster). Such blackouts may last several days and have significant impact on human and economic welfare. The transmission system repair and restoration problem (TSRRP) consists in dispatching crews to repair damaged electrical components in order to minimize the size of the blackout. The TSRRP can be modeled as a large-scale mixed nonlinear, nonconvex program, including both routing components and the nonlinear steady-state power flow equations. To tackle its daunting computational complexity, this paper proposes a 2-stage approach, decoupling the restoration and repair aspects. The first step is a restoration ordering problem, a mixed nonlinear, nonconvex program which is approximated by a mixed integer program. The approximation does not use the traditional DC power flow approximation which is plagued by convergence issues and inoperable dispatches; rather, it uses the recent LPAC approximation that captures reactive power and voltage magnitudes. The second stage is a pickup and repair routing problem which is solved using a constraint-programming model, large neighborhood search, and a randomized adaptive decomposition. Experimental results on benchmarks based on the US electrical infrastructures and state-of-the-art damage scenarios indicate that the 2-stage approach provides significant improvements over the "best practice" in the field.

BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies

Abstract: We propose BlackOut, an approximation algorithm to efficiently train massive recurrent neural network language models (RNNLMs) with million word vocabularies. BlackOut is motivated by using a discriminative loss, and we describe a new sampling strategy which significantly reduces computation while improving stability, sample efficiency, and rate of convergence. One way to understand BlackOut is to view it as an extension of the DropOut strategy to the output layer, wherein we use a discriminative training loss and a weighted sampling scheme. We also establish close connections between BlackOut, importance sampling, and noise contrastive estimation (NCE). Our experiments, on the recently released one billion word language modeling benchmark, demonstrate scalability and accuracy of BlackOut; we outperform the state-of-the art, and achieve the lowest perplexity scores on this dataset. Moreover, unlike other established methods which typically require GPUs or CPU clusters, we show that a carefully implemented version of BlackOut requires only 1-10 days on a single machine to train a RNNLM with a million word vocabulary and billions of parameters on one billion words. Although we describe BlackOut in the context of RNNLM training, it can be used to any networks with large softmax output layers.

Multiagent System-Based Wide-Area Protection and Control Scheme Against Cascading Events

Abstract: In this paper, a multiagent system-based wide-area protection and control scheme is proposed to deal with the long-term voltage instability-induced cascading trips. Based on sensitivity analysis between the relay operation margin and power system state variables, an optimal emergency control strategy is defined to adjust the emergency states timely and prevent the unexpected relay trips. In order to supervise the control process and further minimize the load loss, an agent-based process control is adopted to monitor the states of distributed controllers and adjust the emergency control strategy. A hybrid simulation platform based on LabVIEW and real-time digital simulator is set up to simulate a blackout case in the power system of Eastern Denmark and to demonstrate the effectiveness of the proposed MAS-based protection strategy.

New intelligent controlled islanding scheme in large interconnected power systems

Abstract: This study presents a new intelligent controlled islanding scheme based on wide area measurement systems data to avoid the wide area blackout. Three offline, online and real-time parts are applied to solve three problems including where and when to implement islanding and what to do after separation. New security-based criteria are used to determine the initial stable coherent groups. The boundaries of islands are obtained adaptively considering different operating points by using the weighted time varying graph structure of the network. To reach more stable islands, reactive power is considered by using a self-tuned online fuzzy factor in graph weights. The number of necessary islands with their locations is determined in online part by monitoring the dominant inter-area oscillations between the initial groups (IGs). Then, the network is split into islands with the objective of minimum power flow disruption. To detect the unavoidable islanding cases correctly, a new parallel adaptive neuro-fuzzy inference system (ANFIS) structure is designed. In a parallel structure, for each of two adjacent IGs a distinct ANFIS is also applied to consider variable stability margins between groups. Simulation results confirm that the blackout can be avoided in a large power grid by using the proposed method.

Efficient Splitting Simulation for Blackout Analysis

Abstract: The analysis of severe blackouts has become an essential part of transmission grid planning and operation. This may include evaluation of rare-event probabilities, which can be difficult to estimate. While simulation offers flexibility to model large complex systems, efficiency remains a big concern when estimating very small probabilities. This paper presents an effective simulation technique to evaluate rare-event probabilities associated with cascading blackouts in an electric grid. We test our technique on an IEEE 118-bus electric network and show that it can dramatically improve simulation efficiency. We also demonstrate that the proposed technique can effectively locate vulnerable links. These are links whose failures lead to the highest probabilities of a blackout event.

Alcohol-Induced Blackouts and Other Negative Outcomes During the Transition Out of College

Abstract: Objective:There is considerable debate about the prospective association between alcohol-dependence symptoms and alcohol-related blackouts. The goal of this study was to examine the associations among alcohol-dependence symptoms, blackouts, and social and emotional consequences during the transition out of college.Method:Participants (N = 829; 66% female) were part of a 6-year longitudinal study designed to explore alcohol use and risky behaviors during and after college. Data for these analyses were from Years 4 and 5 of data collection, which most closely corresponded to the transition out of college. Using cross-lagged models, we tested the prospective associations of alcohol-dependence symptoms, blackout frequency, and social and emotional consequences.Results:Alcohol-dependence symptoms in Year 4 predicted increased frequency of blackouts and social and emotional consequences during the subsequent year. Blackouts during Year 4 also significantly predicted increased alcohol-related social and emotiona...

Estimating cascading failure risk with Random Chemistry

Abstract: The potential for cascading failure in power systems adds substantially to overall reliability risk Monte Carlo sampling can be used with a power system model to estimate this impact, but doing so is computationally expensive This paper presents a new approach to estimating the risk of large cascading blackouts triggered by multiple contingencies The method uses a search algorithm (Random Chemistry) to identify blackout-causing contingencies, and then combines the results with outage probabilities to estimate overall risk Comparing this approach with Monte Carlo sampling for two test cases (the IEEE RTS-96 and a 2383 bus model of the Polish grid) suggests that the new approach is at least two orders of magnitude faster than Monte Carlo, without introducing measurable bias Moreover, the approach enables one to compute the sensitivity of overall blackout risk to individual component-failure probabilities in the initiating contingency, allowing one to quickly identify low-cost strategies for reducing risk By computing the sensitivity of risk to individual initial outage probabilities for the Polish system, we found that reducing three line-outage probabilities by 50% would reduce cascading failure risk by 33% Finally, we used the method to estimate changes in risk as a function of load Surprisingly, this calculation suggests that risk can sometimes decrease as load increases

Adaptive Transmission Method for Alleviating the Radio Blackout Problem

Abstract: The radio blackout problem stands as one long obstacle for hypersonic flight and planetary atmosphere reentry. Rather than previous physical mitigation methods aiming to reduce the plasma electron density, this paper proposes a novel method which attempts to communicate at carrier frequency much higher than the plasma cutoff frequency. To overcome the highly dynamic channel characteristics, the reflected wave is used online to estimate the instantaneous channel states and enable adaptive transmission. According to the predicted channel states, the plasma sheath induced phase shift and amplitude attenuation are compensated by baseband modulation and power adaptation, respectively. Numerical simulations are presented and discussed, in order to illustrate the effectiveness of the proposed method.

Neural Network Based Early Warning System for an Emerging Blackout in Smart Grid Power Networks

Abstract: Worldwide power blackouts have attracted great attention of researchers towards early warning techniques for cascading failure in power grid The key issue is how to analyse, predict and control cascading failures in advance and prevent system against emerging blackouts This paper proposes a model which analyse power flow of the grid and predict cascade failure in advance with the integration of Artificial Neural Network (ANN) machine learning tool The Key contribution of this paper is to introduce machine learning concept in early warning system for cascade failure analysis and prediction Integration of power flow analysis with ANN machine learning tool has a potential to make present system more reliable which can prevent the grid against blackouts An IEEE 30 bus test bed system has been modeled in powerworld and used in this paper for preparation of historical blackout data and validation of proposed model The proposed model is a step towards realizing smart grid via intelligent ANN prediction technique

Mitigation of Communication Blackout During Re-Entry Using Static Magnetic Field

Abstract: During re-entry into earth's atmosphere, a spacecraft suffers from loss of communication with the ground control station, known as communication blackout, due to formation of plasma around the re-entry spacecraft. This paper presents the theory and analysis of the communication blackout and its mitigation using static magnetic field method. The interaction between electromagnetic waves and plasma in presence as well as absence of magnetic field is described to determine the effects of plasma sheath on the spacecraft re-entering into the atmosphere. An analysis is done to determine the effectiveness of this mitigation technique for a typical re-entry spacecraft and the strength of magnetic field required to establish the communication link between the re-entry spacecraft and the ground station is obtained.

Challenging Changing Landscapes: Implementing Synchrophasor Technology in Grid Operations in the WECC Region

Abstract: Power grids have become more complex to plan and operate. With grid changes arise new challenges: renewable generation, energy conservation, electric vehicles, energy storage, and load growth. Challenges facing the electric industry today include balancing capacity, reliability, economics, environmental, and other public objectives. Recent cascading outages have demonstrated the challenges faced when operating a system near its limits. These outages over the last few years have had large social and economic impacts. In 2012, an outage in India affected over 620 million people. In 2009, a blackout in Brazil and Paraguay impacted 87 million. In 2011, a power blackout affecting 3 million people in Southern California and Mexico lasted 12 hours and was estimated to have cost over US$100 million in lost revenue. It is a balancing act to meet today?s electricity consumer expectations in a changing landscape.

Microgrid management in emergency scenarios for smart electrical energy usage

Abstract: Power system blackouts harm economic activities and worsen the customers' welfare. Smart grids' self-healing capacity is an important feature for future power systems and it should also include the ability to manage the distributed energy sources to ensure electricity supply for a long time. This is required because the blackout duration is unknown and buck power system blackstart is a complex task. This paper proposes a methodology for microgrid management and control to maximize the duration of electricity supply in emergency situations. In order to accomplish this goal, some management options are considered as intentional load shedding, dispatch of expensive fossil fuel sources, system reconfiguration and so on. The proposed methodology is tested with the IEEE 34 node test-system to investigate its feasibility.

A random forest-based approach for voltage security monitoring in a power system

Abstract: Voltage collapse is a critical problem that impacts power system operational security. Timely and accurate assessment of voltage security is necessary to detect alarm states in order to prevent a large-scale blackout. This paper presents an on-line voltage security assessment scheme using periodically updated random forest-based decision trees. We demonstrated the proposed method on the modified 53-bus IEEE power system. Results are presented and discussed.

Frequency transient analysis of countrywide blackout of Bangladesh Power System on 1st November, 2014

Abstract: In this paper, real time frequency transient analysis with respect to frequency control of Bangladesh Power System (BPS) countrywide blackout on 1st November, 2014 is presented which occurred after HVDC station tripped. The inertia constant of a system can be a significant tool for investigating the frequency stability of a system and the frequency transient of power system is a vital index for power system operation and research. The dynamic frequency characteristic of power system is the basis of many important design works such as the design of under frequency load shedding scheme. Therefore, it is obvious and essential to analyze the dynamic frequency characteristic of BPS before countrywide blackout after disturbance. This paper first analysis real time frequency transient and determine the system inertia. Secondly it shows that maintaining nominal level of inertial reserve in real-time operations with unit commitment which can be crucial to ensure stable, secure and reliable operation of BPS. Beside this under frequency scheme should be revised and controlled. BPS network disintegration along with rate of change of frequency should be ensured for adaptive as well as self-healing of the power system.

Impact of power system blackouts

Abstract: This article consists of a collection of slides from the author's conference presentation: economic impact of a blackout; and power system restoration

A hybrid artificial neural network for voltage security evaluation in a power system

Abstract: A majority of recent large-scale blackouts have been the consequence of instabilities characterized by sudden voltage collapse phenomena. This paper presents a method for voltage instability monitoring in a power system with a hybrid artificial neural network which consist of a multilayer perceptron and the Kohonen neural network. The proposed method has a couple of the following functions: the Kohonen network is used to classify the system operating state; the Kohonen output patterns are used as inputs to train of a multilayer perceptron for identification of alarm states that are dangerous for the system security. The approach is targeting a blackout prevention scheme; given that the blackout signal is captured before it can collapse the power system. The proposed method is realized in R and demonstrated the modified IEEE One Area RTS-96 power system.

Rapid Assessment, Visualization, and Mitigation of Cascading Failure Risk in Power Systems

Abstract: This paper describes a new approach, using "Random Chemistry" sampling, to estimate the risk of large cascading blackouts triggered by multiple contingencies. On a 2383 bus test case the new approach finds the expected value of large-blackout sizes (a measure of risk) two orders of magnitude faster than Monte Carlo sampling, without introducing measurable bias. We also derive a method to compute the sensitivity of blackout risk to individual component-failure probabilities, allowing one to quickly identify low-cost strategies for reducing risk. For example, we show how a 1.9% increase in operational costs reduced the overall risk of cascading failure in a 2383-bus test case by 61%. An examination of how risk changes with load yielded a surprising decrease in cascading failure risk at the highest loadings, due to increased locality in generation and less long-distance transmission. Finally, this paper proposes new visualizations of spatio-temporal patterns in cascading failure risk that could provide valuable guidance to system planners and operators.

Curing critical links in oscillator networks as power grid models

Abstract: Modern societies crucially depend on the robust supply with electric energy. Blackouts of power grids can thus have far reaching consequences. During a blackout, often the failure of a single infrastructure, such as a critical transmission line, results in several subsequent failures that spread across large parts of the network. Preventing such large-scale outages is thus key for assuring a reliable power supply. Here we present a non-local curing strategy for oscillatory power grid networks based on the global collective redistribution of loads. We first identify critical links and compute residual capacities on alternative paths on the remaining network from the original flows. For each critical link, we upgrade lines that constitute bottlenecks on such paths. We demonstrate the viability of this strategy for random ensembles of network topologies as well as topologies derived from real transmission grids and compare the nonlocal strategy against local back-ups of critical links. These strategies are independent of the detailed grid dynamics and combined may serve as an effective guideline to reduce outages in power grid networks by intentionally strengthen optimally selected links.