An IEEE survey of US and Canadian overhead transmission outages at 230 kV and above

Adler, Richard B.; Daniel, S.L.; Heising, C.; Lauby, M. G.; Ludorf, R.P.; White, T.S.

doi:10.1109/61.277677

Cited by 54 publications

(19 citation statements)

References 3 publications

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“…A chi-squared goodness-of-fit test shows that the distributions are consistent at the 5% confidence level (the test groups together 5 or more outages). A heavy tail in the distribution of the total number of line outages is also observed in North American data in [8,1], but our data has a heavier tail than [8,1].…”

Section: Estimating λ and The Distribution Of Outagessupporting

confidence: 56%

Towards quantifying cascading blackout risk

Dobson

Wierzbicki

Kim

et al. 2007

2007 iREP Symposium - Bulk Power System Dynamics and Control - VII. Revitalizing Operational Reliability

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Blackouts become widespread by initial failures propagating in a diverse and intricate cascade of rare events. We describe this complicated cascade using a bulk probabilistic model in which the initial failures propagate randomly according to a branching process. The branching process parameters can be statistically estimated from observed data or simulations and then used to efficiently predict the probability distribution of blackout size. We review the current testing of these methods on simulations and observed data and discuss the next steps towards achieving verified and practical methods for quantifying cascading failure of electric power systems. The ability to efficiently quantify cascading blackout risk from observed data and simulations could offer new ways to monitor power transmission system reliability and quantify the reliability benefit of proposed improvements.

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Section: Estimating λ and The Distribution Of Outagessupporting

confidence: 56%

Towards quantifying cascading blackout risk

Dobson

Wierzbicki

Kim

et al. 2007

2007 iREP Symposium - Bulk Power System Dynamics and Control - VII. Revitalizing Operational Reliability

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“…Random hazards are modeled by random failures with a daily failure rate p 0 ¼ 0.00015 from historical data. 30 For other parameters, based on reality and common practices, their values are set as follows: for restoration time t r ¼ e þ g, where the variable e is assumed satisfying the uniform distribution [0, 3 h] and the variable g satisfying the exponential distribution with mean value 5 h. 26 During the cascading failures, the hidden failure probability hp ¼ 0.005; 24 the probability of overloaded lines failed with damage fd ¼ 0.3; 22 the time required to restore shed load t s ¼ 8 h;…”

Section: -4mentioning

confidence: 99%

Time-dependent resilience assessment and improvement of urban infrastructure systems

Ouyang

Dueñas‐Osorio

2012

Chaos: An Interdisciplinary Journal of Nonlinear Science

208

126

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This paper introduces an approach to assess and improve the time-dependent resilience of urban infrastructure systems, where resilience is defined as the systems' ability to resist various possible hazards, absorb the initial damage from hazards, and recover to normal operation one or multiple times during a time period T. For different values of T and its position relative to current time, there are three forms of resilience: previous resilience, current potential resilience, and future potential resilience. This paper mainly discusses the third form that takes into account the systems' future evolving processes. Taking the power transmission grid in Harris County, Texas, USA as an example, the time-dependent features of resilience and the effectiveness of some resilience-inspired strategies, including enhancement of situational awareness, management of consumer demand, and integration of distributed generators, are all simulated and discussed. Results show a nonlinear nature of resilience as a function of T, which may exhibit a transition from an increasing function to a decreasing function at either a threshold of post-blackout improvement rate, a threshold of load profile with consumer demand management, or a threshold number of integrated distributed generators. These results are further confirmed by studying a typical benchmark system such as the IEEE RTS-96. Such common trends indicate that some resilience strategies may enhance infrastructure system resilience in the short term, but if not managed well, they may compromise practical utility system resilience in the long run. Urban infrastructure systems are vital to the operation of modern society and its economy, yet they are unavoidably subject to different types of hazards and are vulnerable to cascading failures within and across systems. Hence, different from traditional system safety analyses using scenarios or hypothetical accidents in an attempt to understand their effects and the reasons for their occurrence, the concept of resilience has been proposed in acknowledgment of the unavoidability of hazards or accidents. A number of articles have recently assessed the resilience of systems under single and multiple hazards. However, these studies neither address the evolving features of infrastructure systems due to the steady increase of service demand and post-event improvement efforts, nor address inter-hazards interactions, as the occurrences and effects of future hazards may be affected by previous ones. Capturing these unexplored features leads to the proposal of a time-dependent resilience metric for urban infrastructure systems. This paper uses power transmission systems as examples to show the time-dependent and nonlinear features of resilience, and discusses the effectiveness of some resilienceinspired strategies, including situational awareness (SA) enhancement, consumer demand management, and distributed generators (DGs) integration, in order to emphasize the need to carefully manage emerging smart infrastructure techniques.

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“…Outage has been continuously surveyed and investigated for tens of years to reflect failure rate of transmission system [1]. Cascading outage arising from blackout has attracted much interest of researchers.…”

Section: Introductionmentioning

confidence: 99%

Probability Distribution of Fault in Distribution System

Duan

Chan

2008

IEEE Trans. Power Syst.

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This letter analyses time series of two distribution system (DS) fault records to find their probability distribution. Observation indicates that the complementary cumulative distribution function (CCDF) of number of fault per day has a power law tail and that of location of fault has an exponential tail. The data add to the knowledge that is a fundamental basis for developing methods for power system maintenance and reliability.

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An IEEE survey of US and Canadian overhead transmission outages at 230 kV and above

Cited by 54 publications

References 3 publications

Towards quantifying cascading blackout risk

Towards quantifying cascading blackout risk

Time-dependent resilience assessment and improvement of urban infrastructure systems

Probability Distribution of Fault in Distribution System

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