Distribution system reliability assessment: momentary interruptions and storms

Brown, Richard E.; Gupta, Shalini; Christie, R.D.; Venkata, S.S.; Fletcher, Robert H.

doi:10.1109/61.634177

Cited by 106 publications

(34 citation statements)

References 9 publications

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“…Inclusion of additional upper air parameters such as the magnitude and direction of the low level jet may provide useful information into the regression for heavy precipitation and wind events (Gronas 1995;Harnack et al 2001). Wind duration was found to be an important predictor by Brown et al (1997) and would likely provide important additional information for long lasting events. To avoid overfitting the regression for the None and Warm storm modes, an additional stratification into seasonal regression equations may be necessary.…”

Section: Future Workmentioning

confidence: 99%

“…Brown et al (1997) studied the reliability of distribution systems during high wind events using Monte Carlo simulations and found wind speed and wind duration to be essential for assessing system reliability. Han et al (2009) used a general additive model to predict power outage risk based on measured characteristics of hurricanes and were able to outperform regression based approaches.…”

Section: B Statistical Forecasting Applications For Utilitiesmentioning

confidence: 99%

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A Statistical Forecast Model of Weather-Related Damage to a Major Electric Utility

Cerruti¹,

Decker²

2011

Journal of Applied Meteorology and Climatology

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A model has been developed to relate meteorological conditions to damages incurred by the outdoor electrical equipment (plant) of Public Service Electric and Gas (PSE&G), the largest public utility in New Jersey. Utilizing a perfect prognosis approach, the model consists of equations derived from a backwards eliminated multiple linear regression analysis of observed damage (the predictand) and corresponding surface observations from a variety of sources including local storm reports (the predictors). The analysis gives a different equation for each combination of plant damage element (e.g., poles down, transformers blown), the four PSE&G service territories, and objectively defined storm modes (e.g., Thunderstorm, Heat Wave, None). The predictors used most often were found to be products of maximum wind gust with maximum temperature, daily liquid water equivalent precipitation, and ten day accumulated liquid equivalent precipitation, and were often found to be significant (p-value less than 0.05). The number of severe weather reports provided significant predictors for the Thunderstorm storm

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Section: Future Workmentioning

confidence: 99%

Section: B Statistical Forecasting Applications For Utilitiesmentioning

confidence: 99%

A Statistical Forecast Model of Weather-Related Damage to a Major Electric Utility

Cerruti¹,

Decker²

2011

Journal of Applied Meteorology and Climatology

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“…Much of the focus of modeling the effects of weather on power distribution systems has remained on extreme weather conditions [1][2][3]. There is also a body of work that includes weather as a factor in the analysis of specific fault causes [4,5].…”

Section: Introductionmentioning

confidence: 99%

Weather-based interruption prediction in the smart grid utilizing chronological data

Sarwat

Amini

Domijan

et al. 2015

J. Mod. Power Syst. Clean Energy

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This unique study will demonstrate a combined effect of weather parameters on the total number of power distribution interruptions in a region. Based on common weather conditions, a theoretical model can predict interruptions and risk assessment with immediate weather conditions. Using daily and hourly weather data, the created models will predict the number of daily or by-shift interruptions. The weather and environmental conditions to be addressed will include rain, wind, temperature, lightning density, humidity, barometric pressure, snow and ice. Models will be developed to allow broad applications. Statistical and deterministic simulations of the models using the data collected will be conducted by employing existing software, and the results will be used to refine the models. Models developed in this study will be used to predict power interruptions in areas that can be readily monitored, thus validating the models. The application has resulted in defining the predicted number of interruptions in a region with a specific confidence level. Reliability is major concern for every utility. Prediction and timely action to minimize the outage duration improves reliability. Use of this predictor model with existing smart grid self-healing technology is proposed.

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“…Companies are not only affected by the demanding norms but also by high financial lost due to energy non-sell and penalties. The reliability is evaluated using the markovian models (Brown and Gupta, 1998; Asgapoor and Mathine, 1997 Brown, et al, 1997 Brown andOchoa, 1998) or different analytic methods (Billinton y Wagn, 1999), that, according to the past behavior, they make an extrapolation of its future one. This paper is based in developing a method that makes possible the prediction of distribution system's behavior in terms of reliability, addressing it to the Artificial Intelligence (AI).…”

Section: Introductionmentioning

confidence: 99%

Solution to the Reliability Problem of Radial Electric Distribution Systems Through Artificial Intelligence

López

Campos

Tardón

et al. 2006

INT J COMPUT COMMUN

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Distribution system reliability assessment: momentary interruptions and storms

Cited by 106 publications

References 9 publications

A Statistical Forecast Model of Weather-Related Damage to a Major Electric Utility

A Statistical Forecast Model of Weather-Related Damage to a Major Electric Utility

Weather-based interruption prediction in the smart grid utilizing chronological data

Solution to the Reliability Problem of Radial Electric Distribution Systems Through Artificial Intelligence

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