A Bayesian Approach Based Outage Prediction in Electric Utility Systems Using Radar Measurement Data

Yue, Meng; Toto, Tami; Jensen, Michael P.; Giangrande, Scott E.; Lofaro, Robert

doi:10.1109/TSG.2017.2704288

Title: A Bayesian Approach Based Outage Prediction in Electric Utility Systems Using Radar Measurement Data

Abstract

Severe weather events such as strong thunderstorms are some of the most significant and frequent threats to the electrical grid infrastructure. Outages resulting from storms can be very costly. While some tools are available to utilities to predict storm occurrences and damage, they are typically very crude and provide little means of facilitating restoration efforts. This study developed a methodology to use historical high-resolution (both temporal and spatial) radar observations of storm characteristics and outage information to develop weather condition dependent failure rate models (FRMs) for different grid components. Such models can provide an estimation or prediction of the outage numbers in small areas of a utility’s service territory once the real-time measurement or forecasted data of weather conditions become available as the input to the models. Considering the potential value provided by real-time outages reported, a Bayesian outage prediction (BOP) algorithm is proposed to account for both strength and uncertainties of the reported outages and failure rate models. The potential benefit of this outage prediction scheme is illustrated in this study.

Authors:

^[1]; Toto, Tami ^[1]; Jensen, Michael P. ^[1]; Giangrande, Scott E. ^[1]; Lofaro, Robert ^[1]

Brookhaven National Lab. (BNL), Upton, NY (United States)

Publication Date:: 2017-05-18

Research Org.:: Brookhaven National Lab. (BNL), Upton, NY (United States)

Sponsoring Org.:: New York State Energy Research and Development Authority (NYSERDA); USDOE

OSTI Identifier:: 1389221

Report Number(s):: BNL-114128-2017-JA
Journal ID: ISSN 1949-3053

Grant/Contract Number:: SC0012704

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Smart Grid

Additional Journal Information:: Journal Volume: 9; Journal Issue: 6; Journal ID: ISSN 1949-3053

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 24 POWER TRANSMISSION AND DISTRIBUTION

Citation Formats


                    Yue, Meng, Toto, Tami, Jensen, Michael P., Giangrande, Scott E., and Lofaro, Robert. A Bayesian Approach Based Outage Prediction in Electric Utility Systems Using Radar Measurement Data.  United States: N. p., 2017. 
Web.  doi:10.1109/TSG.2017.2704288.

Copy to clipboard


                    Yue, Meng, Toto, Tami, Jensen, Michael P., Giangrande, Scott E., & Lofaro, Robert. A Bayesian Approach Based Outage Prediction in Electric Utility Systems Using Radar Measurement Data.  United States.  https://doi.org/10.1109/TSG.2017.2704288

Copy to clipboard


                    Yue, Meng, Toto, Tami, Jensen, Michael P., Giangrande, Scott E., and Lofaro, Robert. Thu .  
"A Bayesian Approach Based Outage Prediction in Electric Utility Systems Using Radar Measurement Data".  United States.  https://doi.org/10.1109/TSG.2017.2704288.  https://www.osti.gov/servlets/purl/1389221.

Copy to clipboard


                    
@article{osti_1389221,

  title        = {A Bayesian Approach Based Outage Prediction in Electric Utility Systems Using Radar Measurement Data},

  author       = {Yue, Meng and Toto, Tami and Jensen, Michael P. and Giangrande, Scott E. and Lofaro, Robert},

  abstractNote = {Severe weather events such as strong thunderstorms are some of the most significant and frequent threats to the electrical grid infrastructure. Outages resulting from storms can be very costly. While some tools are available to utilities to predict storm occurrences and damage, they are typically very crude and provide little means of facilitating restoration efforts. This study developed a methodology to use historical high-resolution (both temporal and spatial) radar observations of storm characteristics and outage information to develop weather condition dependent failure rate models (FRMs) for different grid components. Such models can provide an estimation or prediction of the outage numbers in small areas of a utility’s service territory once the real-time measurement or forecasted data of weather conditions become available as the input to the models. Considering the potential value provided by real-time outages reported, a Bayesian outage prediction (BOP) algorithm is proposed to account for both strength and uncertainties of the reported outages and failure rate models. The potential benefit of this outage prediction scheme is illustrated in this study.},

  doi          = {10.1109/TSG.2017.2704288},

  journal      = {IEEE Transactions on Smart Grid},

  number       = 6,

  volume       = 9,

  place        = {United States},

  year         = {2017},

  month        = {5}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.1109/TSG.2017.2704288

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 15 works

Citation information provided by
Web of Science

Figures / Tables:

Fig. 1: Data for all four types of NEXRAD measurements (columns), row 1 – KALY, row 2 – KOKX, and row 3, the combined fields.

All figures and tables (10 total)

Save / Share:

Export Metadata

Save to My Library

Figures / Tables found in this record:

Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.

Similar Records in DOE PAGES and OSTI.GOV collections:

Predicting Power Outage During Extreme Weather with EAGLE-I and NWS Datasets

Conference Lee, Sangkeun ; Choi, Jong Youl ; Jung, Gs ; ...

Extreme weather events, such as hurricanes, severe thunderstorms, and floods can significantly disrupt power grid systems, leading to electrical outages that result in inconvenience, economic losses, and life-threatening situations. There is a growing need for a robust and precise predictive model to forecast power outages, which will help prioritize emergency response before, during, and after extreme weather events. In this paper, we introduce machine-learning models that predict power outage risk at the state level during and after extreme weather events. We jointly utilized two publicly available datasets: the U.S. historical power outage data collected by the Environment for Analysis ofmore »« less
https://doi.org/10.1109/IRI58017.2023.00042

Full Text Available
Predicting Storm Outages Through New Representations of Weather and Vegetation

Journal Article Cerrai, Diego ; Wanik, David W. ; Bhuiyan, Md Abul Ehsan ; ... - IEEE Access

This paper presents new developments in an outage prediction model (OPM) for an electric distribution network in the Northeastern United States and assesses their significance to the OPM performance. The OPM uses regression tree models fed by numerical weather prediction outputs, spatially distributed information on soil, vegetation, electric utility assets, and historical power outage data to forecast the number and spatial distribution of outages across the power distribution grid. New modules introduced hereby consist in 1) a storm classifier based on weather variables; 2) a multimodel optimization of regression tree output; and 3) a post-processing routine for more accurately describingmore »« less
Cited by 32
https://doi.org/10.1109/ACCESS.2019.2902558

Full Text Available
ASSIMILATION OF DOPPLER RADAR DATA INTO NUMERICAL WEATHER MODELS

Technical Report Chiswell, S ; Buckley, R

During the year 2008, the United States National Weather Service (NWS) completed an eight fold increase in sampling capability for weather radars to 250 m resolution. This increase is expected to improve warning lead times by detecting small scale features sooner with increased reliability; however, current NWS operational model domains utilize grid spacing an order of magnitude larger than the radar data resolution, and therefore the added resolution of radar data is not fully exploited. The assimilation of radar reflectivity and velocity data into high resolution numerical weather model forecasts where grid spacing is comparable to the radar data resolutionmore »« less
https://doi.org/10.2172/971333

Full Text Available
High concentrations of ice: Investigations using polarimetric radar observations combined with in situ measurements and cloud modeling

Technical Report Ryzhkov, Alexander ; Khain, Alexander ; Phillips, Vaughan ; ...

This DOE-funded joint project had the over-arching goal of understanding the reasons for observed size distributions of ice particles in cold clouds. Its approach involves the use of cloud models and field observations by radar and aircraft of real storms. The project addresses three major research objectives: (1) Utilize a novel polarimetric radar technique to retrieve size distributions and amounts of ice from radar data collected during the previous DOE ARM field campaigns. (2) Evaluate the results of ice microphysical retrievals using available in situ aircraft measurements and other remote sensors (e.g., vertically pointing cloud radars and wind profilers). (3)more »« less
https://doi.org/10.2172/1901905

Full Text Available
The SURE House (Solar Decathlon 2015)

Technical Report Nastasi, John ; May, Edwin

Coastal towns and cities across the Northeastern US, with their high population density, aged utility infrastructure, and unique geography, are increasingly vulnerable to climate change related storm events. In October 2012 superstorm Sandy highlighted the fragility of our current coastal building types and made clear the need for a new model of design and construction which works to understand and mitigate these weaknesses. Dramatic changes in public policy, championed by both The Federal Emergency Management Agency (FEMA) and the National Flood Insurance Program (NFIP) are driving the rebuilding of these shore communities, often resulting in costly renovations, un-sustainable neighborhood configurationsmore »« less
https://doi.org/10.2172/1378086

Full Text Available

Similar Records