Predicting weather-associated impacts in outage management utilizing the GIS framework

Weather-related impacts are at the top of all outage causes. Yet, traditional outage management (OM) approaches do not integrate all available and relevant weather-associated data automatically. This paper presents a predictive method that correlates different weather-associated data layers to provide predictive OM process implemented using the geographic information systems (GIS) framework. Examples for both transmission and distribution OM are demonstrated using vegetation, wind, and power system data in ArcGIS.

[1]  Prasad J. Dongale,et al.  Integration of Asset and Outage Management Tasks for Distribution Application , 2009 .

[2]  A. Baccini,et al.  Mapping forest canopy height globally with spaceborne lidar , 2011 .

[3]  Lloyd A. Treinish On-going Utilization and Evaluation of a Coupled Weather and Outage Prediction for Electric Distribution Operations , 2013 .

[4]  Sai-Yi Wang,et al.  Study and application of decision-making system for urban network planning of Shanghai , 2008, 2008 China International Conference on Electricity Distribution.

[5]  D. Lubkeman,et al.  Large scale storm outage management , 2004, IEEE Power Engineering Society General Meeting, 2004..

[6]  T.E. Lee,et al.  A fuzzy rule-based system for crew management of distribution systems in large-scale multiple outages , 2004, 2004 International Conference on Power System Technology, 2004. PowerCon 2004..

[7]  Mladen Kezunovic,et al.  The role of big data in improving power system operation and protection , 2013, 2013 IREP Symposium Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid.

[8]  M. KEZUNOVIC,et al.  Hierarchically Coordinated Protection : An Integrated Concept of Corrective , Predictive , and Inherently Adaptive Protection , 2015 .

[9]  R. Billinton,et al.  Reliability Cost/Worth Assessment of Distribution Systems Incorporating Time Varying Weather Conditions and Restoration Resources , 2001, IEEE Power Engineering Review.

[10]  Richard J. Campbell,et al.  Weather-Related Power Outages and Electric System Resiliency , 2012 .

[11]  Melinda Marquis Weather, Climate, and the New Energy Economy , 2011 .

[12]  Surajit Chaudhuri,et al.  An overview of data warehousing and OLAP technology , 1997, SGMD.

[13]  Mladen Kezunovic,et al.  Improved Transmission Line Fault Location Using Automated Correlation of Big Data from Lightning Strikes and Fault-Induced Traveling Waves , 2015, 2015 48th Hawaii International Conference on System Sciences.

[14]  Bill Meehan Modeling Electric Distribution with GIS , 2013 .

[15]  Seth D. Guikema,et al.  Incorporating Hurricane Forecast Uncertainty into a Decision-Support Application for Power Outage Modeling , 2014 .

[16]  H M Poulos,et al.  Decision Support for Mitigating the Risk of Tree Induced Transmission Line Failure in Utility Rights-of-Way , 2010, Environmental management.