Real-time Ranking of Electrical Feeders using Expert Advice
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Hila Becker, Marta Arias, "Real-time Ranking of Electrical Feeders using Expert Advice", European Workshop on Data Stream Analysis 2007
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Real-time Ranking of Electrical Feeders using Expert Advice
- 1. Real-time Ranking of Electrical Feeders using Expert Advice Hila Becker 1,2 , Marta Arias 1 1 Center for Computational Learning Systems 2 Computer Science Department Columbia University
- 3. The Electrical System 1. Generation 2. Transmission 3. Primary Distribution 4. Secondary Distribution
- 10. Application Structure Static data SQL Server DB ML Engine ML Models Rankings Decision Support GUI Action Driver Action Tracker Decision Support App Outage data Xfmr Stress data Feeder Load data
- 15. Performance Metric Example ranking outages 0 8 0 7 0 6 1 5 0 4 1 3 1 2 0 1
- 17. Real-time ranking with MartiRank time time time
- 28. Online Ranking Algorithm e 1 . . . e 2 e 3 e B w 1 w 2 w 3 w B ? F1 F4 F3 F2 F5 F4 F2 F1 F3 F5 F1 F3 F5 F4 F2 F1 F3 F4 F2 F5 F1 F3 F4 F2 F5 F3 F1 F4 F2 F5 e B+1 e B+2 w B+1 w B+2
- 32. Performance â Summer 2005
- 33. Performance â Winter 2006
- 34. Parameter Variation - Budget
Hinweis der Redaktion
- Generation: a prime mover, typically the force of water, steam, or hot gasses on a turbine, spins an electromagnet, generating large amounts of electrical current at a generating station Transmission: the current is sent at very high voltage (hundreds of thousands of volts) from the generating station to substations closer to the customers Primary Distribution: electricity is sent at mid-level voltage (tens of thousands of volts) from substations to local transformers , over cables called feeders, usually 10-20 km long, and with a few tens of transformers per feeder. Feeders are composed of many feeder sections connected by joints and splices Secondary Distribution: sends electricity at normal household voltages from local transformers to individual customers
- Many occur in the summer when the load on the system increases, during heat waves When an O/A occurs, the load that had been carried by the failed feeder must shift to adjacent feeders, further stressing them. This can lead to a failure cascade in a distribution network
- (e.g. age and composition of each feeder section) and e.g. electrical load data for a feeder and its transformers, accumulating at a rate of several hundred megabytes per day) Software engineering challenges to manage data
- with sufficient accuracy so that timely preventive maintenance can be taken on the right feeders at the right time.
- How to deal with impending failures that are corrected on the basis of our ranking
- Use data within a window, aggregate dynamic data within that period in various ways (quantiles, counts, sums, averages, etc.) Re-train daily, or weekly, or every 2 weeks, or monthly, orâŠ
- Use data within a window, aggregate dynamic data within that period in various ways (quantiles, counts, sums, averages, etc.) Re-train daily, or weekly, or every 2 weeks, or monthly, orâŠ
- No human intervention needed Changes in system are learned as it runs
- Every 7 days Seems to achieve balance of generating new models to adapt to changing conditions without overflowing system
- if a model was successful in the previous summer but was retired during the winter, it should be rescued back in the upcoming summer if similar environmental conditions reappear.