Name
Abstract | ||
|---|---|---|
We propose a method for power theft detection based on predictive models for technical losses in electrical distribution networks estimated entirely from data collected by smart meters in smart grids. Although the data sampling rate of smart meters is not sufficiently high to detect power theft with complete certainty, detection is still possible in a statistical decision theory sense, based on statistical models estimated from collected data sets. Even without detailed knowledge of the exact topology of the distribution network, it is possible to estimate a statistical model of the technical losses that allows indirect estimation of the non-technical losses (power theft) with high accuracy. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-39712-7_29 | MLDM |
Keywords | Field | DocType |
electrical distribution network,technical loss,smart grid,power theft,statistical decision theory sense,smart meter,statistical model,distribution network,smart meter data analysis,power theft detection | Data mining,Data set,Metre,Smart grid,Computer science,Distribution networks,Load profile,Statistical model,Smart meter,Data sampling | Conference |
Citations | PageRank | References |
6 | 0.99 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Nikovski | 1 | 165 | 31.87 |
| Zhenhua Wang | 2 | 665 | 71.69 |
| Alan Esenther | 3 | 189 | 13.48 |
| Hongbo Sun | 4 | 6 | 1.66 |
| Keisuke Sugiura | 5 | 6 | 0.99 |
| Toru Muso | 6 | 12 | 1.55 |
| Kaoru Tsuru | 7 | 12 | 1.88 |