Statistical and probabilistic models for smart electricity distribution networks
This thesis aims towards an improved statistical understanding of distribution feeders, an improved probabilistic understanding of loads and methods for network-wide assessment of SmartGrid technologies. An efficient multi variable statistical analysis method was presented to identify prototypical f...
| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
Curtin University
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/1515 |
| _version_ | 1848743689221832704 |
|---|---|
| author | Li, Yingliang |
| author_facet | Li, Yingliang |
| author_sort | Li, Yingliang |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This thesis aims towards an improved statistical understanding of distribution feeders, an improved probabilistic understanding of loads and methods for network-wide assessment of SmartGrid technologies. An efficient multi variable statistical analysis method was presented to identify prototypical feeders, which relies upon a few key variables that are highly meaningful from an engineering perspective and readily available in most distribution companies. Hybrid models for residential consumer load were built for high and low demand days. |
| first_indexed | 2025-11-14T05:49:33Z |
| format | Thesis |
| id | curtin-20.500.11937-1515 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T05:49:33Z |
| publishDate | 2013 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-15152017-02-20T06:37:18Z Statistical and probabilistic models for smart electricity distribution networks Li, Yingliang This thesis aims towards an improved statistical understanding of distribution feeders, an improved probabilistic understanding of loads and methods for network-wide assessment of SmartGrid technologies. An efficient multi variable statistical analysis method was presented to identify prototypical feeders, which relies upon a few key variables that are highly meaningful from an engineering perspective and readily available in most distribution companies. Hybrid models for residential consumer load were built for high and low demand days. 2013 Thesis http://hdl.handle.net/20.500.11937/1515 en Curtin University fulltext |
| spellingShingle | Li, Yingliang Statistical and probabilistic models for smart electricity distribution networks |
| title | Statistical and probabilistic models for smart electricity distribution networks |
| title_full | Statistical and probabilistic models for smart electricity distribution networks |
| title_fullStr | Statistical and probabilistic models for smart electricity distribution networks |
| title_full_unstemmed | Statistical and probabilistic models for smart electricity distribution networks |
| title_short | Statistical and probabilistic models for smart electricity distribution networks |
| title_sort | statistical and probabilistic models for smart electricity distribution networks |
| url | http://hdl.handle.net/20.500.11937/1515 |