Extraction of spatial-temporal features of bus loads in electric grids through clustering in a dynamic model space
Bus loads in electric grids have inherently a spatial-temporal behavior and also a certain degree of randomness. The spatial-temporal feature based bus load forecasting, which provides additional information on the spatial distribution and the uncertainty of future electric loads, is of importance t...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2020
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| In: |
IEEE access
Year: 2019, Jahrgang: 8, Pages: 5852-5861 |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/ACCESS.2019.2963071 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1109/ACCESS.2019.2963071
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| Verfasserangaben: | Wei Zhang, Gang Mu, Chen Song, Gangui Yan, and Vincent Heuveline |
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| 245 | 1 | 0 | |a Extraction of spatial-temporal features of bus loads in electric grids through clustering in a dynamic model space |c Wei Zhang, Gang Mu, Chen Song, Gangui Yan, and Vincent Heuveline |
| 264 | 1 | |c 2020 | |
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| 520 | |a Bus loads in electric grids have inherently a spatial-temporal behavior and also a certain degree of randomness. The spatial-temporal feature based bus load forecasting, which provides additional information on the spatial distribution and the uncertainty of future electric loads, is of importance to power systems dispatching and planning, in particular, with intermittent renewable power generation. In this paper, a method for extracting spatial-temporal features, including abnormal states of multiple bus loads in electric grids, is proposed. The abnormal spatial load states are firstly identified by using one-class support vector machine. Then, only the load fluctuations of normal states are mapped into a dynamic model space supported by polynomials in order to approximate the time series of bus loads. The parameters of polynomials are clustered by the Dirichlet process mixture model for deriving the patterns of load state evolution. As a result, the extracted spatial-temporal patterns are a set of different distributions of bus loads with static features and dynamic features displayed explicitly. The method is tested against the bus loads of an electric grid in a city in the Northeast China. The proposed methodology is validated with respect to the bus loads in time slots of the future 10 days. | ||
| 534 | |c 2019 | ||
| 650 | 4 | |a Dirichlet process mixture model | |
| 650 | 4 | |a dynamic model space clustering | |
| 650 | 4 | |a electric grid | |
| 650 | 4 | |a feature extraction | |
| 650 | 4 | |a intermittent renewable power generation | |
| 650 | 4 | |a load forecasting | |
| 650 | 4 | |a Load forecasting | |
| 650 | 4 | |a load state evolution | |
| 650 | 4 | |a mixture models | |
| 650 | 4 | |a multiple bus loads | |
| 650 | 4 | |a Northeast China | |
| 650 | 4 | |a one-class support vector machine | |
| 650 | 4 | |a pattern clustering | |
| 650 | 4 | |a polynomials | |
| 650 | 4 | |a power engineering computing | |
| 650 | 4 | |a power grids | |
| 650 | 4 | |a power system dispatching | |
| 650 | 4 | |a power system planning | |
| 650 | 4 | |a spatial-temporal feature | |
| 650 | 4 | |a spatial-temporal feature based bus load forecasting | |
| 650 | 4 | |a spatial-temporal feature extraction | |
| 650 | 4 | |a support vector machines | |
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