Mapping Rule Estimation for Power Flow Analysis in Distribution Grids Article Swipe
Jiafan Yu
,
Yang Weng
,
Ram Rajagopal
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1702.07948
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1702.07948
The increasing integration of distributed energy resources (DERs) calls for new monitoring and operational planning tools to ensure stability and sustainability in distribution grids. One idea is to use existing monitoring tools in transmission grids and some primary distribution grids. However, they usually depend on the knowledge of the system model, e.g., the topology and line parameters, which may be unavailable in primary and secondary distribution grids. Furthermore, a utility usually has limited modeling ability of active controllers for solar panels as they may belong to a third party like residential customers. To solve the modeling problem in traditional power flow analysis, we propose a support vector regression (SVR) approach to reveal the mapping rules between different variables and recover useful variables based on physical understanding and data mining. We illustrate the advantages of using the SVR model over traditional regression method which finds line parameters in distribution grids. Specifically, the SVR model is robust enough to recover the mapping rules while the regression method fails when 1) there are measurement outliers and missing data, 2) there are active controllers, or 3) measurements are only available at some part of a distribution grid. We demonstrate the superior performance of our method through extensive numerical validation on different scales of distribution grids.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1702.07948
- https://arxiv.org/pdf/1702.07948
- OA Status
- green
- Cited By
- 6
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2950014333
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https://openalex.org/W2950014333Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1702.07948Digital Object Identifier
- Title
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Mapping Rule Estimation for Power Flow Analysis in Distribution GridsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
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2017-02-25Full publication date if available
- Authors
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Jiafan Yu, Yang Weng, Ram RajagopalList of authors in order
- Landing page
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https://arxiv.org/abs/1702.07948Publisher landing page
- PDF URL
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https://arxiv.org/pdf/1702.07948Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1702.07948Direct OA link when available
- Concepts
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Computer science, Grid, Support vector machine, Outlier, Distributed generation, Data mining, Regression analysis, Stability (learning theory), Mathematical optimization, Machine learning, Renewable energy, Artificial intelligence, Engineering, Mathematics, Geometry, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
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2023: 2, 2022: 1, 2021: 2, 2017: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.primary | 37, 62 |
| abstract_inverted_index.problem | 96 |
| abstract_inverted_index.propose | 103 |
| abstract_inverted_index.recover | 119, 157 |
| abstract_inverted_index.support | 105 |
| abstract_inverted_index.through | 201 |
| abstract_inverted_index.usually | 42, 70 |
| abstract_inverted_index.utility | 69 |
| abstract_inverted_index.However, | 40 |
| abstract_inverted_index.approach | 109 |
| abstract_inverted_index.existing | 29 |
| abstract_inverted_index.modeling | 73, 95 |
| abstract_inverted_index.outliers | 171 |
| abstract_inverted_index.physical | 124 |
| abstract_inverted_index.planning | 14 |
| abstract_inverted_index.superior | 196 |
| abstract_inverted_index.topology | 53 |
| abstract_inverted_index.analysis, | 101 |
| abstract_inverted_index.available | 185 |
| abstract_inverted_index.different | 116, 206 |
| abstract_inverted_index.extensive | 202 |
| abstract_inverted_index.knowledge | 46 |
| abstract_inverted_index.numerical | 203 |
| abstract_inverted_index.resources | 6 |
| abstract_inverted_index.secondary | 64 |
| abstract_inverted_index.stability | 18 |
| abstract_inverted_index.variables | 117, 121 |
| abstract_inverted_index.advantages | 132 |
| abstract_inverted_index.customers. | 91 |
| abstract_inverted_index.illustrate | 130 |
| abstract_inverted_index.increasing | 1 |
| abstract_inverted_index.monitoring | 11, 30 |
| abstract_inverted_index.parameters | 145 |
| abstract_inverted_index.regression | 107, 140, 163 |
| abstract_inverted_index.validation | 204 |
| abstract_inverted_index.controllers | 77 |
| abstract_inverted_index.demonstrate | 194 |
| abstract_inverted_index.distributed | 4 |
| abstract_inverted_index.integration | 2 |
| abstract_inverted_index.measurement | 170 |
| abstract_inverted_index.operational | 13 |
| abstract_inverted_index.parameters, | 56 |
| abstract_inverted_index.performance | 197 |
| abstract_inverted_index.residential | 90 |
| abstract_inverted_index.traditional | 98, 139 |
| abstract_inverted_index.unavailable | 60 |
| abstract_inverted_index.Furthermore, | 67 |
| abstract_inverted_index.controllers, | 179 |
| abstract_inverted_index.distribution | 22, 38, 65, 147, 191, 209 |
| abstract_inverted_index.measurements | 182 |
| abstract_inverted_index.transmission | 33 |
| abstract_inverted_index.Specifically, | 149 |
| abstract_inverted_index.understanding | 125 |
| abstract_inverted_index.sustainability | 20 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |