Intelligent forecast of fault risk in active distribution networks considering network reconfiguration Article Swipe
Haiguo Tang
,
Di Zhang
,
Chengying Liu
,
Lei Ren
,
Jiayong Li
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.12158/j.2096-3203.2022.05.024
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.12158/j.2096-3203.2022.05.024
After occurrence of natural disasters,active distribution network (ADN) can promptly restore power supply to some critical loads through tie-line switching and flexible distributed generation (DG),and thus the fault risk is effectively mitigated. A data-driven multi-dimensional intelligent forecast approach for the fault risk levels in ADNs is proposed in this paper. Firstly,a feature selection method based on Chi-square test (χ2) and Pearson correlation coeffects is developed to analyze the strength of fault correlation factors from multiple dimensions and the optimal set of fault features is obtained. Then,an optimal network reconfiguration model is established for the damaged ADNs considering DG integration,and consequently the heterogeneity of the line importance can be taken into account which provides a solid foundation for the classification of fault risks. Furthermore,an intelligent forecast model for ADN fault risk levels is established based on extreme gradient boostig (XGBoost) algorithm. Finally,the numerical tests on IEEE RBTS Bus6 distribution network demonstrate that the proposed approach achieves a predication accuracy 3.17% higher than back propagation (BP) neural network does. The proposed approach has good generalization capability,thus providing an important basis for the fault risk management in ADNs to effectively reduce the fault loss.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doaj.org/article/4c28ebfb89464e25934767e57d48820d
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4361868119Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.12158/j.2096-3203.2022.05.024Digital Object Identifier
- Title
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Intelligent forecast of fault risk in active distribution networks considering network reconfigurationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-09-01Full publication date if available
- Authors
-
Haiguo Tang, Di Zhang, Chengying Liu, Lei Ren, Jiayong LiList of authors in order
- Landing page
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https://doaj.org/article/4c28ebfb89464e25934767e57d48820dPublisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://doaj.org/article/4c28ebfb89464e25934767e57d48820dDirect OA link when available
- Concepts
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Control reconfiguration, Fault (geology), Computer science, Distribution (mathematics), Reliability engineering, Geology, Engineering, Seismology, Mathematics, Embedded system, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(DG),and | 24 |
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| abstract_inverted_index.approach | 37, 153, 169 |
| abstract_inverted_index.critical | 15 |
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| abstract_inverted_index.strength | 68 |
| abstract_inverted_index.tie-line | 18 |
| abstract_inverted_index.(XGBoost) | 138 |
| abstract_inverted_index.Firstly,a | 50 |
| abstract_inverted_index.coeffects | 62 |
| abstract_inverted_index.developed | 64 |
| abstract_inverted_index.important | 176 |
| abstract_inverted_index.numerical | 141 |
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| abstract_inverted_index.switching | 19 |
| abstract_inverted_index.Chi-square | 56 |
| abstract_inverted_index.algorithm. | 139 |
| abstract_inverted_index.dimensions | 75 |
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| abstract_inverted_index.importance | 105 |
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| abstract_inverted_index.considering | 96 |
| abstract_inverted_index.correlation | 61, 71 |
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| abstract_inverted_index.integration,and | 98 |
| abstract_inverted_index.reconfiguration | 88 |
| abstract_inverted_index.disasters,active | 4 |
| abstract_inverted_index.multi-dimensional | 34 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.6100000143051147 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.16398664 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |