Defect Prediction for Capacitive Equipment in Power System Article Swipe
Qingjun Peng
,
Zezhong Zheng
,
Hao Hu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14051968
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14051968
As a core component of the smart grid, capacitive equipment plays a critical role in modern power systems. When defects occur, they pose a significant threat to the safety of both other equipment and personnel. Hence, it is of great significance to predict whether defects occur in capacitive equipment in advance. To achieve this goal, we propose a novel method that integrates the weight of evidence (WOE) feature encoding with machine learning (ML). Five models, including support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron (MLP), and linear classification, are employed with WOE features for defect prediction. Furthermore, based on the prediction of equipment with defects, an additional prediction is conducted to determine the potential defect level of the equipment. Experimental results demonstrate that the performance of each algorithm significantly improves with WOE encoding features. Particularly, the RF model with WOE encoding features exhibits optimal performance. In conclusion, the proposed method offers a promising solution for predicting the occurrence of defects and the corresponding defect levels of capacitive equipment. It enables relevant personnel to focus on and inspect equipment predicted to be at risk of defects, thereby preventing major malfunctions.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app14051968
- https://www.mdpi.com/2076-3417/14/5/1968/pdf?version=1709107871
- OA Status
- gold
- Cited By
- 2
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392238633
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392238633Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app14051968Digital Object Identifier
- Title
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Defect Prediction for Capacitive Equipment in Power SystemWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-02-28Full publication date if available
- Authors
-
Qingjun Peng, Zezhong Zheng, Hao HuList of authors in order
- Landing page
-
https://doi.org/10.3390/app14051968Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/14/5/1968/pdf?version=1709107871Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2076-3417/14/5/1968/pdf?version=1709107871Direct OA link when available
- Concepts
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Electrical engineering, Materials science, Computer science, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
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40Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.predicted | 183 |
| abstract_inverted_index.promising | 157 |
| abstract_inverted_index.(XGBoost), | 86 |
| abstract_inverted_index.additional | 111 |
| abstract_inverted_index.capacitive | 8, 47, 171 |
| abstract_inverted_index.equipment. | 123, 172 |
| abstract_inverted_index.integrates | 61 |
| abstract_inverted_index.occurrence | 162 |
| abstract_inverted_index.perceptron | 88 |
| abstract_inverted_index.personnel. | 34 |
| abstract_inverted_index.predicting | 160 |
| abstract_inverted_index.prediction | 105, 112 |
| abstract_inverted_index.preventing | 191 |
| abstract_inverted_index.conclusion, | 151 |
| abstract_inverted_index.demonstrate | 126 |
| abstract_inverted_index.multi-layer | 87 |
| abstract_inverted_index.performance | 129 |
| abstract_inverted_index.prediction. | 100 |
| abstract_inverted_index.significant | 24 |
| abstract_inverted_index.Experimental | 124 |
| abstract_inverted_index.Furthermore, | 101 |
| abstract_inverted_index.performance. | 149 |
| abstract_inverted_index.significance | 40 |
| abstract_inverted_index.Particularly, | 139 |
| abstract_inverted_index.corresponding | 167 |
| abstract_inverted_index.malfunctions. | 193 |
| abstract_inverted_index.significantly | 133 |
| abstract_inverted_index.classification, | 92 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5083627327 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I150229711 |
| citation_normalized_percentile.value | 0.60790493 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |