Outlier detection and data filling based on KNN and LOF for power transformer operation data classification Article Swipe
Dexu Zou
,
Yongjian Xiang
,
Tao Zhou
,
Qingjun Peng
,
Weiju Dai
,
Zhihu Hong
,
Yong Shi
,
Shan Wang
,
Jian‐Hua Yin
,
Quan Hao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.egyr.2023.04.094
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.egyr.2023.04.094
The missing and abnormal data in power transformer operation and monitoring greatly affect the accuracy of fault diagnosis and thus threaten the stable operation of power systems. To conduct outlier detection and improve data quality for safety warning, this paper proposes a transformer operation data preprocessing method based on KNN (K-nearest neighbor) and LOF (local outlier factor) for power transformer operation data classification. Firstly, this paper analyzes the characteristics of transformer operation data. Secondly, the local reachable density of the input data is calculated by LOF algorithm. The local outlier factor score of the data is derived according to the local reachable density, and the abnormal data is output according to the abnormal score. Then, KNN algorithm is utilized to classify the relevant data around the abnormal value and missing value of the transformer. The data are filled or corrected according to the classification results. Thirdly, the elbow method is used to determine the optimal K value and cluster operation data by K-Means algorithm. Finally, the proposed method is applied and verified with real transformer operation data in case study. The results show the method can effectively detect and correct the abnormal and missing data, conduct transformer data cleaning and preprocessing and provide accurate and effective data samples for transformer fault diagnosis.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.egyr.2023.04.094
- OA Status
- gold
- Cited By
- 44
- References
- 9
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4366591145
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4366591145Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.egyr.2023.04.094Digital Object Identifier
- Title
-
Outlier detection and data filling based on KNN and LOF for power transformer operation data classificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-20Full publication date if available
- Authors
-
Dexu Zou, Yongjian Xiang, Tao Zhou, Qingjun Peng, Weiju Dai, Zhihu Hong, Yong Shi, Shan Wang, Jian‐Hua Yin, Quan HaoList of authors in order
- Landing page
-
https://doi.org/10.1016/j.egyr.2023.04.094Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.egyr.2023.04.094Direct OA link when available
- Concepts
-
Local outlier factor, Outlier, Computer science, Data pre-processing, Data mining, Anomaly detection, Preprocessor, Missing data, Transformer, Pattern recognition (psychology), Artificial intelligence, Engineering, Machine learning, Voltage, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
44Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 23, 2024: 16, 2023: 5Per-year citation counts (last 5 years)
- References (count)
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9Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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