Detection of Power Data Outliers Using Density Peaks Clustering Algorithm Based on K-Nearest Neighbors
As an important research branch in data mining, outlier detection has been widely used in equipment operation monitoring and system operation control. Power data outlier detection is playing an increasingly vital role in power systems. Density peak clustering (DPC) is a simple and efficient density-...
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| Vydané v: | Wireless communications and mobile computing Ročník 2022; číslo 1 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Oxford
Hindawi
2022
John Wiley & Sons, Inc |
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| ISSN: | 1530-8669, 1530-8677 |
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| Abstract | As an important research branch in data mining, outlier detection has been widely used in equipment operation monitoring and system operation control. Power data outlier detection is playing an increasingly vital role in power systems. Density peak clustering (DPC) is a simple and efficient density-based clustering algorithm with a good application prospect. Nevertheless, the clustering results by the DPC algorithm can be greatly influenced by the cutoff distance, indicating that the results are highly sensitive to this parameter. To address the shortcomings of the DPC algorithm and take the characteristics of power data into consideration, we propose a DPC algorithm based on K-nearest neighbors for the detection of power data outliers. The proposed DPC algorithm introduces the idea of K-nearest neighbors and uses a unified definition of local density. In the DPC algorithm, only one parameter (K) needs to be determined, thus eliminating the influence of cutoff distance on the clustering result of the algorithm. The experimental results showed that the proposed algorithm can achieve accurate detection of power data outliers and has broad application prospects. |
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| AbstractList | As an important research branch in data mining, outlier detection has been widely used in equipment operation monitoring and system operation control. Power data outlier detection is playing an increasingly vital role in power systems. Density peak clustering (DPC) is a simple and efficient density-based clustering algorithm with a good application prospect. Nevertheless, the clustering results by the DPC algorithm can be greatly influenced by the cutoff distance, indicating that the results are highly sensitive to this parameter. To address the shortcomings of the DPC algorithm and take the characteristics of power data into consideration, we propose a DPC algorithm based on K-nearest neighbors for the detection of power data outliers. The proposed DPC algorithm introduces the idea of K-nearest neighbors and uses a unified definition of local density. In the DPC algorithm, only one parameter (K) needs to be determined, thus eliminating the influence of cutoff distance on the clustering result of the algorithm. The experimental results showed that the proposed algorithm can achieve accurate detection of power data outliers and has broad application prospects. |
| Author | Wang, Yuhan Li, Qingpeng Chen, Lei |
| Author_xml | – sequence: 1 givenname: Qingpeng surname: Li fullname: Li, Qingpeng organization: Nanchang Power Supply CompanyState Grid Jiangxi Electric Power CompanyNanchang 330200China – sequence: 2 givenname: Lei orcidid: 0000-0002-2658-2067 surname: Chen fullname: Chen, Lei organization: School of Information EngineeringNanchang Institute of TechnologyNanchang 330099Chinanit.edu.cn – sequence: 3 givenname: Yuhan surname: Wang fullname: Wang, Yuhan organization: Nanchang Power Supply CompanyState Grid Jiangxi Electric Power CompanyNanchang 330200China |
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| Cites_doi | 10.1504/IJBIC.2021.113363 10.1109/TII.2018.2873814 10.1007/s00779-016-0954-4 10.1023/A:1009745219419 10.1007/978-3-319-13563-2_27 10.1016/j.energy.2021.122333 10.1007/s00138-013-0516-y 10.1016/j.epsr.2015.03.004 10.1016/j.rcl.2021.07.008 |
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| Copyright | Copyright © 2022 Qingpeng Li et al. Copyright © 2022 Qingpeng Li et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| SubjectTerms | Algorithms Cluster analysis Clustering Consumption Control equipment Data analysis Data mining Datasets Density Outliers (statistics) Parameter sensitivity Teaching methods |
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| Title | Detection of Power Data Outliers Using Density Peaks Clustering Algorithm Based on K-Nearest Neighbors |
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