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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Bibliographic Details
Published in:Wireless communications and mobile computing Vol. 2022; no. 1
Main Authors: Li, Qingpeng, Chen, Lei, Wang, Yuhan
Format: Journal Article
Language:English
Published: Oxford Hindawi 2022
John Wiley & Sons, Inc
Subjects:
Algorithms
Cluster analysis
Clustering
Consumption
Control equipment
Data analysis
Data mining
Datasets
Density
Outliers (statistics)
Parameter sensitivity
Teaching methods
ISSN:1530-8669, 1530-8677
Online Access:Get full text
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Summary: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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ISSN:1530-8669
1530-8677
DOI:10.1155/2022/2203137