Application of an improved k‐means clustering algorithm in power user grouping
The illegal act of stealing electricity has brought serious security risks to the operation of power transmission system. The electricity consumption law of power users has obvious characteristics, which can be classified by clustering method. However, the traditional k‐means clustering method has t...
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| Published in: | International journal of numerical modelling Vol. 35; no. 4 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Chichester, UK
John Wiley & Sons, Inc
01.07.2022
Wiley Subscription Services, Inc |
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| ISSN: | 0894-3370, 1099-1204 |
| Online Access: | Get full text |
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| Abstract | The illegal act of stealing electricity has brought serious security risks to the operation of power transmission system. The electricity consumption law of power users has obvious characteristics, which can be classified by clustering method. However, the traditional k‐means clustering method has the characteristics of randomly selecting the initial cluster center, which leads to the instability of clustering results. Aiming at the shortcomings of traditional k‐means clustering algorithm, this paper proposes a density based k‐means clustering algorithm (Dk‐means clustering) to optimize the initial center selection. According to the characteristics of users' power load, the method of determining the number of clusters and the evaluation method of clustering effect are selected. Then, the traditional k‐means clustering algorithm and the improved Dk‐means clustering algorithm are used to analyze the power load data of a residential area, and six groups of characteristic curves are obtained. Based on the analysis of these curves, the power consumption characteristics of each group of users were evaluated. Finally, through the comparative analysis of Euclidean distance, Manhattan distance and correlation coefficient r, it is proved that Dk‐means algorithm has better clustering effect and is more accurate for the selection of suspicious power stealing users. |
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| AbstractList | The illegal act of stealing electricity has brought serious security risks to the operation of power transmission system. The electricity consumption law of power users has obvious characteristics, which can be classified by clustering method. However, the traditional
k
‐means clustering method has the characteristics of randomly selecting the initial cluster center, which leads to the instability of clustering results. Aiming at the shortcomings of traditional
k
‐means clustering algorithm, this paper proposes a density based
k
‐means clustering algorithm (D
k
‐means clustering) to optimize the initial center selection. According to the characteristics of users' power load, the method of determining the number of clusters and the evaluation method of clustering effect are selected. Then, the traditional
k
‐means clustering algorithm and the improved D
k
‐means clustering algorithm are used to analyze the power load data of a residential area, and six groups of characteristic curves are obtained. Based on the analysis of these curves, the power consumption characteristics of each group of users were evaluated. Finally, through the comparative analysis of Euclidean distance, Manhattan distance and correlation coefficient r, it is proved that D
k
‐means algorithm has better clustering effect and is more accurate for the selection of suspicious power stealing users. The illegal act of stealing electricity has brought serious security risks to the operation of power transmission system. The electricity consumption law of power users has obvious characteristics, which can be classified by clustering method. However, the traditional k‐means clustering method has the characteristics of randomly selecting the initial cluster center, which leads to the instability of clustering results. Aiming at the shortcomings of traditional k‐means clustering algorithm, this paper proposes a density based k‐means clustering algorithm (Dk‐means clustering) to optimize the initial center selection. According to the characteristics of users' power load, the method of determining the number of clusters and the evaluation method of clustering effect are selected. Then, the traditional k‐means clustering algorithm and the improved Dk‐means clustering algorithm are used to analyze the power load data of a residential area, and six groups of characteristic curves are obtained. Based on the analysis of these curves, the power consumption characteristics of each group of users were evaluated. Finally, through the comparative analysis of Euclidean distance, Manhattan distance and correlation coefficient r, it is proved that Dk‐means algorithm has better clustering effect and is more accurate for the selection of suspicious power stealing users. |
| Author | Gang, Wu Dongdong, Zhang Shengrong, Fan |
| Author_xml | – sequence: 1 givenname: Wu surname: Gang fullname: Gang, Wu email: 122513613@qq.com organization: China Three Gorges University – sequence: 2 givenname: Zhang surname: Dongdong fullname: Dongdong, Zhang organization: China Three Gorges University – sequence: 3 givenname: Fan surname: Shengrong fullname: Shengrong, Fan organization: Wuhan Shengjieda Power Technology Co., Ltd |
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| References_xml | – volume: 182 issue: 9 year: 2020 article-title: Detection and identification of energy theft in advanced metering infrastructures publication-title: Electr Power Syst Res – volume: 101 start-page: 301 year: 2018 end-page: 310 article-title: Clustering‐based novelty detection for identification of non‐technical losses publication-title: Int J Electr Power Energy Syst – volume: 60 start-page: 15 issue: 1 year: 2019 end-page: 39 article-title: A hybrid model for anomalies detection in AMI system combining ‐means clustering and deep neural network publication-title: Comput Mater Continua – volume: 141 start-page: 114 year: 2016 end-page: 123 article-title: Comparison and clustering analysis of the daily electrical load in eight European countries publication-title: Electr Power Syst Res – volume: 187 year: 2020 article-title: A moving shape‐based robust fuzzy ‐modes clustering algorithm for electricity profiles publication-title: Electr Power Syst Res – volume: 93 issue: 4 year: 2021 article-title: Design, power quality analysis, and implementation of smart energy meter using internet of things publication-title: Comput Electr Eng – volume: 108 start-page: 56 year: 2018 end-page: 61 article-title: Snatch theft detection in unconstrained surveillance videos using action attribute modelling publication-title: Pattern Recogn Lett – volume: 121 year: 2020 article-title: Energy theft detection in an edge data center using threshold‐based abnormality detector publication-title: Int J Electr Power Energy Syst – volume: 13 start-page: 1 issue: 8 year: 2020 end-page: 20 article-title: Detection of electricity theft behavior based on improved synthetic minority oversampling technique and random forest classifier publication-title: Energies – year: 2020 – volume: 7 start-page: 216 issue: 1 year: 2016 end-page: 226 article-title: Electricity theft detection in AMI using customers' consumption patterns publication-title: IEEE Trans Smart Grid – volume: 15 start-page: 1809 issue: 3 year: 2019 end-page: 1819 article-title: A novel combined data‐driven approach for electricity theft detection publication-title: IEEE Trans Ind Inf – start-page: 63 year: 2014 end-page: 68 – volume: 81 start-page: 716 issue: 2 year: 2011 end-page: 724 article-title: Hopfield– ‐means clustering algorithm: a proposal for the segmentation of electricity customers publication-title: Electr Power Syst Res – year: 2017 – volume: 238 start-page: 481 year: 2019 end-page: 494 article-title: A practical feature‐engineering framework for electricity theft detection in smart grids publication-title: Appl Energy – start-page: 589 year: 2017 end-page: 592 – year: 2019 – volume: 125 issue: 3 year: 2021 article-title: Electricity theft detection in low‐voltage stations based on similarity measure and DT‐KSVM publication-title: Int J Electr Power Energy Syst – start-page: 389 year: 2017 end-page: 393 – ident: e_1_2_7_8_1 doi: 10.1016/j.compeleceng.2021.107203 – ident: e_1_2_7_6_1 doi: 10.1016/j.patrec.2018.03.004 – ident: e_1_2_7_19_1 doi: 10.1109/IWCMC48107.2020.9148301 – ident: e_1_2_7_4_1 doi: 10.1109/ICAECC.2014.7002457 – ident: e_1_2_7_16_1 doi: 10.3390/en13082039 – ident: e_1_2_7_21_1 doi: 10.1016/j.epsr.2020.106425 – ident: e_1_2_7_14_1 – ident: e_1_2_7_18_1 doi: 10.1016/j.ijepes.2020.106162 – ident: e_1_2_7_5_1 doi: 10.32604/cmc.2019.06497 – ident: e_1_2_7_10_1 doi: 10.1109/TII.2018.2873814 – ident: e_1_2_7_20_1 doi: 10.1016/j.epsr.2016.07.002 – ident: e_1_2_7_2_1 doi: 10.1016/j.epsr.2020.106258 – ident: e_1_2_7_3_1 doi: 10.1016/j.ijepes.2018.03.031 – ident: e_1_2_7_15_1 doi: 10.1109/ISGT-Asia.2017.8378347 – ident: e_1_2_7_9_1 doi: 10.1016/j.apenergy.2019.01.076 – ident: e_1_2_7_13_1 doi: 10.1109/ICNSC.2017.8000124 – ident: e_1_2_7_7_1 doi: 10.1109/TSG.2015.2425222 – ident: e_1_2_7_12_1 doi: 10.1016/j.ijepes.2020.106544 – ident: e_1_2_7_11_1 doi: 10.1109/FUZZ-IEEE.2017.8015546 – ident: e_1_2_7_22_1 doi: 10.1016/j.epsr.2010.10.036 – ident: e_1_2_7_17_1 doi: 10.1109/ICEMS.2019.8922302 |
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| SubjectTerms | Algorithms Clustering Correlation coefficients Dk‐means clustering Electrical loads Electricity consumption Electricity distribution Euclidean geometry Evaluation k‐means clustering Power consumption power load curve power user grouping Residential areas |
| Title | Application of an improved k‐means clustering algorithm in power user grouping |
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