Improving Electricity Theft Detection using Combination of Improved Crow Search Algorithm and Support Vector Machine
Energy losses in the electricity distribution and transmission network and electricity theft detection are major challenges of electricity suppliers around the world. Advanced metering infrastructure (AMI) is an essential segment of the smart grids that is responsible for gathering, measuring and an...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
OICC Press
2021-12-01
|
| Series: | Majlesi Journal of Electrical Engineering |
| Subjects: | |
| Online Access: | https://oiccpress.com/mjee/article/view/4933 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849429449912614912 |
|---|---|
| author | Hassan Ghaedi Seyed Reza Kamel Tabbakh Reza Ghaemi |
| author_facet | Hassan Ghaedi Seyed Reza Kamel Tabbakh Reza Ghaemi |
| author_sort | Hassan Ghaedi |
| collection | DOAJ |
| description | Energy losses in the electricity distribution and transmission network and electricity theft detection are major challenges of electricity suppliers around the world. Advanced metering infrastructure (AMI) is an essential segment of the smart grids that is responsible for gathering, measuring and analyzing consuming data of customers. The addition of a security layer to AMI has paved the way for the electricity theft in new ways. The analysis of consumed data related to users is one of the essential resources to identify electricity thieves. In this paper, the crow search algorithm (CSA) is improved and the factors of weight (w ) and awareness probability (AP ) are obtained dynamically and used to adjust the parameters c and γ of support vector machine (SVM). The results illustrate that the ICSA-SVM framework has acceptable performance and detects fraudulent customers with a high accuracy. |
| format | Article |
| id | doaj-art-c9ffc6e2ac194fa48b1d07645cef2790 |
| institution | Kabale University |
| issn | 2345-377X 2345-3796 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | OICC Press |
| record_format | Article |
| series | Majlesi Journal of Electrical Engineering |
| spelling | doaj-art-c9ffc6e2ac194fa48b1d07645cef27902025-08-20T03:28:21ZengOICC PressMajlesi Journal of Electrical Engineering2345-377X2345-37962021-12-0115410.52547/mjee.15.4.63Improving Electricity Theft Detection using Combination of Improved Crow Search Algorithm and Support Vector MachineHassan GhaediSeyed Reza Kamel TabbakhReza GhaemiEnergy losses in the electricity distribution and transmission network and electricity theft detection are major challenges of electricity suppliers around the world. Advanced metering infrastructure (AMI) is an essential segment of the smart grids that is responsible for gathering, measuring and analyzing consuming data of customers. The addition of a security layer to AMI has paved the way for the electricity theft in new ways. The analysis of consumed data related to users is one of the essential resources to identify electricity thieves. In this paper, the crow search algorithm (CSA) is improved and the factors of weight (w ) and awareness probability (AP ) are obtained dynamically and used to adjust the parameters c and γ of support vector machine (SVM). The results illustrate that the ICSA-SVM framework has acceptable performance and detects fraudulent customers with a high accuracy.https://oiccpress.com/mjee/article/view/4933ClassificationCrow Search Algorithm (CSA).Data miningSmart gridTheft Detection |
| spellingShingle | Hassan Ghaedi Seyed Reza Kamel Tabbakh Reza Ghaemi Improving Electricity Theft Detection using Combination of Improved Crow Search Algorithm and Support Vector Machine Majlesi Journal of Electrical Engineering Classification Crow Search Algorithm (CSA). Data mining Smart grid Theft Detection |
| title | Improving Electricity Theft Detection using Combination of Improved Crow Search Algorithm and Support Vector Machine |
| title_full | Improving Electricity Theft Detection using Combination of Improved Crow Search Algorithm and Support Vector Machine |
| title_fullStr | Improving Electricity Theft Detection using Combination of Improved Crow Search Algorithm and Support Vector Machine |
| title_full_unstemmed | Improving Electricity Theft Detection using Combination of Improved Crow Search Algorithm and Support Vector Machine |
| title_short | Improving Electricity Theft Detection using Combination of Improved Crow Search Algorithm and Support Vector Machine |
| title_sort | improving electricity theft detection using combination of improved crow search algorithm and support vector machine |
| topic | Classification Crow Search Algorithm (CSA). Data mining Smart grid Theft Detection |
| url | https://oiccpress.com/mjee/article/view/4933 |
| work_keys_str_mv | AT hassanghaedi improvingelectricitytheftdetectionusingcombinationofimprovedcrowsearchalgorithmandsupportvectormachine AT seyedrezakameltabbakh improvingelectricitytheftdetectionusingcombinationofimprovedcrowsearchalgorithmandsupportvectormachine AT rezaghaemi improvingelectricitytheftdetectionusingcombinationofimprovedcrowsearchalgorithmandsupportvectormachine |