| Title: | Most influential feature form for supervised learning in voltage sag source localization |
|---|
| Authors: | ID Mohammadi, Younes (Author)
ID Polajžer, Boštjan (Author)
ID Chouhy Leborgne, Roberto (Author)
ID Khodadad, Davood (Author) |
|---|
| Files: |  1-s2.0-S0952197624004895-main.pdf (15,94 MB)
MD5: 3DDA8D3E768F62296CBC8B106198650C
 https://www.sciencedirect.com/science/article/pii/S0952197624004895?via%3Dihub
|
|---|
| Language: | English |
|---|
| Work type: | Article |
|---|
| Typology: | 1.01 - Original Scientific Article |
|---|
| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
|
|---|
| Abstract: | The paper investigates the application of machine learning (ML) for voltage sag source localization (VSSL) in electrical power systems. To overcome feature-selection challenges for traditional ML methods and provide more meaningful sequential features for deep learning methods, the paper proposes three time-sample-based feature forms, and evaluates an existing feature form. The effectiveness of these feature forms is assessed using k-means clustering with k = 2 referred to as downstream and upstream classes, according to the direction of voltage sag origins. Through extensive voltage sag simulations, including noises in a regional electrical power network, k-means identifies a sequence involving the multiplication of positive-sequence current magnitude with the sine of its angle as the most prominent feature form. The study develops further traditional ML methods such as decision trees (DT), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), an ensemble learning (EL), and a designed one-dimensional convolutional neural network (1D-CNN). The results found that the combination of 1D-CNN or SVM with the most prominent feature achieved the highest accuracies of 99.37% and 99.13%, respectively, with acceptable/fast prediction times, enhancing VSSL. The exceptional performance of the CNN was also approved by field measurements in a real power network. However, selecting the best ML methods for deployment requires a trade-off between accuracy and real-time implementation requirements. The research findings benefit network operators, large factory owners, and renewable energy park producers. They enable preventive maintenance, reduce equipment downtime/damage in industry and electrical power systems, mitigate financial losses, and facilitate the assignment of power-quality penalties to responsible parties. |
|---|
| Keywords: | voltage sag (dip), source localization, supervised and unsupervised learning, convolutional neural network, time-sample-based features |
|---|
| Publication status: | Published |
|---|
| Publication version: | Version of Record |
|---|
| Submitted for review: | 16.01.2024 |
|---|
| Article acceptance date: | 23.03.2024 |
|---|
| Publication date: | 02.04.2024 |
|---|
| Publisher: | Elsevier Science |
|---|
| Year of publishing: | 2024 |
|---|
| Number of pages: | 29 str. |
|---|
| Numbering: | vol. 133, [article no.] 108331 |
|---|
| PID: | 20.500.12556/DKUM-90152  |
|---|
| UDC: | 621.31 |
|---|
| ISSN on article: | 1873-6769 |
|---|
| COBISS.SI-ID: | 191325699 |
|---|
| DOI: | 10.1016/j.engappai.2024.108331 |
|---|
| Copyright: | © 2024 The Author(s) |
|---|
| Publication date in DKUM: | 23.08.2024 |
|---|
| Views: | 183 |
|---|
| Downloads: | 25 |
|---|
| Metadata: |    |
|---|
| Categories: | Misc.
|
|---|
|
:
|
Copy citation |
|---|
| | | | Average score: | (0 votes) |
|---|
| Your score: | Voting is allowed only for logged in users. |
|---|
| Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
