Non-contact power system fault diagnosis: a machine learning approach with electromagnetic current sensing
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Non-contact power system fault diagnosis: a machine learning approach with electromagnetic current sensing |
| 2. | Creator | Author's name, affiliation, country | Amit L. Nehete; Bharati Vidyapeeth (Deemed to be) University College of Engineering; India |
| 2. | Creator | Author's name, affiliation, country | Deepak S. Bankar; Bharati Vidyapeeth (Deemed to be) University College of Engineering; India |
| 2. | Creator | Author's name, affiliation, country | Ritika Asati; CoEP Technological University; India |
| 2. | Creator | Author's name, affiliation, country | Chetan Khadse; Dr. Vishwanath Karad MIT World Peace University; India |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | Computational efficiency; Electromagnetic sensing; Fault classification; Machine learning algorithms; Smart grid |
| 4. | Description | Abstract | Modern power system protection schemes incorporate artificial intelligence (AI) techniques. However, in a conventional way, most of these schemes rely on the data of current and voltage collected from current transformer (CT) and potential transformer (PT) respectively. CTs suffer from the drawback of core saturation and impact the accuracy and effectiveness of intelligent methods. Also, it has the constraints of size, safety, and economy. The research here explores the effectiveness of magnetic sensors in advanced power system protection schemes as an alternative to traditional current sensing. In the presented work, a novel dataset is prepared by transforming transmission line currents into magnetic field components. Several supervised as well as unsupervised machine learning algorithms have been applied to this data instead of traditional currents and voltage for fault prediction. The paper discusses the comparative evaluation of these algorithms based on various performance metrices which reveals that Gaussian Naïve Bayes (GNB), K-nearest neighbor (KNN), random forest (RF), and extreme gradient boost (XGB) algorithms excel in fault detection, while multilayer perceptron (MLP) and KNN performs better fault classification. The findings promise the potential for developing compact, safe, and cost-effective protection schemes utilizing magnetic field sensors. |
| 5. | Publisher | Organizing agency, location | Institute of Advanced Engineering and Science |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2024-12-01 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://ijeecs.iaescore.com/index.php/IJEECS/article/view/38969 |
| 10. | Identifier | Digital Object Identifier (DOI) | http://doi.org/10.11591/ijeecs.v36.i3.pp1356-1364 |
| 11. | Source | Title; vol., no. (year) | Indonesian Journal of Electrical Engineering and Computer Science; Vol 36, No 3: December 2024 |
| 12. | Language | English=en | en |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
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