| Autor: |
Petar Sarajcev, Dino Lovric, Tonko Garma |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Energies, Vol 15, Iss 21, p 8248 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1996-1073 |
| DOI: |
10.3390/en15218248 |
| Popis: |
This paper introduces a novel machine learning (ML) model for the lightning performance analysis of overhead distribution lines (OHLs), which facilitates a data-centrist and statistical view of the problem. The ML model is a bagging ensemble of support vector machines (SVMs), which introduces two significant features. Firstly, support vectors from the SVMs serve as a scaffolding, and at the same time give rise to the so-called curve of limiting parameters for the line. Secondly, the model itself serves as a foundation for the introduction of the statistical safety factor to the lightning performance analysis of OHLs. Both these aspects bolster an end-to-end statistical approach to the OHL insulation coordination and lightning flashover analysis. Furthermore, the ML paradigm brings the added benefit of learning from a large corpus of data amassed by the lightning location networks and fostering, in the process, a “big data” approach to this important engineering problem. Finally, a relationship between safety factor and risk is elucidated. THe benefits of the proposed approach are demonstrated on a typical medium-voltage OHL. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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