dc.contributor.author |
Madhushan, PN |
|
dc.contributor.author |
Pabasara, WMN |
|
dc.contributor.author |
Shihab, M |
|
dc.contributor.author |
Gunawardana, M |
|
dc.date.accessioned |
2024-07-19T04:11:12Z |
|
dc.date.available |
2024-07-19T04:11:12Z |
|
dc.date.issued |
2023-12 |
|
dc.identifier.uri |
http://dl.lib.uom.lk/handle/123/22578 |
|
dc.description.abstract |
Grounding is one of the most important parts of an electrical system. Earthing systems are done to protect the power system and the personnel from the danger of electrical shocks. Ceylon Electricity Board (CEB) uses a special structure for transformer earthing arrangement. The used structure is copper bonded earth rod with a concrete filled steel cage.
Due to the complexity of the structure and the nonlinear variations in soil parameters, it is challenging to determine resistance before implementing the structure.
We can use an analytical formula for structures to find the resistance. [4] But for complex structure, as we use here, it is challenging to produce an analytical formula. The other solution is to use a Finite Element Method(FEM) to solve the problem.[2] But is also a time-consuming task.[1] So, we propose a combination of FEM and a neural network-based solution for this task.[1]. We propose to generate a data set using FEM and implement it in a neural network. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Engineering Research Unit |
en_US |
dc.subject |
grounding structure |
en_US |
dc.subject |
COMSOL |
en_US |
dc.subject |
Finite Element Method (FEM) |
en_US |
dc.subject |
neural network |
en_US |
dc.title |
Neural network based model for estimating the resistance of outdoor distribution substation grounding |
en_US |
dc.type |
Conference-Extended-Abstract |
en_US |
dc.identifier.faculty |
Engineering |
en_US |
dc.identifier.department |
Department of Electrical Engineering |
en_US |
dc.identifier.year |
2023 |
en_US |
dc.identifier.conference |
ERU Symposium - 2023 |
en_US |
dc.identifier.place |
Sri Lanka |
en_US |
dc.identifier.pgnos |
pp. 34-35 |
en_US |
dc.identifier.proceeding |
Proceedings of the ERU Symposium 2023 |
en_US |
dc.identifier.email |
nipunmadushan1030@gmail.com |
en_US |
dc.identifier.email |
nandun.pabasara@gmail.com |
en_US |
dc.identifier.email |
mohamedshihab2018@gmail.com |
en_US |
dc.identifier.email |
manujag@uom.lk |
en_US |
dc.identifier.doi |
https://doi.org/10.31705/ERU.2023.16 |
en_US |