Gas Turbine Model Parameter Classification during Abnormal Operation Using Support Vector Machine for Maintenance
Electric power transmission; Gas turbines; Governors; Support vector machines; Abnormal operation; Gas turbine generators; Gas turbine modeling; Generator systems; MATLAB/ SIMULINK; Modeling parameters; Problem recognition; Simulink; Support vectors machine; Transmission systems; Simulink
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Institute of Physics
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.uniten.dspace-27123 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-271232023-05-29T17:39:52Z Gas Turbine Model Parameter Classification during Abnormal Operation Using Support Vector Machine for Maintenance Yaw C.T. Yap K.S. Koh S.P. Tiong S.K. Ali K. Low F.W. 36560884300 24448864400 57883863700 15128307800 36130958600 56513524700 Electric power transmission; Gas turbines; Governors; Support vector machines; Abnormal operation; Gas turbine generators; Gas turbine modeling; Generator systems; MATLAB/ SIMULINK; Modeling parameters; Problem recognition; Simulink; Support vectors machine; Transmission systems; Simulink When there is a contingency in a major transmission system, it is crucial to locate and detect abnormal parameters using an accurately modeled gas turbine (GT) generator. In this paper, a new method was proposed to model, a GT generator system. Firstly, MATLAB/Simulink was used to rebuild GT-based model which was validated using a model in PSS/E. Secondly, an artificial intelligence (AI) based approach, namely Support Vector Machine (SVM) was used for problem recognition in GT power plant. The results showed that, under steady-state, the electrical power of the rebuilt GT model was the same as the model in PSS/E. The advantage of having the MATLAB / Simulink GT model was the ability to visually observe the output from each block which was not possible in PSS/E. In addition, the average training accuracy was over 90% for the detection of GT governor parameter during abnormal behavior. As an implication, the developed model should be considered to apply in real-world grids for operation engineers to detect abnormal governor parameters in GT in the next stage. In turn, it assists in the restoration of GT to its operating condition and minimizes troubleshooting time. � Published under licence by IOP Publishing Ltd. Final 2023-05-29T09:39:52Z 2023-05-29T09:39:52Z 2022 Conference Paper 10.1088/1742-6596/2319/1/012004 2-s2.0-85137699418 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137699418&doi=10.1088%2f1742-6596%2f2319%2f1%2f012004&partnerID=40&md5=83587f0d012833f19e34143b5db3e677 https://irepository.uniten.edu.my/handle/123456789/27123 2319 1 12004 All Open Access, Gold Institute of Physics Scopus |
| institution |
Universiti Tenaga Nasional |
| building |
UNITEN Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Tenaga Nasional |
| content_source |
UNITEN Institutional Repository |
| url_provider |
http://dspace.uniten.edu.my/ |
| description |
Electric power transmission; Gas turbines; Governors; Support vector machines; Abnormal operation; Gas turbine generators; Gas turbine modeling; Generator systems; MATLAB/ SIMULINK; Modeling parameters; Problem recognition; Simulink; Support vectors machine; Transmission systems; Simulink |
| author2 |
36560884300 |
| author_facet |
36560884300 Yaw C.T. Yap K.S. Koh S.P. Tiong S.K. Ali K. Low F.W. |
| format |
Conference Paper |
| author |
Yaw C.T. Yap K.S. Koh S.P. Tiong S.K. Ali K. Low F.W. |
| spellingShingle |
Yaw C.T. Yap K.S. Koh S.P. Tiong S.K. Ali K. Low F.W. Gas Turbine Model Parameter Classification during Abnormal Operation Using Support Vector Machine for Maintenance |
| author_sort |
Yaw C.T. |
| title |
Gas Turbine Model Parameter Classification during Abnormal Operation Using Support Vector Machine for Maintenance |
| title_short |
Gas Turbine Model Parameter Classification during Abnormal Operation Using Support Vector Machine for Maintenance |
| title_full |
Gas Turbine Model Parameter Classification during Abnormal Operation Using Support Vector Machine for Maintenance |
| title_fullStr |
Gas Turbine Model Parameter Classification during Abnormal Operation Using Support Vector Machine for Maintenance |
| title_full_unstemmed |
Gas Turbine Model Parameter Classification during Abnormal Operation Using Support Vector Machine for Maintenance |
| title_sort |
gas turbine model parameter classification during abnormal operation using support vector machine for maintenance |
| publisher |
Institute of Physics |
| publishDate |
2023 |
| _version_ |
1806428506047905792 |
| score |
13.214268 |