Details
| Original language | English |
|---|---|
| Title of host publication | CANDO-EPE 2023 - Proceedings |
| Subtitle of host publication | IEEE 6th International Conference and Workshop Obuda on Electrical and Power Engineering |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 237-242 |
| Number of pages | 6 |
| ISBN (electronic) | 9798350328752 |
| ISBN (print) | 979-8-3503-2876-9 |
| Publication status | Published - 2024 |
| Event | 6th IEEE International Conference and Workshop Obuda on Electrical and Power Engineering, CANDO-EPE 2023 - Budapest, Hungary Duration: 19 Oct 2023 → 20 Oct 2023 |
Publication series
| Name | Proceedings: IEEE 6th International Conference and Workshop Obuda on Electrical and Power Engineering |
|---|---|
| ISSN (Print) | 2831-4492 |
| ISSN (electronic) | 2831-4506 |
Abstract
As simulation studies play a significant role in the development of gas turbine plants and their control systems, it is important to validate their results and adapt the data from real plants. In this paper, two examples are presented on how the interaction between real plant data and the corresponding models can be used efficiently. The first example shows that the accuracy in simulating a load rejection event can be improved significantly by using real world data for identification of model parameters. Instead of developing simplified models as presented in related work, a detailed existing model is object of this identification. For the second example, the opposite direction is illustrated: to possibly support the commissioning progress, a reduced model is utilized to optimize the parameters defining the primary frequency response of a gas turbine plant. The same black-box optimization algorithm is used and its capability to perform different optimization tasks is shown.
Keywords
- Black-box Optimization, Gas Turbine, Model Identification, Over-speed Analysis, Primary Frequency Control
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Mathematics(all)
- Control and Optimization
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Software
- Decision Sciences(all)
- Information Systems and Management
- Energy(all)
- Energy Engineering and Power Technology
Cite this
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- BibTeX
- RIS
CANDO-EPE 2023 - Proceedings: IEEE 6th International Conference and Workshop Obuda on Electrical and Power Engineering. Institute of Electrical and Electronics Engineers Inc., 2024. p. 237-242 (Proceedings: IEEE 6th International Conference and Workshop Obuda on Electrical and Power Engineering).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Efficient Identification and Tuning of Gas Turbine Models Using Black-Box Optimization
AU - Peters, Lukas
AU - Schafer, Marc
AU - Kastner, Tim Cedrik
AU - Kutzner, Rudiger
AU - Lutz Hofmann, Habil
N1 - Publisher Copyright: © 2023 IEEE.
PY - 2024
Y1 - 2024
N2 - As simulation studies play a significant role in the development of gas turbine plants and their control systems, it is important to validate their results and adapt the data from real plants. In this paper, two examples are presented on how the interaction between real plant data and the corresponding models can be used efficiently. The first example shows that the accuracy in simulating a load rejection event can be improved significantly by using real world data for identification of model parameters. Instead of developing simplified models as presented in related work, a detailed existing model is object of this identification. For the second example, the opposite direction is illustrated: to possibly support the commissioning progress, a reduced model is utilized to optimize the parameters defining the primary frequency response of a gas turbine plant. The same black-box optimization algorithm is used and its capability to perform different optimization tasks is shown.
AB - As simulation studies play a significant role in the development of gas turbine plants and their control systems, it is important to validate their results and adapt the data from real plants. In this paper, two examples are presented on how the interaction between real plant data and the corresponding models can be used efficiently. The first example shows that the accuracy in simulating a load rejection event can be improved significantly by using real world data for identification of model parameters. Instead of developing simplified models as presented in related work, a detailed existing model is object of this identification. For the second example, the opposite direction is illustrated: to possibly support the commissioning progress, a reduced model is utilized to optimize the parameters defining the primary frequency response of a gas turbine plant. The same black-box optimization algorithm is used and its capability to perform different optimization tasks is shown.
KW - Black-box Optimization
KW - Gas Turbine
KW - Model Identification
KW - Over-speed Analysis
KW - Primary Frequency Control
UR - http://www.scopus.com/inward/record.url?scp=85185707534&partnerID=8YFLogxK
U2 - 10.1109/CANDO-EPE60507.2023.10418001
DO - 10.1109/CANDO-EPE60507.2023.10418001
M3 - Conference contribution
AN - SCOPUS:85185707534
SN - 979-8-3503-2876-9
T3 - Proceedings: IEEE 6th International Conference and Workshop Obuda on Electrical and Power Engineering
SP - 237
EP - 242
BT - CANDO-EPE 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference and Workshop Obuda on Electrical and Power Engineering, CANDO-EPE 2023
Y2 - 19 October 2023 through 20 October 2023
ER -