Details
| Original language | English |
|---|---|
| Pages (from-to) | 292-302 |
| Number of pages | 11 |
| Journal | Acta Mechanica et Automatica |
| Volume | 17 |
| Issue number | 2 |
| Early online date | 10 May 2023 |
| Publication status | Published - Jun 2023 |
Abstract
In this study, the potential of the so-called black-box optimisation (BBO) to increase the efficiency of simulation studies in power engineering is evaluated. Three algorithms ("Multilevel Coordinate Search"(MCS) and "Stable Noisy Optimization by Branch and Fit"(SNOBFIT) by Huyer and Neumaier and "blackbox: A Procedure for Parallel Optimization of Expensive Black-box Functions"(blackbox) by Knysh and Korkolis) are implemented in MATLAB and compared for solving two use cases: the analysis of the maximum rotational speed of a gas turbine after a load rejection and the identification of transfer function parameters by measurements. The first use case has a high computational cost, whereas the second use case is computationally cheap. For each run of the algorithms, the accuracy of the found solution and the number of simulations or function evaluations needed to determine the optimum and the overall runtime are used to identify the potential of the algorithms in comparison to currently used methods. All methods provide solutions for potential optima that are at least 99.8% accurate compared to the reference methods. The number of evaluations of the objective functions differs significantly but cannot be directly compared as only the SNOBFIT algorithm does stop when the found solution does not improve further, whereas the other algorithms use a predefined number of function evaluations. Therefore, SNOBFIT has the shortest runtime for both examples. For computationally expensive simulations, it is shown that parallelisation of the function evaluations (SNOBFIT and blackbox) and quantisation of the input variables (SNOBFIT) are essential for the algorithmic performance. For the gas turbine overspeed analysis, only SNOBFIT can compete with the reference procedure concerning the runtime. Further studies will have to investigate whether the quantisation of input variables can be applied to other algorithms and whether the BBO algorithms can outperform the reference methods for problems with a higher dimensionality.
Keywords
- black-box optimisation, derivative-free optimisation, power plant engineering, simulation studies in power engineering
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Mechanical Engineering
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In: Acta Mechanica et Automatica, Vol. 17, No. 2, 06.2023, p. 292-302.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Ability of Black-Box Optimisation to Efficiently Perform Simulation Studies in Power Engineering
AU - Peters, Lukas
AU - Kutzner, Rüdiger
AU - Schäfer, Marc
AU - Hofmann, Lutz
PY - 2023/6
Y1 - 2023/6
N2 - In this study, the potential of the so-called black-box optimisation (BBO) to increase the efficiency of simulation studies in power engineering is evaluated. Three algorithms ("Multilevel Coordinate Search"(MCS) and "Stable Noisy Optimization by Branch and Fit"(SNOBFIT) by Huyer and Neumaier and "blackbox: A Procedure for Parallel Optimization of Expensive Black-box Functions"(blackbox) by Knysh and Korkolis) are implemented in MATLAB and compared for solving two use cases: the analysis of the maximum rotational speed of a gas turbine after a load rejection and the identification of transfer function parameters by measurements. The first use case has a high computational cost, whereas the second use case is computationally cheap. For each run of the algorithms, the accuracy of the found solution and the number of simulations or function evaluations needed to determine the optimum and the overall runtime are used to identify the potential of the algorithms in comparison to currently used methods. All methods provide solutions for potential optima that are at least 99.8% accurate compared to the reference methods. The number of evaluations of the objective functions differs significantly but cannot be directly compared as only the SNOBFIT algorithm does stop when the found solution does not improve further, whereas the other algorithms use a predefined number of function evaluations. Therefore, SNOBFIT has the shortest runtime for both examples. For computationally expensive simulations, it is shown that parallelisation of the function evaluations (SNOBFIT and blackbox) and quantisation of the input variables (SNOBFIT) are essential for the algorithmic performance. For the gas turbine overspeed analysis, only SNOBFIT can compete with the reference procedure concerning the runtime. Further studies will have to investigate whether the quantisation of input variables can be applied to other algorithms and whether the BBO algorithms can outperform the reference methods for problems with a higher dimensionality.
AB - In this study, the potential of the so-called black-box optimisation (BBO) to increase the efficiency of simulation studies in power engineering is evaluated. Three algorithms ("Multilevel Coordinate Search"(MCS) and "Stable Noisy Optimization by Branch and Fit"(SNOBFIT) by Huyer and Neumaier and "blackbox: A Procedure for Parallel Optimization of Expensive Black-box Functions"(blackbox) by Knysh and Korkolis) are implemented in MATLAB and compared for solving two use cases: the analysis of the maximum rotational speed of a gas turbine after a load rejection and the identification of transfer function parameters by measurements. The first use case has a high computational cost, whereas the second use case is computationally cheap. For each run of the algorithms, the accuracy of the found solution and the number of simulations or function evaluations needed to determine the optimum and the overall runtime are used to identify the potential of the algorithms in comparison to currently used methods. All methods provide solutions for potential optima that are at least 99.8% accurate compared to the reference methods. The number of evaluations of the objective functions differs significantly but cannot be directly compared as only the SNOBFIT algorithm does stop when the found solution does not improve further, whereas the other algorithms use a predefined number of function evaluations. Therefore, SNOBFIT has the shortest runtime for both examples. For computationally expensive simulations, it is shown that parallelisation of the function evaluations (SNOBFIT and blackbox) and quantisation of the input variables (SNOBFIT) are essential for the algorithmic performance. For the gas turbine overspeed analysis, only SNOBFIT can compete with the reference procedure concerning the runtime. Further studies will have to investigate whether the quantisation of input variables can be applied to other algorithms and whether the BBO algorithms can outperform the reference methods for problems with a higher dimensionality.
KW - black-box optimisation
KW - derivative-free optimisation
KW - power plant engineering
KW - simulation studies in power engineering
UR - http://www.scopus.com/inward/record.url?scp=85159293164&partnerID=8YFLogxK
U2 - 10.2478/ama-2023-0034
DO - 10.2478/ama-2023-0034
M3 - Article
AN - SCOPUS:85159293164
VL - 17
SP - 292
EP - 302
JO - Acta Mechanica et Automatica
JF - Acta Mechanica et Automatica
SN - 1898-4088
IS - 2
ER -