- Tytuł:
- Prediction of Kaplan turbine coordination tests based on least squares support vector machine with an improved grey wolf optimization algorithm
- Autorzy:
-
Kong, Fannie
Xia, Jiahui
Yang, Daliang
Luo, Ming - Powiązania:
- https://bibliotekanauki.pl/articles/2173627.pdf
- Data publikacji:
- 2021
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
Kaplan turbine
coordination tests
least squares support vector machine
improved grey wolf optimization
turbina Kaplana
test koordynacyjny
metoda najmniejszych kwadratów
ulepszona optymalizacja szarego wilka - Opis:
- The optimum combination of blade angle of the runner and guide vane opening with Kaplan turbine can improve the hydroelectric generating the set operation efficiency and the suppression capability of oscillations. Due to time and cost limitations and the complex operation mechanism of the Kaplan turbine, the coordination test data is insufficient, making it challenging to obtain the whole curves at each head under the optimum coordination operation by field tests. The field test data is employed to propose a least-squares support vector machine (LSSVM)-based prediction model for Kaplan turbine coordination tests. Considering the small sample characteristics of the test data of Kaplan turbine coordination, the LSSVM parameters are optimized by an improved grey wolf optimization (IGWO) algorithm with mixed non-linear factors and static weights. The grey wolf optimization (GWO) algorithm has some deficiencies, such as the linear convergence factor, which inaccurately simulates the actual situation, and updating the position indeterminately reflects the absolute leadership of the leader wolf. The IGWO algorithm is employed to overcome the aforementioned problems. The prediction model is simulated to verify the effectiveness of the proposed IGWO-LSSVM. The results show high accuracy with small samples, a 2.59% relative error in coordination tests, and less than 1.85% relative error in non-coordination tests under different heads.
- Źródło:
-
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e137124
0239-7528 - Pojawia się w:
- Bulletin of the Polish Academy of Sciences. Technical Sciences
- Dostawca treści:
- Biblioteka Nauki
Artykuł