An improved-MPGA and its application in OTSG load reduction characteristics optimization. (January 2023)
- Record Type:
- Journal Article
- Title:
- An improved-MPGA and its application in OTSG load reduction characteristics optimization. (January 2023)
- Main Title:
- An improved-MPGA and its application in OTSG load reduction characteristics optimization
- Authors:
- Xu, Yifan
Peng, Minjun
Xia, Genglei - Abstract:
- Highlights: The once through steam generator adopting the operation design scheme has poor load following performance under rapid load reduction. The initial population generation method and population genetic strategy in multi-population genetic algorithm (MPGA) are ameliorated. The improved-MPGA is applied to the optimization of OTSG load reduction characteristics. The optimal setting of primary coolant average temperature and steam pressure that can improve OTSG's dynamic characteristics is found. Abstract: Multi-population genetic algorithm (MPGA) perfectly inherits the advantages of standard genetic algorithm (SGA), and improves the global search performance of SGA by introducing multiple populations. However, its optimization results depend on the initial solution and the search ability is weakened in the later calculation process. To overcome these disadvantages, an initial population generation method based on the idea of cluster analysis was improved to improve the diversity of the population. To balance global exploration ability and local search ability, different crossover operation values and different mutation operation values were assigned to different populations. The algorithm is tested by using classical functions of different dimensions and the results show that the improved algorithm has better robustness. What's more, the Improved-MPGA is introduced to optimize parameters set in the reactor design control scheme. Parameters that can meet the requirementsHighlights: The once through steam generator adopting the operation design scheme has poor load following performance under rapid load reduction. The initial population generation method and population genetic strategy in multi-population genetic algorithm (MPGA) are ameliorated. The improved-MPGA is applied to the optimization of OTSG load reduction characteristics. The optimal setting of primary coolant average temperature and steam pressure that can improve OTSG's dynamic characteristics is found. Abstract: Multi-population genetic algorithm (MPGA) perfectly inherits the advantages of standard genetic algorithm (SGA), and improves the global search performance of SGA by introducing multiple populations. However, its optimization results depend on the initial solution and the search ability is weakened in the later calculation process. To overcome these disadvantages, an initial population generation method based on the idea of cluster analysis was improved to improve the diversity of the population. To balance global exploration ability and local search ability, different crossover operation values and different mutation operation values were assigned to different populations. The algorithm is tested by using classical functions of different dimensions and the results show that the improved algorithm has better robustness. What's more, the Improved-MPGA is introduced to optimize parameters set in the reactor design control scheme. Parameters that can meet the requirements of reactor power overshoot and dimensionless evaluation index are obtained. And the results show that the load reduction characteristics of the Once-through Steam Generator (OTSG) under the optimal scheme are well improved compared with the design scheme. … (more)
- Is Part Of:
- Annals of nuclear energy. Volume 180(2023)
- Journal:
- Annals of nuclear energy
- Issue:
- Volume 180(2023)
- Issue Display:
- Volume 180, Issue 2023 (2023)
- Year:
- 2023
- Volume:
- 180
- Issue:
- 2023
- Issue Sort Value:
- 2023-0180-2023-0000
- Page Start:
- Page End:
- Publication Date:
- 2023-01
- Subjects:
- Optimization -- MPGA -- Load reduction characteristics -- Once-through steam generator
Nuclear energy -- Periodicals
Nuclear engineering -- Periodicals
621.4805 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03064549 ↗
http://catalog.hathitrust.org/api/volumes/oclc/2243298.html ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.anucene.2022.109461 ↗
- Languages:
- English
- ISSNs:
- 0306-4549
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 1043.150000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 24140.xml