Parameter identification and uncertainty quantification of a non‐linear pump‐turbine governing system based on the differential evolution adaptive Metropolis algorithm
Abstract Pumped storage units (PSUs) are now widely used for energy storage. However, the uncertainty of the identification results of the pump‐turbine governing system (PTGS) caused by the random observation noises and the lack of prior knowledge remains an unaddressed issue. In recent years, the d...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-02-01
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| Series: | IET Renewable Power Generation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rpg2.12027 |
| _version_ | 1798035958834135040 |
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| author | Chu Zhang Tian Peng Jianzhong Zhou Muhammad Shahzad Nazir |
| author_facet | Chu Zhang Tian Peng Jianzhong Zhou Muhammad Shahzad Nazir |
| author_sort | Chu Zhang |
| collection | DOAJ |
| description | Abstract Pumped storage units (PSUs) are now widely used for energy storage. However, the uncertainty of the identification results of the pump‐turbine governing system (PTGS) caused by the random observation noises and the lack of prior knowledge remains an unaddressed issue. In recent years, the differential evolution adaptive Metropolis algorithm (DREAM) based on the Bayesian theory has been extensively used for parameter estimation and uncertainty analysis, but its application to the uncertainty analysis of PTGS has been rare. This study systematically evaluates the applicability of DREAM in the parameter identification and uncertainty quantification of PTGS. A real PSU in China has been employed as a case study for numerical experiments. Four groups of control experiments with different proportions of observation noises and different prior search spaces have been constructed in this study. It can be concluded from this study that: (a) accurate point identification results and effective uncertainty quantification can be obtained simultaneously using DREAM, (b) a lower proportion of observation noises can enhance the efficiency and effectiveness of the DREAM algorithm when applying to PTGS simulation. The DREAM method can narrow the prior search space of PTGS parameters effectively and thus help the hydropower engineers to make decisions. |
| first_indexed | 2024-04-11T21:05:46Z |
| format | Article |
| id | doaj.art-56f7cf99db304796abb8a730565224cc |
| institution | Directory Open Access Journal |
| issn | 1752-1416 1752-1424 |
| language | English |
| last_indexed | 2024-04-11T21:05:46Z |
| publishDate | 2021-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj.art-56f7cf99db304796abb8a730565224cc2022-12-22T04:03:21ZengWileyIET Renewable Power Generation1752-14161752-14242021-02-0115234235310.1049/rpg2.12027Parameter identification and uncertainty quantification of a non‐linear pump‐turbine governing system based on the differential evolution adaptive Metropolis algorithmChu Zhang0Tian Peng1Jianzhong Zhou2Muhammad Shahzad Nazir3College of Automation Huaiyin Institute of Technology Huaian ChinaCollege of Automation Huaiyin Institute of Technology Huaian ChinaSchool of Hydropower and Information Engineering Huazhong University of Science and Technology Wuhan ChinaCollege of Automation Huaiyin Institute of Technology Huaian ChinaAbstract Pumped storage units (PSUs) are now widely used for energy storage. However, the uncertainty of the identification results of the pump‐turbine governing system (PTGS) caused by the random observation noises and the lack of prior knowledge remains an unaddressed issue. In recent years, the differential evolution adaptive Metropolis algorithm (DREAM) based on the Bayesian theory has been extensively used for parameter estimation and uncertainty analysis, but its application to the uncertainty analysis of PTGS has been rare. This study systematically evaluates the applicability of DREAM in the parameter identification and uncertainty quantification of PTGS. A real PSU in China has been employed as a case study for numerical experiments. Four groups of control experiments with different proportions of observation noises and different prior search spaces have been constructed in this study. It can be concluded from this study that: (a) accurate point identification results and effective uncertainty quantification can be obtained simultaneously using DREAM, (b) a lower proportion of observation noises can enhance the efficiency and effectiveness of the DREAM algorithm when applying to PTGS simulation. The DREAM method can narrow the prior search space of PTGS parameters effectively and thus help the hydropower engineers to make decisions.https://doi.org/10.1049/rpg2.12027Pumped storage stations and plantsMonte Carlo methodsMarkov processesOptimisation techniques |
| spellingShingle | Chu Zhang Tian Peng Jianzhong Zhou Muhammad Shahzad Nazir Parameter identification and uncertainty quantification of a non‐linear pump‐turbine governing system based on the differential evolution adaptive Metropolis algorithm IET Renewable Power Generation Pumped storage stations and plants Monte Carlo methods Markov processes Optimisation techniques |
| title | Parameter identification and uncertainty quantification of a non‐linear pump‐turbine governing system based on the differential evolution adaptive Metropolis algorithm |
| title_full | Parameter identification and uncertainty quantification of a non‐linear pump‐turbine governing system based on the differential evolution adaptive Metropolis algorithm |
| title_fullStr | Parameter identification and uncertainty quantification of a non‐linear pump‐turbine governing system based on the differential evolution adaptive Metropolis algorithm |
| title_full_unstemmed | Parameter identification and uncertainty quantification of a non‐linear pump‐turbine governing system based on the differential evolution adaptive Metropolis algorithm |
| title_short | Parameter identification and uncertainty quantification of a non‐linear pump‐turbine governing system based on the differential evolution adaptive Metropolis algorithm |
| title_sort | parameter identification and uncertainty quantification of a non linear pump turbine governing system based on the differential evolution adaptive metropolis algorithm |
| topic | Pumped storage stations and plants Monte Carlo methods Markov processes Optimisation techniques |
| url | https://doi.org/10.1049/rpg2.12027 |
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