Linearization threshold condition and stability analysis of a stochastic dynamic model of one-machine infinite-bus (OMIB) power systems
Abstract With the increase in the proportion of multiple renewable energy sources, power electronics equipment and new loads, power systems are gradually evolving towards the integration of multi-energy, multi-network and multi-subject affected by more stochastic excitation with greater intensity. T...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-06-01
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| Series: | Protection and Control of Modern Power Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41601-021-00198-8 |
| _version_ | 1818462024734081024 |
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| author | Lijuan Li Yongdong Chen Bin Zhou Hongliang Liu Yongfei Liu |
| author_facet | Lijuan Li Yongdong Chen Bin Zhou Hongliang Liu Yongfei Liu |
| author_sort | Lijuan Li |
| collection | DOAJ |
| description | Abstract With the increase in the proportion of multiple renewable energy sources, power electronics equipment and new loads, power systems are gradually evolving towards the integration of multi-energy, multi-network and multi-subject affected by more stochastic excitation with greater intensity. There is a problem of establishing an effective stochastic dynamic model and algorithm under different stochastic excitation intensities. A Milstein-Euler predictor-corrector method for a nonlinear and linearized stochastic dynamic model of a power system is constructed to numerically discretize the models. The optimal threshold model of stochastic excitation intensity for linearizing the nonlinear stochastic dynamic model is proposed to obtain the corresponding linearization threshold condition. The simulation results of one-machine infinite-bus (OMIB) systems show the correctness and rationality of the predictor-corrector method and the linearization threshold condition for the power system stochastic dynamic model. This study provides a reference for stochastic modelling and efficient simulation of power systems with multiple stochastic excitations and has important application value for stability judgment and security evaluation. |
| first_indexed | 2024-12-14T23:55:28Z |
| format | Article |
| id | doaj.art-eab047b5b1274ee68b09c31de46dfab3 |
| institution | Directory Open Access Journal |
| issn | 2367-2617 2367-0983 |
| language | English |
| last_indexed | 2024-12-14T23:55:28Z |
| publishDate | 2021-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Protection and Control of Modern Power Systems |
| spelling | doaj.art-eab047b5b1274ee68b09c31de46dfab32022-12-21T22:43:08ZengSpringerOpenProtection and Control of Modern Power Systems2367-26172367-09832021-06-016111110.1186/s41601-021-00198-8Linearization threshold condition and stability analysis of a stochastic dynamic model of one-machine infinite-bus (OMIB) power systemsLijuan Li0Yongdong Chen1Bin Zhou2Hongliang Liu3Yongfei Liu4College of Automation and Electronic Information, Hunan Engineering Research Center of Multi energy Cooperative Control Technology, Xiangtan UniversityCollege of Automation and Electronic Information, Xiangtan UniversityCollege of Electrical and Information Engineering, Hunan UniversityCollege of Mathematics and Computational Science, Xiangtan UniversityState Grid Jiangsu Electric Power Co. Electric Power Research InstituteAbstract With the increase in the proportion of multiple renewable energy sources, power electronics equipment and new loads, power systems are gradually evolving towards the integration of multi-energy, multi-network and multi-subject affected by more stochastic excitation with greater intensity. There is a problem of establishing an effective stochastic dynamic model and algorithm under different stochastic excitation intensities. A Milstein-Euler predictor-corrector method for a nonlinear and linearized stochastic dynamic model of a power system is constructed to numerically discretize the models. The optimal threshold model of stochastic excitation intensity for linearizing the nonlinear stochastic dynamic model is proposed to obtain the corresponding linearization threshold condition. The simulation results of one-machine infinite-bus (OMIB) systems show the correctness and rationality of the predictor-corrector method and the linearization threshold condition for the power system stochastic dynamic model. This study provides a reference for stochastic modelling and efficient simulation of power systems with multiple stochastic excitations and has important application value for stability judgment and security evaluation.https://doi.org/10.1186/s41601-021-00198-8Power system stabilityStochastic dynamic modelStochastic excitationStochastic processesThreshold condition |
| spellingShingle | Lijuan Li Yongdong Chen Bin Zhou Hongliang Liu Yongfei Liu Linearization threshold condition and stability analysis of a stochastic dynamic model of one-machine infinite-bus (OMIB) power systems Protection and Control of Modern Power Systems Power system stability Stochastic dynamic model Stochastic excitation Stochastic processes Threshold condition |
| title | Linearization threshold condition and stability analysis of a stochastic dynamic model of one-machine infinite-bus (OMIB) power systems |
| title_full | Linearization threshold condition and stability analysis of a stochastic dynamic model of one-machine infinite-bus (OMIB) power systems |
| title_fullStr | Linearization threshold condition and stability analysis of a stochastic dynamic model of one-machine infinite-bus (OMIB) power systems |
| title_full_unstemmed | Linearization threshold condition and stability analysis of a stochastic dynamic model of one-machine infinite-bus (OMIB) power systems |
| title_short | Linearization threshold condition and stability analysis of a stochastic dynamic model of one-machine infinite-bus (OMIB) power systems |
| title_sort | linearization threshold condition and stability analysis of a stochastic dynamic model of one machine infinite bus omib power systems |
| topic | Power system stability Stochastic dynamic model Stochastic excitation Stochastic processes Threshold condition |
| url | https://doi.org/10.1186/s41601-021-00198-8 |
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