Portal Dynamics Learning Method for Renewable-integrated Regional Power Networks Based on Neural Differential-Algebraic Equations
In the context of high penetration of renewables, it is very important for new power system dynamic analysis to establish a dynamic model that can accurately describe the portal dynamics of renewable-integrated regional power networks under the influence of complex environmental factors. Therefore a...
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| Format: | Article |
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State Grid Energy Research Institute
2023-02-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202208009 |
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| author | Bin CAO Ke SU Shuai YUAN Tannan XIAO Ying CHEN |
| author_facet | Bin CAO Ke SU Shuai YUAN Tannan XIAO Ying CHEN |
| author_sort | Bin CAO |
| collection | DOAJ |
| description | In the context of high penetration of renewables, it is very important for new power system dynamic analysis to establish a dynamic model that can accurately describe the portal dynamics of renewable-integrated regional power networks under the influence of complex environmental factors. Therefore a neural differential-algebraic equations-based portal dynamics learning method is proposed for renewable-integrated regional power networks. In this method, the differential-algebraic neural network is used to learn the portal dynamics model expressed in the form of neural network based on the time series measurements of the access point of the regional power networks and the environmental measurement data such as the radiation intensity and temperature. The learned model is composed of an initial state extracting block, a neural differential equation block and an algebraic equation block, and can be directly integrated into power system transient simulations to analyze the overall dynamics of power systems. The proposed method is tested through simulation in the IEEE-39 system, and the test results show that the obtained model can adapt to different environmental scenarios with acceptable accuracy, which verifies the effectiveness of the proposed method. The modelling method only needs portal time series measurements and has great application potential in the dynamic analysis of new power systems. |
| format | Article |
| id | doaj-art-153e914dd46a445da4e0e6becdd027f9 |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2023-02-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-153e914dd46a445da4e0e6becdd027f92025-08-20T02:52:24ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492023-02-01562233110.11930/j.issn.1004-9649.202208009zgdl-55-11-caobinPortal Dynamics Learning Method for Renewable-integrated Regional Power Networks Based on Neural Differential-Algebraic EquationsBin CAO0Ke SU1Shuai YUAN2Tannan XIAO3Ying CHEN4Inner Mongolia Power Research Institute Branch, Inner Mongolia Power (Group) Co., Ltd., Hohhot 010020, ChinaInner Mongolia Power Research Institute Branch, Inner Mongolia Power (Group) Co., Ltd., Hohhot 010020, ChinaInner Mongolia Power Research Institute Branch, Inner Mongolia Power (Group) Co., Ltd., Hohhot 010020, ChinaTsinghua Sichuan Energy Internet Research Institute, Chengdu 610200, ChinaTsinghua Sichuan Energy Internet Research Institute, Chengdu 610200, ChinaIn the context of high penetration of renewables, it is very important for new power system dynamic analysis to establish a dynamic model that can accurately describe the portal dynamics of renewable-integrated regional power networks under the influence of complex environmental factors. Therefore a neural differential-algebraic equations-based portal dynamics learning method is proposed for renewable-integrated regional power networks. In this method, the differential-algebraic neural network is used to learn the portal dynamics model expressed in the form of neural network based on the time series measurements of the access point of the regional power networks and the environmental measurement data such as the radiation intensity and temperature. The learned model is composed of an initial state extracting block, a neural differential equation block and an algebraic equation block, and can be directly integrated into power system transient simulations to analyze the overall dynamics of power systems. The proposed method is tested through simulation in the IEEE-39 system, and the test results show that the obtained model can adapt to different environmental scenarios with acceptable accuracy, which verifies the effectiveness of the proposed method. The modelling method only needs portal time series measurements and has great application potential in the dynamic analysis of new power systems.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202208009renewable energyportal dynamics modelingdifferential-algebraic equationneural networkdynamic simulation |
| spellingShingle | Bin CAO Ke SU Shuai YUAN Tannan XIAO Ying CHEN Portal Dynamics Learning Method for Renewable-integrated Regional Power Networks Based on Neural Differential-Algebraic Equations Zhongguo dianli renewable energy portal dynamics modeling differential-algebraic equation neural network dynamic simulation |
| title | Portal Dynamics Learning Method for Renewable-integrated Regional Power Networks Based on Neural Differential-Algebraic Equations |
| title_full | Portal Dynamics Learning Method for Renewable-integrated Regional Power Networks Based on Neural Differential-Algebraic Equations |
| title_fullStr | Portal Dynamics Learning Method for Renewable-integrated Regional Power Networks Based on Neural Differential-Algebraic Equations |
| title_full_unstemmed | Portal Dynamics Learning Method for Renewable-integrated Regional Power Networks Based on Neural Differential-Algebraic Equations |
| title_short | Portal Dynamics Learning Method for Renewable-integrated Regional Power Networks Based on Neural Differential-Algebraic Equations |
| title_sort | portal dynamics learning method for renewable integrated regional power networks based on neural differential algebraic equations |
| topic | renewable energy portal dynamics modeling differential-algebraic equation neural network dynamic simulation |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202208009 |
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