Minimum inertia demand estimation of new power system considering diverse inertial resources based on deep neural network
Abstract With the high‐proportion integration of renewable energy and power electronic equipment, the inertia supporting ability of new power system continues to decline, which seriously threatens the frequency stability of power grids. In order to clarify the operation boundary, and realise the rap...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-03-01
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| Series: | IET Energy Systems Integration |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/esi2.12086 |
| _version_ | 1811154977872150528 |
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| author | Liu Zicheng Tao Zhou Zhong Chen Yi Wang Yalun Wang |
| author_facet | Liu Zicheng Tao Zhou Zhong Chen Yi Wang Yalun Wang |
| author_sort | Liu Zicheng |
| collection | DOAJ |
| description | Abstract With the high‐proportion integration of renewable energy and power electronic equipment, the inertia supporting ability of new power system continues to decline, which seriously threatens the frequency stability of power grids. In order to clarify the operation boundary, and realise the rapid analysis and prediction of the minimum inertia demand of new power systems, this study proposes a minimum inertia demand estimation method based on deep neural network (DNN). Firstly, this study establishes the system frequency response model of new power systems containing diverse inertia resources including renewable energy, induction machine and so on. Considering the constraints of rate of change of frequency and maximum frequency deviation, the minimum inertia demand estimation model is established to ensure the system frequency stability. DNN is introduced to effectively map non‐linear relations in complex situations, which can quickly estimate and predict the minimum inertia of new power systems. Adam algorithm is utilised to optimise the input weight matrix and hidden layer feature vector of the network to improve accuracy. Finally, the simulations and analysis are conducted in IEEE‐39 system to verify the accuracy and generalisation ability of the proposed method in this paper. |
| first_indexed | 2024-04-10T04:26:18Z |
| format | Article |
| id | doaj.art-876973ec68bd47fe9a486470c65a4b91 |
| institution | Directory Open Access Journal |
| issn | 2516-8401 |
| language | English |
| last_indexed | 2024-04-10T04:26:18Z |
| publishDate | 2023-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Energy Systems Integration |
| spelling | doaj.art-876973ec68bd47fe9a486470c65a4b912023-03-10T14:13:22ZengWileyIET Energy Systems Integration2516-84012023-03-0151809410.1049/esi2.12086Minimum inertia demand estimation of new power system considering diverse inertial resources based on deep neural networkLiu Zicheng0Tao Zhou1Zhong Chen2Yi Wang3Yalun Wang4School of Automation Nanjing University of Science and Technology Nanjing ChinaSchool of Automation Nanjing University of Science and Technology Nanjing ChinaSchool of Electrical Engineering Southeast University Nanjing ChinaState Grid Electric Power Research Institute Smart Grid Protection and Operation Control National Key Laboratory Nanjing ChinaSchool of Automation Nanjing University of Science and Technology Nanjing ChinaAbstract With the high‐proportion integration of renewable energy and power electronic equipment, the inertia supporting ability of new power system continues to decline, which seriously threatens the frequency stability of power grids. In order to clarify the operation boundary, and realise the rapid analysis and prediction of the minimum inertia demand of new power systems, this study proposes a minimum inertia demand estimation method based on deep neural network (DNN). Firstly, this study establishes the system frequency response model of new power systems containing diverse inertia resources including renewable energy, induction machine and so on. Considering the constraints of rate of change of frequency and maximum frequency deviation, the minimum inertia demand estimation model is established to ensure the system frequency stability. DNN is introduced to effectively map non‐linear relations in complex situations, which can quickly estimate and predict the minimum inertia of new power systems. Adam algorithm is utilised to optimise the input weight matrix and hidden layer feature vector of the network to improve accuracy. Finally, the simulations and analysis are conducted in IEEE‐39 system to verify the accuracy and generalisation ability of the proposed method in this paper.https://doi.org/10.1049/esi2.12086neurophysiologynonlinear programmingoptimisationpower system stabilitywind power |
| spellingShingle | Liu Zicheng Tao Zhou Zhong Chen Yi Wang Yalun Wang Minimum inertia demand estimation of new power system considering diverse inertial resources based on deep neural network IET Energy Systems Integration neurophysiology nonlinear programming optimisation power system stability wind power |
| title | Minimum inertia demand estimation of new power system considering diverse inertial resources based on deep neural network |
| title_full | Minimum inertia demand estimation of new power system considering diverse inertial resources based on deep neural network |
| title_fullStr | Minimum inertia demand estimation of new power system considering diverse inertial resources based on deep neural network |
| title_full_unstemmed | Minimum inertia demand estimation of new power system considering diverse inertial resources based on deep neural network |
| title_short | Minimum inertia demand estimation of new power system considering diverse inertial resources based on deep neural network |
| title_sort | minimum inertia demand estimation of new power system considering diverse inertial resources based on deep neural network |
| topic | neurophysiology nonlinear programming optimisation power system stability wind power |
| url | https://doi.org/10.1049/esi2.12086 |
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