Robust Simplified Modeling of Microgrid in the Context of Constructing New Power Systems
The development of microgrid with high proportion of renewable energy is one of the important means to construct new modern power systems so as to achieve energy security and low carbon emissions. However, amid the analysis of the dynamic characteristics of microgrid-integrated power system, the cur...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
State Grid Energy Research Institute
2024-01-01
|
| Series: | Zhongguo dianli |
| Subjects: | |
| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202307072 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850249797689147392 |
|---|---|
| author | Daxing WANG Yan Ning Jingpei WANG Yang XU Jun BI Mingbiao ZHOU Peng WANG |
| author_facet | Daxing WANG Yan Ning Jingpei WANG Yang XU Jun BI Mingbiao ZHOU Peng WANG |
| author_sort | Daxing WANG |
| collection | DOAJ |
| description | The development of microgrid with high proportion of renewable energy is one of the important means to construct new modern power systems so as to achieve energy security and low carbon emissions. However, amid the analysis of the dynamic characteristics of microgrid-integrated power system, the current equivalent models appear to be not robust enough. Specifically, these models can well reproduce the behaviors of actual system under the faults in training set, they may not be able to reflect actual system responses under other unknown faults (non-training faults). In regard to this, k-means++ is introduced first to effectively distinguish the typical operation condition of microgrid such that the randomness and time-varying characteristics of the system can be represented. Next, key parameter selection-based parameter identification method is applied to avoid the issue of multiple solutions in parameter identification process. Then, the convolutional neural network is used to generalize the model parameters with respect to different typical system operation conditions. Additionally, online matching of equivalent model parameters is achieved by virtue of Fisher discriminant analysis. Finally, the effectiveness of the proposed method has been verified in a real microgrid system in China. |
| format | Article |
| id | doaj-art-0df778eb80d643b6bafa8b731ca8a621 |
| institution | OA Journals |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2024-01-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-0df778eb80d643b6bafa8b731ca8a6212025-08-20T01:58:24ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492024-01-0157114815710.11930/j.issn.1004-9649.202307072zgdl-57-01-xuyangRobust Simplified Modeling of Microgrid in the Context of Constructing New Power SystemsDaxing WANG0Yan Ning1Jingpei WANG2Yang XU3Jun BI4Mingbiao ZHOU5Peng WANG6State Grid Sichuan Electric Power Research Institute, Chengdu 610041, ChinaZhejiang Dayou Industry Co., Ltd., Hangzhou 310009, ChinaState Grid Quzhou Power Supply Company, Quzhou 324000, ChinaSchool of Mechanical and Electrical Engineering University of Electronic Science and Technology of China, Chengdu 611731, ChinaState Grid Aba Power Supply Company, Aba 624000, ChinaState Grid Sanming Electric Power Co., Ltd., Sanming 365000, ChinaSchool of Mechanical and Electrical Engineering University of Electronic Science and Technology of China, Chengdu 611731, ChinaThe development of microgrid with high proportion of renewable energy is one of the important means to construct new modern power systems so as to achieve energy security and low carbon emissions. However, amid the analysis of the dynamic characteristics of microgrid-integrated power system, the current equivalent models appear to be not robust enough. Specifically, these models can well reproduce the behaviors of actual system under the faults in training set, they may not be able to reflect actual system responses under other unknown faults (non-training faults). In regard to this, k-means++ is introduced first to effectively distinguish the typical operation condition of microgrid such that the randomness and time-varying characteristics of the system can be represented. Next, key parameter selection-based parameter identification method is applied to avoid the issue of multiple solutions in parameter identification process. Then, the convolutional neural network is used to generalize the model parameters with respect to different typical system operation conditions. Additionally, online matching of equivalent model parameters is achieved by virtue of Fisher discriminant analysis. Finally, the effectiveness of the proposed method has been verified in a real microgrid system in China.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202307072microgridequivalent modelingrobustnessk-means++ clusteringconvolutional neural networkfisher discriminant analysis |
| spellingShingle | Daxing WANG Yan Ning Jingpei WANG Yang XU Jun BI Mingbiao ZHOU Peng WANG Robust Simplified Modeling of Microgrid in the Context of Constructing New Power Systems Zhongguo dianli microgrid equivalent modeling robustness k-means++ clustering convolutional neural network fisher discriminant analysis |
| title | Robust Simplified Modeling of Microgrid in the Context of Constructing New Power Systems |
| title_full | Robust Simplified Modeling of Microgrid in the Context of Constructing New Power Systems |
| title_fullStr | Robust Simplified Modeling of Microgrid in the Context of Constructing New Power Systems |
| title_full_unstemmed | Robust Simplified Modeling of Microgrid in the Context of Constructing New Power Systems |
| title_short | Robust Simplified Modeling of Microgrid in the Context of Constructing New Power Systems |
| title_sort | robust simplified modeling of microgrid in the context of constructing new power systems |
| topic | microgrid equivalent modeling robustness k-means++ clustering convolutional neural network fisher discriminant analysis |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202307072 |
| work_keys_str_mv | AT daxingwang robustsimplifiedmodelingofmicrogridinthecontextofconstructingnewpowersystems AT yanning robustsimplifiedmodelingofmicrogridinthecontextofconstructingnewpowersystems AT jingpeiwang robustsimplifiedmodelingofmicrogridinthecontextofconstructingnewpowersystems AT yangxu robustsimplifiedmodelingofmicrogridinthecontextofconstructingnewpowersystems AT junbi robustsimplifiedmodelingofmicrogridinthecontextofconstructingnewpowersystems AT mingbiaozhou robustsimplifiedmodelingofmicrogridinthecontextofconstructingnewpowersystems AT pengwang robustsimplifiedmodelingofmicrogridinthecontextofconstructingnewpowersystems |