A neural network design for black-box identification of converter impedance models in arbitrary operating conditions
The impedance-based method is favored by engineering because it can analyze system stability under conditions with the unknown device control structure or parameters. Considering that the impedance characteristics of AC grid-connected equipment represented by power electronic converters are easily a...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Electric Power Engineering Technology
2025-01-01
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| Series: | 电力工程技术 |
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| Online Access: | https://www.epet-info.com/dlgcjsen/article/abstract/231105370 |
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| author | CHEN Bing ZHAO Chongbin JIANG Qirong WANG Xu WANG Fangming |
| author_facet | CHEN Bing ZHAO Chongbin JIANG Qirong WANG Xu WANG Fangming |
| author_sort | CHEN Bing |
| collection | DOAJ |
| description | The impedance-based method is favored by engineering because it can analyze system stability under conditions with the unknown device control structure or parameters. Considering that the impedance characteristics of AC grid-connected equipment represented by power electronic converters are easily affected by the AC steady-state operating point, quickly deriving an impedance model for any operating condition of the converter using black-box identification can greatly improve the efficiency of stability analysis. The neural network-based can eliminate the limitations of the least squares method-based identification, this paper further improves the neural network design to significantly improve its interpretability. In the data collection stage, the frequency sweep method is used to obtain the frequency response of the closed-loop impedance model under enough operating conditions. In the model training stage, taking into account the latent features of the converter impedance model, a neural network with the same number as the disturbance frequency was designed, and the Levenberg-Marquardt algorithm with Bayesian regularization integrated is used to enhance the generalization ability of the network trained with a small dataset. In the model verification phase, the network is fed with set operating conditions, achieving highly accurate identification of stable operating conditions and offline prediction. |
| format | Article |
| id | doaj-art-7b66701e92d14196bb650f571f8d7322 |
| institution | Kabale University |
| issn | 2096-3203 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Editorial Department of Electric Power Engineering Technology |
| record_format | Article |
| series | 电力工程技术 |
| spelling | doaj-art-7b66701e92d14196bb650f571f8d73222025-02-08T08:40:18ZzhoEditorial Department of Electric Power Engineering Technology电力工程技术2096-32032025-01-014412810.12158/j.2096-3203.2025.01.001231105370A neural network design for black-box identification of converter impedance models in arbitrary operating conditionsCHEN Bing0ZHAO Chongbin1JIANG Qirong2WANG Xu3WANG Fangming4State Grid Jiangsu Electric Power Co., Ltd. Research Institute, Nanjing 211103, ChinaState Key Lab of Control and Simulation of Power System Operation and Contorl (Department of Electrical Engineering, Tsinghua University), Beijing 100084, ChinaState Key Lab of Control and Simulation of Power System Operation and Contorl (Department of Electrical Engineering, Tsinghua University), Beijing 100084, ChinaState Grid Jiangsu Electric Power Co., Ltd. Research Institute, Nanjing 211103, ChinaState Grid Jiangsu Electric Power Co., Ltd. Research Institute, Nanjing 211103, ChinaThe impedance-based method is favored by engineering because it can analyze system stability under conditions with the unknown device control structure or parameters. Considering that the impedance characteristics of AC grid-connected equipment represented by power electronic converters are easily affected by the AC steady-state operating point, quickly deriving an impedance model for any operating condition of the converter using black-box identification can greatly improve the efficiency of stability analysis. The neural network-based can eliminate the limitations of the least squares method-based identification, this paper further improves the neural network design to significantly improve its interpretability. In the data collection stage, the frequency sweep method is used to obtain the frequency response of the closed-loop impedance model under enough operating conditions. In the model training stage, taking into account the latent features of the converter impedance model, a neural network with the same number as the disturbance frequency was designed, and the Levenberg-Marquardt algorithm with Bayesian regularization integrated is used to enhance the generalization ability of the network trained with a small dataset. In the model verification phase, the network is fed with set operating conditions, achieving highly accurate identification of stable operating conditions and offline prediction.https://www.epet-info.com/dlgcjsen/article/abstract/231105370power electronic converterimpedance modelblack-box identificationneural networkstability analysispower quality |
| spellingShingle | CHEN Bing ZHAO Chongbin JIANG Qirong WANG Xu WANG Fangming A neural network design for black-box identification of converter impedance models in arbitrary operating conditions 电力工程技术 power electronic converter impedance model black-box identification neural network stability analysis power quality |
| title | A neural network design for black-box identification of converter impedance models in arbitrary operating conditions |
| title_full | A neural network design for black-box identification of converter impedance models in arbitrary operating conditions |
| title_fullStr | A neural network design for black-box identification of converter impedance models in arbitrary operating conditions |
| title_full_unstemmed | A neural network design for black-box identification of converter impedance models in arbitrary operating conditions |
| title_short | A neural network design for black-box identification of converter impedance models in arbitrary operating conditions |
| title_sort | neural network design for black box identification of converter impedance models in arbitrary operating conditions |
| topic | power electronic converter impedance model black-box identification neural network stability analysis power quality |
| url | https://www.epet-info.com/dlgcjsen/article/abstract/231105370 |
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