Distribution network fault comprehensive identification method based on voltage–ampere curves and deep ensemble learning
To identify and locate faults of small-current grounded distribution networks under high-impedance fault with weak characteristics, a fault comprehensive identification method for distribution networks based on voltage-ampere curves and deep ensemble learning is proposed. First, the correlations of...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | International Journal of Electrical Power & Energy Systems |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061524006264 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832595381723070464 |
|---|---|
| author | Jian Wang Bo Zhang Dong Yin Jinxin Ouyang |
| author_facet | Jian Wang Bo Zhang Dong Yin Jinxin Ouyang |
| author_sort | Jian Wang |
| collection | DOAJ |
| description | To identify and locate faults of small-current grounded distribution networks under high-impedance fault with weak characteristics, a fault comprehensive identification method for distribution networks based on voltage-ampere curves and deep ensemble learning is proposed. First, the correlations of the voltage-ampere curves with the fault causes, fault types, and fault distances are analyzed to illustrate the feasibility of using three-phase and zero-sequence voltage-ampere curves as input features. In addition, a multimodal residual network model is developed to extract the fault features using RGB normalization and an attention mechanism. Moreover, a vertical segmentation technique is employed to enhance the feature extraction for fault location by using fault-phase voltage-ampere curves at the identified section to improve the overall fault comprehensive identification performance. Finally, the advantages of the proposed fault identification model and fault location model are validated through comparison experiments. Moreover, the proposed method has significant advantages over the impedance method and artificial neural network method for fault section identification and fault distance estimation. The proposed method has good adaptability and generalization ability to different systems and real-world data. The proposed method can provide decision guidance for automatic line reclosing, fault recovery and operation and maintenance repair.© 2017 Elsevier Inc. All rights reserved. |
| format | Article |
| id | doaj-art-29c8c41ef0e943a0ab9d7a597dad0d8b |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-29c8c41ef0e943a0ab9d7a597dad0d8b2025-01-19T06:23:55ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-03-01164110403Distribution network fault comprehensive identification method based on voltage–ampere curves and deep ensemble learningJian Wang0Bo Zhang1Dong Yin2Jinxin Ouyang3State Key Laboratory of Power Transmission Equipment Technology, Chongqing University, Shapingba, Chongqing 400044, People's Republic of China; Corresponding author.State Key Laboratory of Power Transmission Equipment Technology, Chongqing University, Shapingba, Chongqing 400044, People's Republic of ChinaState Key Laboratory of Power Transmission Equipment Technology, Chongqing University, Shapingba, Chongqing 400044, People's Republic of China; Shinan Electric Power Supply Branch Company, State Grid Chongqing Electric Power Company, Nan’an, Chongqing 400060, People's Republic of ChinaState Key Laboratory of Power Transmission Equipment Technology, Chongqing University, Shapingba, Chongqing 400044, People's Republic of ChinaTo identify and locate faults of small-current grounded distribution networks under high-impedance fault with weak characteristics, a fault comprehensive identification method for distribution networks based on voltage-ampere curves and deep ensemble learning is proposed. First, the correlations of the voltage-ampere curves with the fault causes, fault types, and fault distances are analyzed to illustrate the feasibility of using three-phase and zero-sequence voltage-ampere curves as input features. In addition, a multimodal residual network model is developed to extract the fault features using RGB normalization and an attention mechanism. Moreover, a vertical segmentation technique is employed to enhance the feature extraction for fault location by using fault-phase voltage-ampere curves at the identified section to improve the overall fault comprehensive identification performance. Finally, the advantages of the proposed fault identification model and fault location model are validated through comparison experiments. Moreover, the proposed method has significant advantages over the impedance method and artificial neural network method for fault section identification and fault distance estimation. The proposed method has good adaptability and generalization ability to different systems and real-world data. The proposed method can provide decision guidance for automatic line reclosing, fault recovery and operation and maintenance repair.© 2017 Elsevier Inc. All rights reserved.http://www.sciencedirect.com/science/article/pii/S0142061524006264Distribution networkFault identificationHigh-impedance faultResNetVoltage-Ampere curve |
| spellingShingle | Jian Wang Bo Zhang Dong Yin Jinxin Ouyang Distribution network fault comprehensive identification method based on voltage–ampere curves and deep ensemble learning International Journal of Electrical Power & Energy Systems Distribution network Fault identification High-impedance fault ResNet Voltage-Ampere curve |
| title | Distribution network fault comprehensive identification method based on voltage–ampere curves and deep ensemble learning |
| title_full | Distribution network fault comprehensive identification method based on voltage–ampere curves and deep ensemble learning |
| title_fullStr | Distribution network fault comprehensive identification method based on voltage–ampere curves and deep ensemble learning |
| title_full_unstemmed | Distribution network fault comprehensive identification method based on voltage–ampere curves and deep ensemble learning |
| title_short | Distribution network fault comprehensive identification method based on voltage–ampere curves and deep ensemble learning |
| title_sort | distribution network fault comprehensive identification method based on voltage ampere curves and deep ensemble learning |
| topic | Distribution network Fault identification High-impedance fault ResNet Voltage-Ampere curve |
| url | http://www.sciencedirect.com/science/article/pii/S0142061524006264 |
| work_keys_str_mv | AT jianwang distributionnetworkfaultcomprehensiveidentificationmethodbasedonvoltageamperecurvesanddeepensemblelearning AT bozhang distributionnetworkfaultcomprehensiveidentificationmethodbasedonvoltageamperecurvesanddeepensemblelearning AT dongyin distributionnetworkfaultcomprehensiveidentificationmethodbasedonvoltageamperecurvesanddeepensemblelearning AT jinxinouyang distributionnetworkfaultcomprehensiveidentificationmethodbasedonvoltageamperecurvesanddeepensemblelearning |