Application of multi-sensor information fusion technology in fault early warning of smart grid equipment
Abstract The purpose of this paper is to improve the fault early warning effect of smart grid equipment through multi-sensor information fusion technology. Therefore, based on the analytical model of power grid fault diagnosis, this paper considers the influence of distributed generation in distribu...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-11-01
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| Series: | Energy Informatics |
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| Online Access: | https://doi.org/10.1186/s42162-024-00433-0 |
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| _version_ | 1846158172625043456 |
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| author | Zhihui Kang Yanjie Zhang Yuhong Du |
| author_facet | Zhihui Kang Yanjie Zhang Yuhong Du |
| author_sort | Zhihui Kang |
| collection | DOAJ |
| description | Abstract The purpose of this paper is to improve the fault early warning effect of smart grid equipment through multi-sensor information fusion technology. Therefore, based on the analytical model of power grid fault diagnosis, this paper considers the influence of distributed generation in distribution network on fault diagnosis, as well as the misoperation or refusal of protection and switch, and the false alarm or leakage of alarm signal. At the same time, in order to display the results of fault diagnosis accurately and intuitively, an analytical model of fault diagnosis of distribution network based on multi-source information fusion is proposed. Finally, this paper verifies the effectiveness of this method through an example application. This article uses the PEDL dataset for experimental research, Through the comparison of fault data, it can be seen that compared with existing methods, the method proposed in this paper achieves the highest goodness of fit for warning, indicating the best fault warning effect.When there is enough training set, the prediction accuracy of the fault set can reach over 99%, Based on experimental analysis, it can be concluded that the proposed power grid equipment model has higher accuracy and reliability compared to traditional models. And the model in this article integrates the real-time monitoring function of power grid equipment and the equipment fault warning function, which improves the practicality of the power grid equipment monitoring system. |
| format | Article |
| id | doaj-art-6f63ee6e943942fbaa0500f35dcd8715 |
| institution | Kabale University |
| issn | 2520-8942 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj-art-6f63ee6e943942fbaa0500f35dcd87152024-11-24T12:45:08ZengSpringerOpenEnergy Informatics2520-89422024-11-017112410.1186/s42162-024-00433-0Application of multi-sensor information fusion technology in fault early warning of smart grid equipmentZhihui Kang0Yanjie Zhang1Yuhong Du2Hebi Institute of Engineering and Technology, Henan Polytechnic UniversityHebi Institute of Engineering and Technology, Henan Polytechnic UniversityHebi Institute of Engineering and Technology, Henan Polytechnic UniversityAbstract The purpose of this paper is to improve the fault early warning effect of smart grid equipment through multi-sensor information fusion technology. Therefore, based on the analytical model of power grid fault diagnosis, this paper considers the influence of distributed generation in distribution network on fault diagnosis, as well as the misoperation or refusal of protection and switch, and the false alarm or leakage of alarm signal. At the same time, in order to display the results of fault diagnosis accurately and intuitively, an analytical model of fault diagnosis of distribution network based on multi-source information fusion is proposed. Finally, this paper verifies the effectiveness of this method through an example application. This article uses the PEDL dataset for experimental research, Through the comparison of fault data, it can be seen that compared with existing methods, the method proposed in this paper achieves the highest goodness of fit for warning, indicating the best fault warning effect.When there is enough training set, the prediction accuracy of the fault set can reach over 99%, Based on experimental analysis, it can be concluded that the proposed power grid equipment model has higher accuracy and reliability compared to traditional models. And the model in this article integrates the real-time monitoring function of power grid equipment and the equipment fault warning function, which improves the practicality of the power grid equipment monitoring system.https://doi.org/10.1186/s42162-024-00433-0Multi-sensorInformation fusionSmart gridEquipment failureEarly warning |
| spellingShingle | Zhihui Kang Yanjie Zhang Yuhong Du Application of multi-sensor information fusion technology in fault early warning of smart grid equipment Energy Informatics Multi-sensor Information fusion Smart grid Equipment failure Early warning |
| title | Application of multi-sensor information fusion technology in fault early warning of smart grid equipment |
| title_full | Application of multi-sensor information fusion technology in fault early warning of smart grid equipment |
| title_fullStr | Application of multi-sensor information fusion technology in fault early warning of smart grid equipment |
| title_full_unstemmed | Application of multi-sensor information fusion technology in fault early warning of smart grid equipment |
| title_short | Application of multi-sensor information fusion technology in fault early warning of smart grid equipment |
| title_sort | application of multi sensor information fusion technology in fault early warning of smart grid equipment |
| topic | Multi-sensor Information fusion Smart grid Equipment failure Early warning |
| url | https://doi.org/10.1186/s42162-024-00433-0 |
| work_keys_str_mv | AT zhihuikang applicationofmultisensorinformationfusiontechnologyinfaultearlywarningofsmartgridequipment AT yanjiezhang applicationofmultisensorinformationfusiontechnologyinfaultearlywarningofsmartgridequipment AT yuhongdu applicationofmultisensorinformationfusiontechnologyinfaultearlywarningofsmartgridequipment |