Character-word level ensemble integrated model for power transformer defect recording text mining method
The operation and maintenance management of transformers has accumulated a large amount of unstructured defect recording data in the form of text. However, the lack of effective mining method has led to an extremely low utilization rate. A text mining method for transformer defect recording text bas...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Electric Power Engineering Technology
2024-11-01
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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/231007230 |
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| author | LI Yuan LI Rui LIN Jinshan JIN Lingfeng SHAO Xianjun ZHANG Guanjun |
| author_facet | LI Yuan LI Rui LIN Jinshan JIN Lingfeng SHAO Xianjun ZHANG Guanjun |
| author_sort | LI Yuan |
| collection | DOAJ |
| description | The operation and maintenance management of transformers has accumulated a large amount of unstructured defect recording data in the form of text. However, the lack of effective mining method has led to an extremely low utilization rate. A text mining method for transformer defect recording text based on a character-word level ensemble integrated model is proposed in this paper. Firstly, the transformer defect recording texts are preprocessed with text segmentation, stop word removal, text augmentation, and text feature representation to convert the data into mathematical vectors for input. By integrating multiple word- and character-level classification models, the method can realize accurate identification and classification of transformer defect types through the synergistic and complementary effects of meta-learners on the individual base learners. Compared to single-text classification algorithms, this method can obtain the semantic features of the text more comprehensively, achieving a classification precision of 91% and F1 score of 0.9, which is the comprehensive evaluation score for model precision and recall. By applying natural language processing technology to precise power equipment defect recoding text classification and efficient fault recognition, data resources are awakened, and the intelligent management level of power transformers is significantly improved. |
| format | Article |
| id | doaj-art-ba6861195efe43d9ab8dab01e2709fea |
| institution | OA Journals |
| issn | 2096-3203 |
| language | zho |
| publishDate | 2024-11-01 |
| publisher | Editorial Department of Electric Power Engineering Technology |
| record_format | Article |
| series | 电力工程技术 |
| spelling | doaj-art-ba6861195efe43d9ab8dab01e2709fea2025-08-20T02:38:58ZzhoEditorial Department of Electric Power Engineering Technology电力工程技术2096-32032024-11-0143615316210.12158/j.2096-3203.2024.06.015231007230Character-word level ensemble integrated model for power transformer defect recording text mining methodLI Yuan0LI Rui1LIN Jinshan2JIN Lingfeng3SHAO Xianjun4ZHANG Guanjun5School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, ChinaSchool of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, ChinaSchool of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, ChinaState Grid Zhejiang Electric Power Co., Ltd. Research Institute, Hangzhou 310014, ChinaState Grid Zhejiang Electric Power Co., Ltd. Research Institute, Hangzhou 310014, ChinaSchool of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, ChinaThe operation and maintenance management of transformers has accumulated a large amount of unstructured defect recording data in the form of text. However, the lack of effective mining method has led to an extremely low utilization rate. A text mining method for transformer defect recording text based on a character-word level ensemble integrated model is proposed in this paper. Firstly, the transformer defect recording texts are preprocessed with text segmentation, stop word removal, text augmentation, and text feature representation to convert the data into mathematical vectors for input. By integrating multiple word- and character-level classification models, the method can realize accurate identification and classification of transformer defect types through the synergistic and complementary effects of meta-learners on the individual base learners. Compared to single-text classification algorithms, this method can obtain the semantic features of the text more comprehensively, achieving a classification precision of 91% and F1 score of 0.9, which is the comprehensive evaluation score for model precision and recall. By applying natural language processing technology to precise power equipment defect recoding text classification and efficient fault recognition, data resources are awakened, and the intelligent management level of power transformers is significantly improved.https://www.epet-info.com/dlgcjsen/article/abstract/231007230power transformernatural language processingtext miningfault diagnosisensemble learningartificial intelligence |
| spellingShingle | LI Yuan LI Rui LIN Jinshan JIN Lingfeng SHAO Xianjun ZHANG Guanjun Character-word level ensemble integrated model for power transformer defect recording text mining method 电力工程技术 power transformer natural language processing text mining fault diagnosis ensemble learning artificial intelligence |
| title | Character-word level ensemble integrated model for power transformer defect recording text mining method |
| title_full | Character-word level ensemble integrated model for power transformer defect recording text mining method |
| title_fullStr | Character-word level ensemble integrated model for power transformer defect recording text mining method |
| title_full_unstemmed | Character-word level ensemble integrated model for power transformer defect recording text mining method |
| title_short | Character-word level ensemble integrated model for power transformer defect recording text mining method |
| title_sort | character word level ensemble integrated model for power transformer defect recording text mining method |
| topic | power transformer natural language processing text mining fault diagnosis ensemble learning artificial intelligence |
| url | https://www.epet-info.com/dlgcjsen/article/abstract/231007230 |
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