دورية أكاديمية
Partial discharge pattern recognition algorithm of overhead covered conductors based on feature optimization and bidirectional LSTM‐GRU
| العنوان: | Partial discharge pattern recognition algorithm of overhead covered conductors based on feature optimization and bidirectional LSTM‐GRU |
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| المؤلفون: | Chungfeng Zhang, Mingli Chen, Yongjun Zhang, Wenyang Deng, Yu Gong, Di Zhang |
| المصدر: | IET Generation, Transmission & Distribution, Vol 18, Iss 4, Pp 680-693 (2024) |
| بيانات النشر: | Wiley, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Production of electric energy or power. Powerplants. Central stations |
| مصطلحات موضوعية: | corona discharge, partial discharge, pattern recognition, wavelet transformation, Distribution or transmission of electric power, TK3001-3521, Production of electric energy or power. Powerplants. Central stations, TK1001-1841 |
| الوصف: | Abstract Recognition of partial discharge (PD) patterns is essential for insulation diagnosis of covered conductors in overhead lines. Current research has not sufficiently addressed the complex background noise in real environments, and most detection methods depend primarily on feature engineering or deep learning, suggesting potential for improvement in accuracy and efficiency. This has led the authors to propose a PD pattern recognition algorithm that integrates feature selection and deep learning. This algorithm incorporates the design of a discrete wavelet denoising function specifically tailored to the characteristics of PD for data preprocessing. It employs Bayesian optimization algorithms and light gradient boosting machines for characterizing corona discharge features. Furthermore, it develops multi‐scale clustering features and phase‐resolved features for feature fusion, and constructs insightful features based on the light gradient boosting machine. Finally, a novel deep learning model is formulated, demonstrating exceptional detection performance for early faults in covered conductors. Experimental results show that this algorithm attains an Matthews correlation coefficient score of 0.814, a 13.2% improvement over the baseline algorithm's 0.719, and a speed increase of 39.18%. The final accuracy amounts to 97.85%. This algorithm demonstrates exceptional performance in detecting early insulation faults in conductors. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1751-8695 1751-8687 |
| Relation: | https://doaj.org/toc/1751-8687; https://doaj.org/toc/1751-8695 |
| DOI: | 10.1049/gtd2.13104 |
| URL الوصول: | https://doaj.org/article/4e0a74a1c6c644789a0b302b1ec8544f |
| رقم الأكسشن: | edsdoj.4e0a74a1c6c644789a0b302b1ec8544f |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 17518695 17518687 |
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| DOI: | 10.1049/gtd2.13104 |