Series Arc Fault Detection Based on Improved Artificial Hummingbird Algorithm Optimizer Optimized XGBoost
Based on the wide variety of electrical appliances, it is difficult to detect similar current waveforms when different appliances experience arc faults due to insufficient extraction of fault arc characteristics and low detection accuracy. To address these issues, a series arc fault detection method...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/12/6861 |
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| author | Lichun Qi Takahiro Kawaguchi Seiji Hashimoto |
| author_facet | Lichun Qi Takahiro Kawaguchi Seiji Hashimoto |
| author_sort | Lichun Qi |
| collection | DOAJ |
| description | Based on the wide variety of electrical appliances, it is difficult to detect similar current waveforms when different appliances experience arc faults due to insufficient extraction of fault arc characteristics and low detection accuracy. To address these issues, a series arc fault detection method combining artificial hummingbird algorithm (AHA) and XGboost has been proposed. According to GB14287.4—2014, an experimental platform for fault arcs was designed and built to collect fault arc signals. By leveraging the global search capability and dynamic adaptive mechanism of AHA, key feature subsets sensitive to arcs are selected from high-dimensional time–frequency domain features. Combining the parallel computing advantages and regularization strategies of XGBoost, a low-complexity, highly interpretable fault classification model is constructed. The hyperparameters of XGBoost are simultaneously optimized by AHA. Experimental results show that the proposed method achieves a fault arc detection accuracy rate of 98.098%, effectively identifying series arc faults. |
| format | Article |
| id | doaj-art-5563bc43323245ca8fb8452a73be37c9 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-5563bc43323245ca8fb8452a73be37c92025-08-20T03:26:10ZengMDPI AGApplied Sciences2076-34172025-06-011512686110.3390/app15126861Series Arc Fault Detection Based on Improved Artificial Hummingbird Algorithm Optimizer Optimized XGBoostLichun Qi0Takahiro Kawaguchi1Seiji Hashimoto2Division of Electronics and Informatics, School of Science and Technology, Gunma University, 1-5-1 Tenjin-cho, Kiryu 376-8515, Gunma, JapanDivision of Electronics and Informatics, School of Science and Technology, Gunma University, 1-5-1 Tenjin-cho, Kiryu 376-8515, Gunma, JapanDivision of Electronics and Informatics, School of Science and Technology, Gunma University, 1-5-1 Tenjin-cho, Kiryu 376-8515, Gunma, JapanBased on the wide variety of electrical appliances, it is difficult to detect similar current waveforms when different appliances experience arc faults due to insufficient extraction of fault arc characteristics and low detection accuracy. To address these issues, a series arc fault detection method combining artificial hummingbird algorithm (AHA) and XGboost has been proposed. According to GB14287.4—2014, an experimental platform for fault arcs was designed and built to collect fault arc signals. By leveraging the global search capability and dynamic adaptive mechanism of AHA, key feature subsets sensitive to arcs are selected from high-dimensional time–frequency domain features. Combining the parallel computing advantages and regularization strategies of XGBoost, a low-complexity, highly interpretable fault classification model is constructed. The hyperparameters of XGBoost are simultaneously optimized by AHA. Experimental results show that the proposed method achieves a fault arc detection accuracy rate of 98.098%, effectively identifying series arc faults.https://www.mdpi.com/2076-3417/15/12/6861arc faultartificial hummingbird algorithmfeature extractionXGBoost |
| spellingShingle | Lichun Qi Takahiro Kawaguchi Seiji Hashimoto Series Arc Fault Detection Based on Improved Artificial Hummingbird Algorithm Optimizer Optimized XGBoost Applied Sciences arc fault artificial hummingbird algorithm feature extraction XGBoost |
| title | Series Arc Fault Detection Based on Improved Artificial Hummingbird Algorithm Optimizer Optimized XGBoost |
| title_full | Series Arc Fault Detection Based on Improved Artificial Hummingbird Algorithm Optimizer Optimized XGBoost |
| title_fullStr | Series Arc Fault Detection Based on Improved Artificial Hummingbird Algorithm Optimizer Optimized XGBoost |
| title_full_unstemmed | Series Arc Fault Detection Based on Improved Artificial Hummingbird Algorithm Optimizer Optimized XGBoost |
| title_short | Series Arc Fault Detection Based on Improved Artificial Hummingbird Algorithm Optimizer Optimized XGBoost |
| title_sort | series arc fault detection based on improved artificial hummingbird algorithm optimizer optimized xgboost |
| topic | arc fault artificial hummingbird algorithm feature extraction XGBoost |
| url | https://www.mdpi.com/2076-3417/15/12/6861 |
| work_keys_str_mv | AT lichunqi seriesarcfaultdetectionbasedonimprovedartificialhummingbirdalgorithmoptimizeroptimizedxgboost AT takahirokawaguchi seriesarcfaultdetectionbasedonimprovedartificialhummingbirdalgorithmoptimizeroptimizedxgboost AT seijihashimoto seriesarcfaultdetectionbasedonimprovedartificialhummingbirdalgorithmoptimizeroptimizedxgboost |