Series Arc Fault Detection Based on Improved Artificial Hummingbird Algorithm Optimizer Optimized XGBoost
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Electrical Fault Detection and Protection · Integrated Circuits and Semiconductor Failure Analysis · Quality and Safety in Healthcare
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Division of Electronics and Informatics, School of Science and Technology, Gunma University, 1-5-1 Tenjin-cho, Kiryu 376-8515, Gunma, Japan
References (19)
Show source-provided references
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