Multi-Branch Line Fault Arc Detection Method Based on the Improved Northern Goshawk Optimization Adaptive Base Class LogitBoost Algorithm

• Published in: Energies
• Main Authors: Xue Wang, Yu Zhao
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2024
• Subjects: series fault arc; branch circuit fault; feature extraction; feature selection; ABCLogitBoost; Technology; T; Northern Goshawk
• Online Access: https://doi.org/10.3390/en17040954; https://doaj.org/article/84135a6f026046abb2d869425e1199ff

In low-voltage AC distribution systems, when a series arc fault occurs in a branch with multiple loads operating in parallel, it will be significantly more difficult to identify. Existing arc fault detection methods make it difficult to effectively detect faults occurring in the lower-level branch. This study introduces a novel series arc fault detection approach based on the improved northern goshawk optimization adaptive base class LogitBoost (INGO-ABCLogitBoost) algorithm. Considering the zero-rest, intermittent, and random fluctuation and high-frequency features of the arc current, the zero-rest coefficient, discrete coefficient, harmonic amplitude, and wavelet entropy are proposed to establish the high-dimensional feature matrix of the arc current. The ReliefF feature selection algorithm is used to optimize feature quality and decrease feature dimensionality. Subsequently, the ABCLogitBoost fault detection model is proposed, with the INGO algorithm applied to optimize the model parameters, thus enhancing the model’s diagnostic capabilities. The efficacy of the proposed diagnostic model is validated through the construction of a multi-load arc simulation system. The simulation results show that the overall fault diagnosis accuracy of the proposed method reaches 99.01% and can effectively identify the fault load types, which helps to locate the fault location.