High impedance fault detection method in distribution network based on improved Emanuel model and DenseNet. (November 2022)

• Record Type: Journal Article
• Authors: Bai, Hao; Tang, Bingnan; Cheng, Tianyu; Liu, Hongwen
• Is Part Of: Energy reports. Volume 8(2022)Supplement 10
• Page Start: 982
• Page End: 987
• Publication Date: 2022-11
• Subjects: Distribution network -- High impedance fault -- Artificial intelligence -- DenseNet -- Emanuel model; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.05.199
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: The paper proposes a high impedance fault (HIF) detection method in distribution network based on improved Emanuel and DenseNet. Firstly, the improved Emanuel model is introduced to accurately simulate HIF under different working conditions. Secondly, an improved DenseNet model is developed to implement HIF detection, the model uses multi-scale convolution kernels to extract different scale fault information, applies dual-channel Dense Block to transmit fault features more effectively, which can alleviate the vanishing gradient problem. ELU is employed as an activation function to expedite convergent speed of iterative operation. The comparative experiments verify the effectiveness of the proposed method.