| Title | Identification of Transformer Magnetizing Inrush Current Based on Empirical Mode Decomposition |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Liu, Zepeng, Xiao, Shiwu, Dong, Huanyu |
| Conference Name | 2021 IEEE 4th International Electrical and Energy Conference (CIEEC) |
| Date Published | may |
| Keywords | artificial intelligence, Artificial intelligence algorithm, compositionality, cyber physical systems, Empirical mode decomposition, Indexes, Internet of Things, magnetizing inrush current, Mean impact value, noise reduction, Prediction algorithms, pubcrawl, remanence, Resiliency, simulation, Technological innovation |
| Abstract | Aiming at the fact that the existing feature quantities cannot well identify the magnetizing inrush current during remanence and bias and the huge number of feature quantities, a new identification method using empirical mode decomposition energy index and artificial intelligence algorithm is proposed in 'this paper. Decomposition and denoising are realized through empirical mode decomposition, and then the corresponding energy index is obtained for the waveform of each inherent modal component and simplified by the mean impact value method. Finally, the accuracy of prediction using artificial intelligence algorithm is close to 100%. This reflects the practicality of the method proposed in 'this article. |
| DOI | 10.1109/CIEEC50170.2021.9510298 |
| Citation Key | liu_identification_2021 |
Identification of Transformer Magnetizing Inrush Current Based on Empirical Mode Decomposition