TY - GEN
T1 - The ICA-SVM based operation state identification for oil immersed distribution transformers
AU - Zhang, Wenting
AU - Yuan, Haiwen
AU - Xie, Li
AU - Ju, Yong
AU - Zhao, Luxing
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Distribution transformers are one of the most important equipment in the modern distribution systems. They directly affect the stability and security of distribution power grid. Dissolved gas analysis (DGA) is a common approach used for operation state identification of distribution transformers. This paper proposes an operation state identification method by using the DGA data measured from the distribution transformers. The proposed method is based on the integration of independent component analysis and support vector machine (ICA-SVM). Firstly, the ICA is performed on the DGA data to extract feature vectors; Then, the feature vectors are served as input of SVM to identify the transformer operation states; Finally, the on-site monitoring DGA data from 110 kV distribution transformers are applied to verify the effectiveness of the proposed method. The experimental results show that the proposed ICA-SVM method can recognize the operation states of distribution transformers effectively.
AB - Distribution transformers are one of the most important equipment in the modern distribution systems. They directly affect the stability and security of distribution power grid. Dissolved gas analysis (DGA) is a common approach used for operation state identification of distribution transformers. This paper proposes an operation state identification method by using the DGA data measured from the distribution transformers. The proposed method is based on the integration of independent component analysis and support vector machine (ICA-SVM). Firstly, the ICA is performed on the DGA data to extract feature vectors; Then, the feature vectors are served as input of SVM to identify the transformer operation states; Finally, the on-site monitoring DGA data from 110 kV distribution transformers are applied to verify the effectiveness of the proposed method. The experimental results show that the proposed ICA-SVM method can recognize the operation states of distribution transformers effectively.
KW - DGA data
KW - distribution transformer
KW - independent component analysis
KW - operation state identification
KW - support vector machine
UR - https://www.scopus.com/pages/publications/85047458863
U2 - 10.1109/ICIEA.2017.8283176
DO - 10.1109/ICIEA.2017.8283176
M3 - 会议稿件
AN - SCOPUS:85047458863
T3 - Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017
SP - 2055
EP - 2059
BT - Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017
Y2 - 18 June 2017 through 20 June 2017
ER -