TY - GEN
T1 - Data-Driven Approaches for Distribution Transformer Health Monitoring: A Review
AU - Mogos, Aman Samson
AU - Liang, Xiaodong
AU - Chung, C. Y.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/9
Y1 - 2023/9
N2 - Distribution transformers are the key components in distribution systems to maintain reliability of the system operation and reduce power outages. In this paper, a literature review is conducted on data-driven methods of the distribution transformer health monitoring by classifying the research streams and emphasizing advancements in machine learning, artificial intelligence and hybrid approaches in this area. The significance of data-driven methods is highlighted, demonstrating their ability to overcome traditional analytic limitations by providing real-time monitoring, prediction, and adaptability. As the distribution system continues to expand due to the increasing penetration of distributed energy resources (DERs) and electric vehicles (EVs), data-driven techniques emerge as a dependable and adaptable option for the effective transformer health monitoring.
AB - Distribution transformers are the key components in distribution systems to maintain reliability of the system operation and reduce power outages. In this paper, a literature review is conducted on data-driven methods of the distribution transformer health monitoring by classifying the research streams and emphasizing advancements in machine learning, artificial intelligence and hybrid approaches in this area. The significance of data-driven methods is highlighted, demonstrating their ability to overcome traditional analytic limitations by providing real-time monitoring, prediction, and adaptability. As the distribution system continues to expand due to the increasing penetration of distributed energy resources (DERs) and electric vehicles (EVs), data-driven techniques emerge as a dependable and adaptable option for the effective transformer health monitoring.
KW - Data-driven approaches
KW - distribution transformer
KW - health index
KW - health monitoring
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85177441894&partnerID=8YFLogxK
U2 - 10.1109/CCECE58730.2023.10288844
DO - 10.1109/CCECE58730.2023.10288844
M3 - Conference article published in proceeding or book
AN - SCOPUS:85177441894
T3 - Canadian Conference on Electrical and Computer Engineering
SP - 31
EP - 36
BT - 2023 Annual IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2023
Y2 - 24 September 2023 through 27 September 2023
ER -