Review on Evolution of Intelligent Algorithms for Transformer Condition Assessment

Wang，Jian1; Zhang，Xihai2; Zhang，Fangfang3; Wan，Junhe3; Kou，Lei3; Ke，Wende4

2022-05-25

10.3389/fenrg.2022.904109

Frontiers in Energy Research   Impact Factor & Quartile

2296-598X

Transformers are playing an increasingly significant part in energy conversion, transmission, and distribution, which link various resources, including conventional, renewable, and sustainable energy, from generation to consumption. Power transformers and their components are vulnerable to various operational factors during their entire life cycle, which may lead to catastrophic failures, irreversible revenue losses, and power outages. Hence, it is crucial to investigate transformer condition assessment to grasp the operating state accurately to reduce the failures and operating costs and enhance the reliability performance. In this context, comprehensive data mining and analysis based on intelligent algorithms are of great significance for promoting the comprehensiveness, efficiency, and accuracy of condition assessment. In this article, in an attempt to provide and reveal the current status and evolution of intelligent algorithms for transformer condition assessment and provide a better understanding of research perspectives, a unified framework of intelligent algorithms for transformer condition assessment and a survey of new findings in this rapidly-advancing field are presented. First, the failure statistics analysis is outlined, and the developing mechanism of the transformer internal latent fault is investigated. Then, in combination with intelligent demands of the tasks in each stage of transformer condition assessment under big data, we analyze the data source in-depth and redefine the concept and architecture of transformer condition assessment. Furthermore, the typical methods widely used in transformer condition assessment are mainly divided into rule, information fusion, and artificial intelligence. The new findings for intelligent algorithms are also elaborated, including differentiated evaluation, uncertainty methods, and big data analysis. Finally, future research directions are discussed.

artificial intelligence big data analysis condition-based maintenance information fusion transformer condition assessment uncertainty method

SCI ; EI

English

Corresponding

WOS:000807895000001

20222412235919

Artificial intelligence ; Big data ; Condition based maintenance ; Data handling ; Data mining ; Energy conversion ; Failure analysis ; Information fusion ; Life cycle ; Outages ; Power transformers ; Uncertainty analysis

Energy Conversion Issues:525.5 ; Electric Power Systems:706.1 ; Electric Power Lines and Equipment:706.2 ; Data Processing and Image Processing:723.2 ; Artificial Intelligence:723.4 ; Information Sources and Analysis:903.1 ; Cost Accounting:911.1 ; Industrial Economics:911.2 ; Maintenance:913.5 ; Probability Theory:922.1

2-s2.0-85131882268

Scopus

1.School of Electrical Engineering,Southwest Jiaotong University,Chengdu,China
2.School of Electrical and Information Engineering,Tianjin University,Tianjin,China
3.Qilu University of Technology (Shandong Academy of Sciences),Qingdao,China
4.Department of Mechanical and Energy Engineering,Southern University of Science and Technology,Shenzhen,China

Wang，Jian,Zhang，Xihai,Zhang，Fangfang,et al. Review on Evolution of Intelligent Algorithms for Transformer Condition Assessment[J]. Frontiers in Energy Research,2022,10.

Wang，Jian,Zhang，Xihai,Zhang，Fangfang,Wan，Junhe,Kou，Lei,&Ke，Wende.(2022).Review on Evolution of Intelligent Algorithms for Transformer Condition Assessment.Frontiers in Energy Research,10.

Wang，Jian,et al."Review on Evolution of Intelligent Algorithms for Transformer Condition Assessment".Frontiers in Energy Research 10(2022).