Review on Evolution of Intelligent Algorithms for Transformer Condition Assessment - Wang, Jian; Zhang, Xihai; Zhang, Fangfang; Wan, Junhe; Kou, Lei; Ke, Wende (Frontiers in Energy Research, 2022)
Transformers are playing an increasingly significant part in electricity 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 entire life cycle, which may lead to catastrophic failures, irreversible revenue losses, and power outages. Hence, it is crucial to investigate the applications of intelligent algorithms in transformer condition assessment, which can reduce the failures and operating costs, and enhance the reliability performance. 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, this study presents a unified framework of intelligent algorithms for transformer condition assessment and a survey of new findings in this rapidly-advancing field. Firstly, after overviewing transformer failure statistics and analysis, the concept and architecture of transformer condition assessment is redefined. Then, the typical methods widely used in transformer condition assessment are mainly based on rule, information fusion, and artificial intelligence. The new findings are also elaborated, including differentiated evaluation, uncertainty methods, and big data analysis. Finally, conclusions and future trends are presented.