Review on Evolution of Intelligent Algorithms for Transformer Condition Assessment

Abstract

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.

In-Service Power Transformer Life Time Prospects: Review and Prospects

“…As illustrated in Figure 3, power transformers are complex equipment that includes integration of circuits, insulation, and mechanical mechanisms [27]. ese mechanisms are susceptible to diverse interior and exterior stresses and other influencing factors.…”

“…Because transformers have complex architectures and many components, the relationship between failure causes and components is not always plainly evident, and certain faults have inductivity and compliance characteristics. at is, one fault could be triggered by another, and the other faults may be induced by it [27].…”

“…Overloading a transformer causes solid insulation materials to deteriorate, releasing huge volumes of gases, which has an impact on the transformer's mechanical, electrical, and insulation qualities. Figure 4 exemplifies the development mechanism of a transformer's internal faults as suggested in [27]. In addition, there are complicated electrical and informational interactions between transformers and other power equipment.…”

Development of a simulation model for assessing the technical condition of the magnetic circuit of oil power transformers by measuring the temperature of the tank and the external environment

“…Due to the high cost of the housings, it is impossible to replace them with new ones, but you can extend the operating time [1][2]6]. Therefore, diagnostics of oil-immersed power transformers should be considered an integral part of ensuring the reliability and long-term operation of electrical power systems [7][8][9][10][11]. This practice makes it possible to effectively manage resources and ensure a stable energy supply, which ultimately has a positive effect on the economy and reliability of the energy infrastructure [9,[12][13].…”

Health condition assessment of transformers based on cross message passing graph neural networks

“…A power transformer is one of the most important parts of the power system (Xie et al, 2020), and the health of a power transformer plays a vital role in the safe operation of power grids and the development of national economy (Wang et al, 2022). Accurate health condition assessment helps detect potential faults and reduce maintenance costs by 20-50% (Sun et al, 2022).…”