Frequency Response Analysis-Based Transformer Condition Monitoring Supported by Artificial Intelligence—A Review

Abstract

Among the most expensive assets in power grids, power transformers are essential for the reliability of the power supply chain and the overall stability of the grid. Due to their permanent connection to the network, this equipment is exposed to all kinds of faults and phenomena, including short-circuit faults and overvoltages caused by lightning and switching. Hence, ongoing monitoring of the transformer's condition is essential to prevent breakdowns and damage to the transformer. Among the different condition monitoring methods, the frequency response analysis (FRA) method is sensitive to the smallest functional changes of the transformer, as it is completely related to the physics and geometry of the transformer. This method stands out as one of the most effective and efficient approaches to transformer monitoring, especially for detecting mechanical faults. However, the FRA method faces an important challenge of interpretation: the correlation between the type of fault that occurred and the way the transformer's function changes is still not well-known, and studies in this field are ongoing. One of the most widely used methods of interpreting frequency response results is the use of numerical indices, coil modelling, transformer function estimation, and artificial intelligence algorithms. This paper introduces these methods, and their advantages and disadvantages are discussed. Then, the most widely used artificial intelligence algorithms in transformer condition monitoring are presented and compared. Finally, future research directions are anticipated.