A Machine-Learning Approach to Identify the Influence of Temperature on FRA Measurements

Abstract

Frequency response analysis (FRA) is a powerful and widely used tool for condition assessment in power transformers. However, interpretation schemes are still challenging. Studies show that FRA data can be influenced by parameters other than winding deformation, including temperature. In this study, a machine-learning approach with temperature as an input attribute was used to objectively identify faults in FRA traces. To the best knowledge of the authors, this has not been reported in the literature. A single-phase transformer model was specifically designed and fabricated for use as a test object for the study. The model is unique in that it allows the non-destructive interchange of healthy and distorted winding sections and, hence, reproducible and repeatable FRA measurements. FRA measurements taken at temperatures ranging from −40 °C to 40 °C were used first to describe the impact of temperature on FRA traces and then to test the ability of the machine learning algorithms to discriminate between fault conditions and temperature variation. The results show that when temperature is not considered in the training dataset, the algorithm may misclassify healthy measurements, taken at different temperatures, as mechanical or electrical faults. However, once the influence of temperature was considered in the training set, the performance of the classifier as studied was restored. The results indicate the feasibility of using the proposed approach to prevent misclassification based on temperature changes.

Derivation of transformer winding equivalent circuit by employing the transfer function obtained from frequency response analysis data

“…Several TW EC models, encompassing two or 3 TW models, were investigated in Refs. [33–37]. In these configurations, high voltage (HV), medium voltage (MV), and low voltage (LV) windings are depicted using the EC model shown in Figure 1.…”

On the Feasibility of Monitoring Power Transformer’s Winding Vibration and Temperature along with Moisture in Oil Using Optical Sensors

“…To avoid potential catastrophic consequences, different methods allowing the monitoring of the physical integrity of the transformer such as: the frequency response method (FRA) [ 13 ] sweep frequency response analysis (SFRA) [ 14 ], leakage reactance method [ 15 ], and winding deformation analysis, in power transformers using the Finite Element Method [ 10 ] were proposed. Although these methods are effective in studying the physical integrity of the transformer, they do not provide real time monitoring of the power transformer windings’ integrity, to reflect dynamically their condition [ 16 ].…”