Regelii Suassuna De Andrade Ferreira

Frequency response analysis (FRA) is being employed worldwide as one of the main methods for the internal condition assessment of transformers due to its capability of detecting mechanical changes. Nonetheless, the objective interpretation of FRA measurements is still a challenge for the industry. This is mainly attributable to the lack of complete data from the same or similar units. A large database of FRA measurements can contribute to improving classification algorithms and lead to a more objective interpretation. Due to their destructive nature, mechanical deformations cannot be performed on real transformers to collect data from different scenarios. The use of simulation and laboratory transformer models is necessary. This research contribution is based on a new method using Finite Element Method simulation and a lumped element circuit to obtain FRA traces from a laboratory model at healthy and faulty states, along with an optimization method to improve capacitive parameters from estimated values. The results show that measured and simulated FRA traces are in good agreement. Furthermore, the faulty FRA traces were analyzed to obtain the characterization of faults based on the variation of the lumped element’s parameters. This supports the use of the proposed method in the generation of faulty frequency response traces and its further use in classifying and localizing faults in the transformer windings. The proposed approach is therefore tailored for generating a larger and unique database of FRA traces with industrial importance and academic significance.

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.

Frequency response analysis is a powerful tool for mechanical fault diagnostics in power transformers. However, interpretation schemes still today depend on expert analyses, mainly because of the complex structure of power transformers. One of the fundamental shortcomings of experimental investigations is that mechanical deformations cannot be managed on real transformers to obtain data for different scenarios because they are too destructive. To address this issue in a systematic way, the current research used a specially designed laboratory transformer model that allows mechanical defects to be introduced so its frequency response can be evaluated under different conditions. The key feature of this model is the non-destructive interchangeability of its winding sections, allowing reproducibility and repeatability of frequency response measurements. Numerical indices were compared over key performance indicators (linearity, sensitivity and monotonicity). The analysis indicated that comparative standard deviation offered promising results for evaluation of mechanical deformations on the laboratory winding model given its monotonic behaviour, sensitivity and linear increase with fault severity. Additionally, support vector machine learning, radial basis function neural network and the statistical k-nearest neighbour method were used for fault classification with different strategies and configurations. While limited data from different transformers are used in the available literature, the approach discussed here considers 371 measurements from the same transformer model. The test results are supportive and demonstrate great accuracy when machine learning is used for winding fault classification.

Many researchers have been studying frequency response analysis measurements and its relevant interpretations. Nowadays, the measurements have been well standardized. However, more objective results interpretation is still needed. This research used a laboratory winding model to enable the simulation of mechanical defects and obtained controlled measurements of its frequency response. The vectfit and the Chinese standard methods were used to compare different interpretations. Results indicated an agreement between the vectfit method and the experimental data, demonstrating that the model has potential of providing a powerful tool to interpretation of power transformer frequency behaviour. Nonetheless, the Chinese standard failed to show the severity of the defects, indicating a normal winding for some cases evaluated. For an overall maintenance strategy, frequency response tests can help taking restorative measures before deterioration reaches a point where failure of the transformer is inevitable.

Two of the main problems studied in protective current transformers (CTs) are the saturation and residual flux in the ferromagnetic core. To minimise the effects of high saturation levels and residual flux, a gapped core CT can be employed. Although solving both issues, research reports that the introduction of gaps in the ferromagnetic core can develop into a mechanical fragility when compared with a closed core. However, the fragility is not proven in the research. To investigate the mechanical fragility of a gapped core CT, this research aims to verify the influence of the electromagnetic forces in the gap borders of a protective CT under a short-circuit condition. The influence was verified in three different gap lengths: 8, 16 and 24 mm. On the basis of the results, the electromagnetic forces in the gap borders of a protective CT did not influence the CT response.