Methodology to Evaluate the Electromechanical Effects of Electromagnetic Forces on Conductive Materials in Transformer Windings Using the Von Mises and Fatigue Criteria

Araújo, Jalberth Fernandes de; Costa, Edson G. da; Andrade, Fábia Letícia Martins de; Germano, André Dantas; Ferreira, Tarso Vilela

doi:10.1109/tpwrd.2016.2579165

Cited by 15 publications

(3 citation statements)

References 18 publications

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“…Mechanical forces beyond the design limits of the transformer may cause deformations in the windings. Once such deformations occur, the transformer's ability to withstand further mechanical forces originating in a potential overcurrent is greatly reduced due to localized electromagnetic stresses [3].…”

Section: Introductionmentioning

confidence: 99%

Frequency Response Analysis Interpretation Using Numerical Indices and Machine Learning: A Case Study Based on a Laboratory Model

et al. 2021

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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.INDEX TERMS Condition monitoring, frequency response analysis interpretation, machine learning, numerical indices, power transformers, radial basis function, support vector machines.

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Section: Introductionmentioning

confidence: 99%

Frequency Response Analysis Interpretation Using Numerical Indices and Machine Learning: A Case Study Based on a Laboratory Model

et al. 2021

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“…is time consuming process is not a problem for power transformer design because the simulation is limited to a few cases corresponding to the worst case scenario. However, when conducting a continuous analysis of the effect of the forces or mechanical stress in transformer windings, such as a stress analysis [10,11], using FEM is unfeasible mainly because the results are needed almost in real-time.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Algorithms for the Prediction of Mechanical Stress in Three-Phase Power Transformer Winding Conductors

Valencia

Arcos

Quilumba

2021

Journal of Electrical and Computer Engineering

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This research compares four machine learning techniques: linear regression, support vector regression, random forests, and artificial neural networks, with regard to the determination of mechanical stress in power transformer winding conductors due to three-phase electrical faults. The accuracy compared with finite element results was evaluated for each model. The input data were the transient electrical fault currents of power system equivalents with impedances from low to high values. The output data were the mechanical stress in the conductors located in the middle of the winding. To simplify the design, only one hyperparameter was varied on each machine learning technique. The random forests technique had the most accurate results. The highest errors were found for low-stress values, mainly due to the high difference between maximum and minimum stresses, which made the training of the machine learning models difficult. In the end, an accurate model that could be used in the continuous monitoring of mechanical stress was obtained.

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“…In general, none of the two aspects highlighted in the previous paragraph are considered in transformers design methods currently. Moreover, there are a growing number of researches that point to the importance of these aspects, some of which can be cited [8][9][10][11]. Therefore, in order to determine the importance of such aspects in power transformers design, in-depth research on the cumulative effect of electromechanical stresses caused by inrush currents is necessary.…”

Section: Introductionmentioning

confidence: 99%

Contribution of inrush current to mechanical failure of power transformers windings

et al. 2019

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This work presents a study regarding the contribution of inrush current to the occurrence of mechanical fatigue in windings of a power transformer and, consequently, the impact of that phenomenon on the equipment's lifetime. In order to perform the study, electromagnetic transient simulations were used to determine the inrush current in a three-phase transformer model. Then, three-dimensional simulations, based on the finite element method, were performed to determine the electromechanical stresses in the equipment windings, under the effect of the inrush current. Next, the methods of critical plans and Palmgren-Miner were applied to perform the fatigue study. From the results, the impact of inrush current on the integrity of the transformer's windings was determined, as well as, to estimate the reduction of lifespan of the equipment due to inrush occurrence. The number of inrushes needed for the total reduction of lifespan of the equipment due to mechanical fatigue was also determined. The results can be used in the transformers design stage, with the purpose of assisting the evaluation of mechanical withstand of equipment, which can minimise the possibility of failure and increase its lifespan.

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Methodology to Evaluate the Electromechanical Effects of Electromagnetic Forces on Conductive Materials in Transformer Windings Using the Von Mises and Fatigue Criteria

Cited by 15 publications

References 18 publications

Frequency Response Analysis Interpretation Using Numerical Indices and Machine Learning: A Case Study Based on a Laboratory Model

Frequency Response Analysis Interpretation Using Numerical Indices and Machine Learning: A Case Study Based on a Laboratory Model

Comparison of Machine Learning Algorithms for the Prediction of Mechanical Stress in Three-Phase Power Transformer Winding Conductors

Contribution of inrush current to mechanical failure of power transformers windings

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