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Analysis of Factors Affecting the Failure of the Electric Arc Furnace (EAF) Transformer and Improving its Life

Isvand, Saman | 2024

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 58523 (46)
  4. University: Sharif University of Technology
  5. Department: Energy Engineering
  6. Advisor(s): Rajabi Ghahnavieh, Abbas
  7. Abstract:
  8. The transfer of energy from one object to another due to a temperature difference is a well-known phenomenon. In other words, the temperature difference acts as the driving force for energy transfer. In electrical machines, heat is initially transferred through conduction and then through convection and radiation. No-load and load losses generate heat in the transformer, leading to an increase in temperature. To analyze the thermal state inside the transformer, the similarity between electrical and thermal parameters is used. Various thermal models exist to predict the thermal behavior of transformers. However, most of these models require parameters that are usually not readily available or need special measurements and tests that are not commonly performed. As a result, these models are not easy to apply in practice. In this thesis, using the standard relations of IEEE and IEC, a model is proposed in which the parameters can be determined based on commonly measured data such as load, ambient temperature, and oil temperature. Using this model, we can predict the thermal behavior of the top-oil temperature of the transformer tank under different loading and ambient temperature conditions. To use the proposed model, certain coefficients must be identified. For determining these coefficients and evaluating the model, load and temperature data from an electric arc furnace transformer were used. The coefficients were obtained using various methods (LS, RLS, RLSF, & MM). For model evaluation, a 50-hour ahead prediction of the top-oil temperature was performed using the coefficients derived from all methods, and the predicted values were compared with actual measurements. Since the thermal model of the transformer is dynamic in nature, methods such as RLSF and MM, which can track system dynamics, provided much better predictions with acceptable error margins. Furthermore, using the transformer oil temperature model and IEEE/IEC standard formulas, the hot-spot temperatures were estimated and compared with actual values, showing that the model can accurately predict the transformer’s thermal behavior. The resulting model can be used to determine the maximum load factor in an offline mode, as well as the maximum deliverable load of the transformer in an online mode. Following the presentation of predictive models, the cooling method of the transformer using the OFWF (Oil Forced Water Forced) technique is thoroughly discussed, including a complete set of related calculations. Finally, a model for transformer failure rate and aging is proposed, based on thermal conditions and the performance of the cooling system parameters. The results clearly show that by improving the parameters of the cooling system, the rate of failure growth, aging factor, and the total duration of elevated temperatures remain within acceptable limits
  9. Keywords:
  10. Transformers ; Thermal Modeling ; Ambient Temperature ; Hot Spot ; Electric Arc Furnaces ; Top-Oil Temperature

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