A Neural Approach to Evaluate the Effect of Lightning in Power Transformers
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André Nunes De Souza
This paper proposes the application of computational intelligence techniques to assist complex problems concerning lightning in transformers. In order to estimate the currents related to lightning in a transformer, a neural tool is presented. ATP has generated the training vectors. The input variables used in Artificial Neural Networks (ANN) were the wave front time, the wave tail time, the voltage variation rate and the output variable is the maximum current in the secondary of the transformer. These parameters can define the behavior and severity of lightning. Based on these concepts and from the results obtained, it can be verified that the overvoltages at the secondary of transformer are also affected by the discharge waveform in a similar way to the primary side. By using the tool developed, the high voltage process in the distribution transformers can be mapped and estimated with more precision aiding the transformer project process, minimizing empirics and evaluation errors, and contributing to minimize the failure rate of transformers.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Artikel in diesem Heft
- Article
- Reactive Power Optimization Based on Adaptive Immune Algorithm
- Calculation of the Critical Energy of the Power System with Protection Operation
- Advanced Analysis of Clustered Photovoltaic System's Performance Based on the Battery-Integrated Voltage Control Algorithm
- Comparative Study of LEO and GEO SSPS for Some Identified Sites in India
- Output Power Leveling of a Wind Generation System Using Inertia of a Wind Turbine
- Impact of Overspeed Protection Control on Stability for Islanded Power System
- Measurements and Simulations on ELF-EMF Magnetic Field Exposures from Multiple Electric Transmission Lines
- A Neural Approach to Evaluate the Effect of Lightning in Power Transformers
