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- Title
Transformer Core Saturation Fault Analysis using Current Sensor Signals and Thermal Image Features.
- Authors
VIDHYA, Ramalingam; VANAJA RANJAN, Prema; SHANKER, Nagalingam Rajendiran
- Abstract
Transformer faults are identified and classified using current sensor signals. Transformer core saturation detection is challenging using current sensor signals due to overlapping of load-based faults such as overload, short circuit, grounding faults, in-rush and power supply fluctuations in current sensor signals. Existing methods are unable to differentiate load-based fault and Transformer core-based faults from current sensor signals. In this paper, transformer core-based faults such as overheating, voltage regulation issues due to power fluctuation, increased current draw due to short circuit or overload are differentiated from load-based faults, using current sensor signal energy band and thermal image of current sensor which are acquired simultaneously. In this paper, transformer core-based faults are differentiated from load-based faults after the current signals are processed with Modified-Tunable Q-factor Wavelet Transform and Rational Dilation Wavelet Transform and current sensor thermal images are processed with Multi Resolution wavelet - Deep Convolutional Neural Network. Energy band-based values from current sensor signal and current sensor thermal image Haralick features are used for differentiating transformer core-based and load-based faults. From the experimental and simulation results the transformer core-based and load-based faults are detected with an accuracy of 95% and compared with traditional methods.
- Subjects
CURRENT transformers (Instrument transformer); TRANSFORMER models; CONVOLUTIONAL neural networks; THERMOGRAPHY; SIGNAL processing; CURRENT fluctuations
- Publication
Advances in Electrical & Computer Engineering, 2023, Vol 23, Issue 4, p69
- ISSN
1582-7445
- Publication type
Article
- DOI
10.4316/AECE.2023.04008