Abstract
Internal and external faults in a power transformer are discriminated in this paper using an algorithm based on a combination of a discrete wavelet transform (DWT) and a probabilistic neural network (PNN). DWT decomposes high-frequency fault components using the maximum coefficients of a ¼ cycle DWT as input patterns for the training process in a decision algorithm. A division algorithm between a zero sequence of post-fault differential current waveforms and the differential current coefficient in the ¼ cycle DWT is used to detect the maximum ratio and faults. The simulation system uses various study cases based on Thailand’s electricity transmission and distribution systems. The simulation results demonstrated that the PNN and BPNN are effectively implemented and perform fault detection with satisfactory accuracy. However, the PNN method is most suitable for detecting internal and external faults, and the maximum coefficient algorithm is the most effective in detecting the fault. This study will be useful in differential protection for power transformers.
Highlights
Nowadays, the Electricity Generating Authority of Thailand (EGAT) uses conventional methods to diagnose power transformer faults
As the data and training time of the probabilistic neural network (PNN) are less compared to the BPNN, the PNN is chosen in this algorithm
The maximum coefficients algorithm of the PNN method provides the highest accuracy, providing an overall accuracy of 196.67% of 200%, divided into the internal fault detection accuracy within 100% and the external fault detection accuracy within 96.67%
Summary
The Electricity Generating Authority of Thailand (EGAT) uses conventional methods (such as over-current, voltage, and differential relays) to diagnose power transformer faults. This research presents a new decision algorithm of protective relay development to discriminate between external and internal faults. The PNN has not been fully evaluated, the PNN has many advantages, such as fast training, flexible adding or removing data from a dataset, training without storing data for a long time, etc These are useful for distinguishing between external and internal faults using a DWT and PNN. Design a new decision algorithm for protective relay to discriminate between internal and external faults. Study the simulation model in the ATP program to understand and consider the issues in the study.
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