• Ismaila Musa Hassan
• Ukashatu Abubakar
• Sani Abdullahi Mohammed
• Jamilu Suleiman

This study aims at detecting and classifying the distribution transformers' faults. To deal with the problem of an extremely large data set with different fault situations, a three-step optimized neural network approach has been proposed. The approach utilizes Discrete Wavelet Transform for detection and two different types of self-organized, unsupervised Adaptive Resonance Theory neural networks for classification. The fault scenarios are simulated using the Alternate Transients Program, and the performance of this highly improved scheme is compared with the existing techniques. In order to analyze a signal, wavelet transform can be applied as well as Fourier transform. The Fourier transform and its inverse can transform a signal between the time and frequency domains. Therefore, it is possible to view the signal characteristics either in the time or frequency domain, but not the combination of both domains. Differently from the case of the Fourier transform, the Wavelet Analysis (WT) provides a varying time-frequency resolution in the time-frequency plane. In this thesis, a new method for protecting power transformers based on the energy of differential-current signals is introduced. The simulation results show that it is possible to detect different kinds of internal faults using this method. Furthermore, it is possible to distinguish such faults from magnetizing inrush current.

Distribution Transformers, Fault detection, Artificial Neural Networks, Fault Classification.

"FAULT DETECTION AND PREDICTION IN DISTRIBUTION TRANSFORMERS USING NEURAL WAVELET AND NETWORK TRANSFORM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 9, page no.d92-d99, September-2026, Available :http://www.jetir.org/papers/JETIR2609311.pdf

"FAULT DETECTION AND PREDICTION IN DISTRIBUTION TRANSFORMERS USING NEURAL WAVELET AND NETWORK TRANSFORM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 9, page no. ppd92-d99, September-2026, Available at : http://www.jetir.org/papers/JETIR2609311.pdf