This paper presents an algorithm based on a combination of Discrete Wavelet Transforms and neural networks for detection and classification of internal faults in a twowinding three-phase transformer. Fault conditions of the transformer are simulated using ATP/EMTP in order to obtain current signals. The training process for the neural network and fault diagnosis decision are implemented using t...

This paper investigates the application of wavelet transform as a preprocessor for neural networks (NN) in identifying internal turn-to-turn faults in transformer windings. The faulty and normal signals generated by numerical simulation of ElectroMagnetic Transient Program (EMTP) are preprocessed using discrete wavelet transform (DWT). The mean values of the wavelet coefficients are input into ...

This paper will propose a cascade of minimum description length criterion with entropy approach along with artificial neural network (ANN) as an optimal feature extraction and selection tool for a wavelet packet transform based transformer differential protection. The proposed protection method provides a reliable and computationally efficient tool for distinguishing between internal faults and...

Microgrids are a highly efficient means of embedding distributed generation sources in a power system. However, if a fault occurs inside or outside the microgrid, the microgrid should be immediately disconnected from the main grid using a static switch installed at the secondary side of the main transformer near the point of common coupling (PCC). The static switch should have a reliable module...

This paper presents an efficient wavelet and neural network (WNN) based algorithm for distinguishing magnetizing inrush currents from internal fault currents in three phase power transformers. The wavelet transform is applied first to decompose the current signals of the power transformer into a series of detailed wavelet components. The values of the detailed coefficients obtained can accurate...

Happening of Ferroresonance phenomenon is one of the reasons of consuming and ruining transformers, so recognition of Ferroresonance phenomenon has a special importance. A novel method for classification of Ferroresonance presented in this paper. Using this method Ferroresonance can be discriminate from other transients such as capacitor switching, load switching, transformer switching. Wavelet...

This paper presents an innovative method for detecting and classifying various power system disturbances using wavelet analysis. The proposed method employs a multiresolution analysis (MRA) using localized wavelet basis functions. The nonlinear sub-band time-frequency structure extracted using the MRA can provide the needed features to detect and classify any power system transient disturbance....

Currently, many face forgery detection methods aggregate spatial and frequency features to enhance the generalization ability gain promising performance under cross-dataset scenario. However, these only leverage one level information which limits their expressive ability. To overcome limitations, we propose a multi-scale wavelet transformer framework for detection. Specifically, take full advan...

This paper presents an algorithm to detect and classify voltage sag causes based on Wavelet Transform (WT) and Probabilistic Neural Network (PNN). A technique is required which is capable of extracting both time-frequency information to identify the causes which contribute to power quality disturbances. Wavelet transform based on multiresolution analysis is used to extract the features from the...