Fault Location Estimation on Transmission Lines Using Wavelet Transform and Artificial Neural Network
نویسندگان
چکیده
This paper presents a wavelet transform (WT) and artificial neural network (ANN) based algorithm for estimating fault location on transmission lines. The algorithm is developed as a one-end frequency based technique and used both voltage and current effect resulting from remote end of the power system. Fault simulation is carry out in Alternative Transient Program (ATP). One cycle of waveform, covering pre-fault and post-fault information is abstracted for analysis. The discrete wavelet transform (DWT) is used for data preprocessing and this data are used for training and testing ANN. Five types of mother wavelet are used for signal processing to identify a suitable wavelet family that is more appropriate for use in estimating fault location. It is found that the proposed method gives satisfactory results and will be useful for estimating fault location.
منابع مشابه
Accurate Fault Classification of Transmission Line Using Wavelet Transform and Probabilistic Neural Network
Fault classification in distance protection of transmission lines, with considering the wide variation in the fault operating conditions, has been very challenging task. This paper presents a probabilistic neural network (PNN) and new feature selection technique for fault classification in transmission lines. Initially, wavelet transform is used for feature extraction from half cycle of post-fa...
متن کاملAccurate Hybrid Method for Rapid Fault Detection, Classification and Location in Transmission Lines using Wavelet Transform and ANNs
The present paper presents an accurate hybrid framework capable to rapidly detect, classify & locate shortcircuit faults on transmission lines. The proposed algorithm has employed the values resulted from each threephase currents wavelet transform in order to obtain instantaneous fault detection. Singling out short-circuit faults based on the measured voltage waveforms and three-phase current i...
متن کاملDetection and Classification of Faults on Parallel Transmission Lines Using Wavelet Transform and Neural Network
The protection of parallel transmission lines has been a challenging task due to mutual coupling between the adjacent circuits of the line. This paper presents a novel scheme for detection and classification of faults on parallel transmission lines. The proposed approach uses combination of wavelet transform and neural network, to solve the problem. While wavelet transform is a powerful mathema...
متن کاملAN INTELLIGENT FAULT DIAGNOSIS APPROACH FOR GEARS AND BEARINGS BASED ON WAVELET TRANSFORM AS A PREPROCESSOR AND ARTIFICIAL NEURAL NETWORKS
In this paper, a fault diagnosis system based on discrete wavelet transform (DWT) and artificial neural networks (ANNs) is designed to diagnose different types of fault in gears and bearings. DWT is an advanced signal-processing technique for fault detection and identification. Five features of wavelet transform RMS, crest factor, kurtosis, standard deviation and skewness of discrete wavelet co...
متن کاملFault location and classification in non-homogeneous transmission line utilizing breaker transients
In this paper, a single-ended fault location method is presented based on a circuit breaker operation using the frequencies of traveling waves. The proposed method receives the required data from voltage traveling waves with the aid of Fast Fourier Transform (FFT) and Wavelet Transform. Then, the Artificial Neural Network (ANN) identifies fault type and determines its location. In order to eval...
متن کامل