Post dispatch analysis of signals obtained from digital disturbances registers provide important information to identify and classify disturbances in power systems, looking for a more efficient management of the supply. In order to enhance the task of identifying and classifying the disturbances - providing an automatic assessment - techniques of digital signal processing can be helpful. The Wavelet Transform has become a very efficient tool for the analysis of voltage or current signals, obtained immediately after disturbances occurrences in the network. This paper presents a methodology based on the Discrete Wavelet Transform to implement this process. It uses a comparison between distribution curves of signals energy, with and without disturbance. This is done for different resolution levels of its decomposition in order to obtain descriptors that permit its classification, using artificial neural networks.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Influence of Signal Pre-Processing in the Efficiency of Algorithms Based on Neural Networks for Disturbance Classification




    Publication date :

    2007-04-01


    Size :

    4795364 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    "Object Based Image Classification Using Neural Networks Image Processing"

    Karmakar, G. C. / Rahman, S. M. / Bignall, B. | British Library Conference Proceedings | 1998


    Soft Stepping and Disturbance Compensation through Rotary Acceleration Signal Processing

    Starin, S. / Rodriguez, T. / European Space Agency et al. | British Library Conference Proceedings | 2003



    Ship classification based on convolutional neural networks

    Yang, Yang / Ding, Kaifa / Chen, Zhuang | Taylor & Francis Verlag | 2022


    Electroencephalogram signal classification method based on hybrid neural network

    CHEN WANZHONG / WANG ZHENG | European Patent Office | 2023

    Free access