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Detection and Classification of Power Quality Disturbancewaveform Using MRA Based Modified Wavelet Transfrom and Neural Networks

机译:基于MRA的改进小波变换和神经网络对电能质量扰动波形的检测和分类

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Detection and Classification of Power Quality Disturbancewaveform Using MRA Based Modified Wavelet Transfrom and Neural Networks In this paper, the modified wavelet based artificial neural network (ANN) is implemented and tested for power signal disturbances. The power signal is decomposed by using modified wavelet transform and the classification is carried by using ANN. Discrete modified wavelet transforms based signal decomposition technique is integrated with the back propagation artificial neural network model is proposed. Varieties of power quality events including voltage sag, swell, momentary interruption, harmonics, transient oscillation and voltage fluctuation are used to test the performance of the proposed approach. The simulation is carried out by using MATLAB software. The simulation results show that the proposed scheme offers superior detection and classification compared to the conventional approaches.
机译:利用基于MRA的改进小波变换和神经网络对电能质量扰动波形进行检测和分类本文实现了基于改进小波的人工神经网络(ANN),并进行了功率信号扰动测试。通过使用改进的小波变换来分解功率信号,并使用ANN进行分类。提出了基于离散改进小波变换的信号分解技术和反向传播人工神经网络模型。各种电能质量事件(包括电压骤降,骤升,瞬时中断,谐波,瞬态振荡和电压波动)用于测试所提出方法的性能。通过使用MATLAB软件进行仿真。仿真结果表明,与传统方法相比,该方案具有更好的检测和分类能力。

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