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首页> 外文会议>International Conference on Signal Processing, Communication, Power and Embedded System >Wavelet approach and Support Vector Networks based Power Quality EventsRecognition and Categorisation
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Wavelet approach and Support Vector Networks based Power Quality EventsRecognition and Categorisation

机译：小波方法和支持向量基于Vircuity Femply EventRognition和分类

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The power quality disturbances are still unsolved problems due to the increased power electronic load getting into the system. This is as a result of continuous use of non-linear loads and the faults that occur on the power system. The power quality disturbances that often occur include voltage sag, voltage swell, harmonics, transients, flicker and interruption. To detect power quality disturbances, discrete wavelet approach was adopted in the feature extraction process. For the classification of the power disturbances support vector networks was used. Synthetic power quality signals were used in this paper. In this work, Mathematical models for various PQ signal disturbances is developed and validated against real time signal. The synthetic signal was generated using synthetic parametric equations; the signal was filtered to remove unwanted noise. Events such as dip, swell and interruption were introduced to the signal. Discrete Wavelet Transform is then used for the detection of the events and change points, the signals of each event were trained using Support Vector Networks. The results obtained from the developed system show a high degree of classification rate.
机译：由于进入系统的电力电子负荷增加，电能质量扰动仍然是未解决的问题。这是由于连续使用非线性载荷和电力系统上发生的故障。经常发生的电源质量扰动包括电压凹槽，电压膨胀，谐波，瞬态，闪烁和中断。为了检测功率质量障碍，在特征提取过程中采用了离散小波法。对于电力干扰的分类，使用支持向量网络。本文使用了合成功率质量信号。在这项工作中，针对实时信号开发和验证了各种PQ信号干扰的数学模型。使用合成参数方程产生合成信号;滤波信号以消除不需要的噪声。引入诸如DIP，膨胀和中断之类的事件。然后将离散小波变换用于检测事件和改变点，使用支持向量网络训练每个事件的信号。从发发系统获得的结果显示出高度的分类率。

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