Disturbance Recognition of Power Quality Based on Wavelet Packet-Energy Entropy
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摘要: 提出了一种基于小波包能量熵的电能质量扰动识别方法。该方法对仿真的扰动电压信号进行4层小波包分解,提取小波包能量熵特征向量,利用主分量分析法提取电压信号的小波包特征向量并输入到概率神经网络(PNN)进行扰动识别,实现了扰动样本的最优压缩,简化了扰动分类中神经网络分类器的结构,提高了神经网络扰动识别的速度和精度。仿真结果表明,该方法具有良好的扰动识别能力。Abstract: A disturbances recognition method of power quality based on wavelet packet-energy entropy(WP-EE) was put forward,in which four layers wavelet packet decomposition of simulaed disturbance voltage signals were performed and characteristic vectors of wavelet packet energy entropy were extracted,principal components analysis(PCA) theory was used to extract characteristic vectors of wavelet packet of the voltage signal and the characteristic vectors were put into probabilistic neural network(PNN) for disturbance recognition.The method realizes optimum compression of disturbance data,simplifies structure of neural network classifier in disturbance classify,and enhances speed and precision of disturbance recognition.The simulation results showed the method has a very good disturbance recognition ability.
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