基于小波和改进神经树的电能质量扰动分类

准确地识别和分类电能质量扰动对分析和综合治理电能质量问题具有重要意义。提出了一种基于小波和改进神经树的电能质量扰动分类方法。该方法利用小波分解扰动信号到各个频带,在基频频带、谐波频带和高频带上分别计算其能量值和小波系数熵作为特征值,另计算基波频带扰动过程的均方根作为特征的补充,融合能量值、熵和均方根值作为扰动判断的特征向量,规范化后输入到改进神经树分类器进行训练和分类。改进神经树分类器是由神经网络和决策树及其分类规则构成。仿真表明,该方法提取特征值的计算量小且融合后的特征向量能够很好地体现不同扰动信号之间的差异信息,构造的改进神经树分类器结合了神经网络和决策树在模式分类中各自的优点,结构简单且表现出良好的收敛性、全局最优性和泛化性,分类准确率较高,能够有效地识别七种常见的电能质量扰动。

Power quality disturbance classification based on a wavelet and improved neural tree

Precise identification and classification for power quality disturbances is significantly important to analyze and comprehensively cope with power quality problems. Based on wavelet and improved neural tree techniques, a new classification methodology for power quality disturbances is proposed. In the method, the disturbance signal is decomposed into different frequency bands, whilst energy values and wavelet coefficient entropies of the base, harmonic and high frequency bands are calculated as eigenvalues respectively. The root mean produced in the disturbance process of the base wave band is calculated as a supplement, which is then combined with the energy values and wavelet coefficient entropies as eigenvectors for judging the disturbances. Thereafter the eigenvectors are normalized and input into the improved neural tree classifier, composed of neural network, decision trees and classification rules, for training and classifying. Simulation results demonstrate the method has a small amount of calculation to extract eigenvalues and the obtained eigenvectors can adequately reflect the difference information for different disturbance signals. The improved neural tree classifier combines respective superiorities of the neural network and decision tree in pattern classification, thus the classifier presents good convergence, global optimality and generalization, and can effectively identify seven common power quality disturbances with a simple structure and high accuracy.