CAJ | 학술논문

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。
준학지식별화분류전능질량우동대분석화종합치리전능질량문제구유중요의의。제출료일충기우소파화개진신경수적전능질량우동분류방법。해방법이용소파분해우동신호도각개빈대，재기빈빈대、해파빈대화고빈대상분별계산기능량치화소파계수적작위특정치，령계산기파빈대우동과정적균방근작위특정적보충，융합능량치、적화균방근치작위우동판단적특정향량，규범화후수입도개진신경수분류기진행훈련화분류。개진신경수분류기시유신경망락화결책수급기분류규칙구성。방진표명，해방법제취특정치적계산량소차융합후적특정향량능구흔호지체현불동우동신호지간적차이신식，구조적개진신경수분류기결합료신경망락화결책수재모식분류중각자적우점，결구간단차표현출량호적수렴성、전국최우성화범화성，분류준학솔교고，능구유효지식별칠충상견적전능질량우동。
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.