应用主成分分析约简电压暂降扰动源识别特征的方法

电压暂降是一种典型的电能质量扰动现象,准确识别引起电压暂降的扰动源类型是电能质量监测与管理的重要内容之一。为解决由于特征指标的相关性和冗余性而导致电压暂降扰动源识别准确率低的问题,提出一种基于主成分分析的电压暂降扰动源识别特征约简方法。通过分析单一电压暂降扰动源和复合电压暂降扰动源,利用小波系数从统计量、波形态、熵、能量等方面构建电压暂降特征指标。根据主成分分析方法对原始特征指标进行标准化处理,计算协方差矩阵并确定综合特征指标个数,最后得到约简后的综合特征指标。这些综合特征指标有效地消除了原始特征指标间的相关性和冗余性。采用常规方法构造分类器进行验证表明,约简后得到综合特征指标,不仅有效降低了输入到分类器中的特征向量个数,而且在不同噪声强度下对单一电压暂降扰动源和复合电压暂降扰动源的识别准确率明显高于利用原始特征指标进行的分类识别。

Method to reduce identification feature of different voltage sag disturbance source based on principal component analysis

Voltage sag is a typical power quality disturbance. Identifying the type of disturbance source causing voltage sag accurately is one of the important matters in power quality monitoring and management. Due to the correlativity and redundancy of the features, the identification method for voltage sag disturbance source has low accuracy. To resolve the problem, this paper proposes a method of feature reduction of voltage sag disturbance based on principal component analysis (PCA). Through the analysis of single disturbance source of voltage sag and complex disturbance source of voltage sag, multiple feature indices of voltage sag are obtained using wavelet coefficients in terms of statistics, wave morphology, entropy, energy, etc. These original feature indices are correlative and redundant. Based on PCA, the original feature indices are normalized, and then the covariance matrix is calculated, a couple of comprehensive feature indices after reduction can be obtained lastly. The correlativity and redundancy of the comprehensive feature indices are eliminated effectively. General classifier is used to verify the method. The simulation results show that comprehensive feature indices after reduction can effectively reduce the number of feature vectors which are input to SVM and the identification accuracy which is obtained using comprehensive feature indices is higher than the original features indices in the classification and identification of single and complex disturbance source of voltage sag under different noisy conditions.