基于空间相量模型的三相电压暂降扰动特征提取与分类

对暂降扰动进行精准类型识别是电能质量评估和治理的前提。现有暂降特征提取多是对单一扰动数据进行识别分类，采用数学变换法进行特征提取时数据维数高且计算量大。针对这些问题，提出了一种基于三相电压空间相量模型的多级暂降扰动可视化特征提取及分类方法。首先，将三相电压时域波形数据转换为空间相量模型；其次，使用 K-mean算法，将电压降落扰动聚类成平面内可视化的圆或椭圆；最后，利用逻辑回归算法对每一个聚类的圆或椭圆进行特征提取与分类。应用所提方法分别进行了单一扰动和多级扰动识别的仿真实验，结果表明，所提方法能有效识别A、Ca、Cb、Cc、Da、Db和Dc等七类电压暂降扰动。该方法降低了数据维度，减少了模型计算量，避免了对动态过渡过程的检测，降低了错误识别的风险，为多级电压暂降扰动的识别与分类提供了一种有效的辅助手段。

Feature extraction and classification of three-phase voltage dips based on a space phasor model

Accurate classification identification of voltage dip disturbance is a prerequisite for power quality assessment and management. Most of the existing voltage dips'' feature extraction consists of identifying and classifying single disturbance data. When using a mathematical transformation method for feature extraction, the data dimension is high and the amount of calculation is large. To solve these problems, a visual feature extraction and classification method based on a three-phase voltage space phasor model is proposed for multi-level voltage dips disturbance. First, the three-phase voltage waveform data are transformed into a spatial phasor model. Secondly, the voltage dip disturbances are clustered into visible circles or ellipses by using the K-mean algorithm. Finally, a logical regression algorithm is used to extract and classify the features of each cluster circle or ellipse. The simulation experiments for single disturbance and multi-level disturbance are done using the proposed method. The results show that the proposed method can effectively identify seven kinds of voltage-drop disturbances, such as A, Ca, Cb, Cc, Da, Db, Dc, etc. This method not only reduces the data dimension and the calculation amount of the model, but also reduces the risk of misidentification by eliminating the detection of the dynamic transition process. Overall it provides an effective means for the identification of multi-level voltage dip disturbances.This work is supported by the National Natural Science Foundation of China (No. 71471060).