基于二维离散余弦S变换的电能质量扰动类型识别

为获得可靠的高质量电能,提高电能质量扰动(Power Quality Distrubances, PQD)类型识别准确率,提出了一种基于二维离散余弦S变换(2D-DCST)的PQD类型识别方法。首先在数学模型的基础上,生成包括7种复合扰动在内的17类不同的电能质量事件。然后将一维的PQD信号转换成行列相等的二维信号,利用2D-DCST方法从二维信号中得到其振幅矩阵,对振幅矩阵提取基于统计、能量和图像的特征。再使用第二代非支配排序遗传算法(NSGA-Ⅱ)将提取的大量特征降维成少量有用的特征组。最后对所选特征使用支持向量机(SVM)分类器,构建一个分类准确率高、特征数目少的类型识别模型。实验结果表明,该方法能够准确高效地识别17类电能质量事件,并且有较好的抗噪性。同时对复合扰动也有较高的识别准确率,为电能质量扰动类型识别问题提供了新的方法。

Power quality disturbance type identification based on a two-dimensional discrete cosine S-transform

To obtain reliable high-quality power and improve the accuracy of Power Quality Disturbance (PQD) type identification, a new PQD type based on a Two-Dimensional Discrete Cosine S-Transform (2D-DCST) is proposed. First, based on mathematical models, 17 kinds of power quality events including 7 kinds of complex disturbances are generated. Then, one-dimensional PQD signals are upgraded into two-dimensional signals with equal rows and columns. An amplitude matrix is obtained from the two-dimensional signals using the 2D-DCST method. The statistics, energy and image features of the amplitude matrix are first extracted, and then reduced into a small number of useful feature groups using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). Finally, a Support Vector Machine (SVM) classifier is used to construct a type identification model with high identification accuracy and few features. Experimental results show that the method can identify 17 kinds of power quality events accurately and efficiently and also exhibits a good noiseproof feature. At the same time, the method also has a high identification accuracy for complex disturbances. This provides a new method for power quality disturbance type identification. This work is supported by the National Natural Science Foundation of China (No. 61672032) and Anhui Science and Technology Major Project (No. 18030901018).