基于数字图像处理技术的暂态电能质量扰动分类

摘要

为改进暂态电能质量扰动分类方法的准确性，先将暂态电能质量扰动一维数据信号通过归一化处理转换为二维灰度图像，再应用伽马校正、边缘检测及峰谷检测等数字图像处理方法增强扰动特征，得到新的灰度图像和二值图像。提取二值图像的形态学特征值组成特征向量。通过概率神经网络实现暂态电能质量扰动分类。对所提方法进行了仿真计算和比较分析。结果表明，所提出的暂态电能质量扰动分类新方法改进了扰动分类的准确性，是一种有效可行的方法。%A new method is proposed to improve the accuracy of method for classifying transient power quality disturbance. First, the grayscale images are created by normalizing the data of disturbance voltage waveforms. Then image enhancement techniques, such as gamma correction and edge detection as well as peak detection methods, are employed to produce new grayscale images and binary images so as to make characteristics of the disturbance striking. The morphologic feature values are extracted from the binary images. At last, the probability neural network (PNN) is trained by the morphologic feature values and then used to classify transient power quality disturbance. The proposed new method for classifying transient power quality disturbance is simulated based on numerical examples. Simulation results show that the accuracy of the new method is better than the existing methods, it is effective and practical.