格拉米角场和图卷积神经网络识别复杂电能质量扰动

Complex power quality disturbance identification based on Gramian angular field and graph convolution neural network

可再生能源并网引起的电能质量问题往往伴随着复杂的电能质量扰动,因此电能质量的扰动类型识别是后续电能污染控制的首要任务。该文针对复杂电能质量扰动的分类问题,提出一种基于格拉米角场和图卷积神经网络的复杂电能质量扰动识别方法。首先,通过搭建直驱型风力发电动态模拟实验平台模拟生成不同的单一扰动和复杂扰动信号,将原始一维时间序列的电能质量扰动数据通过格拉米角场值的密度分布进行表示,生成二维格拉米角场值密度图;然后,通过图卷积神经网络对输入图像进行无监督特征学习,自动提取数据特征的稀疏特征表达;最后,通过softmax分类器进行微调训练,并输出电能质量扰动事件分类结果。通过仿真和硬件实验表明:对于复杂电能质量的扰动类型识别,该方法能保持94.87%左右的识别准确率,在算法训练速度和识别精度方面,优于其他常见深度学习算法,具有良好的工程应用前景。

Power quality problems caused by renewable energy integration are often accompanied by complex power quality disturbances,so the identification of power quality disturbance types is the primary task of power pollution control.Aiming at the classification of complex power quality disturbances,this paper proposes a recognition method of complex power quality disturbances based on Gramian angular field and graph convolution neural network.Firstly,different single disturbance and complex disturbance signals are generated by building a direct drive wind power generation dynamic simulation experimental platform,and the power quality disturbance data of the original one-dimensional time series is represented by the density distribution of the Gramian angular field value to generate a two-dimensional Gramian angular field value density map;then,the unsupervised feature analysis of the input image is performed by graph convolution neural network Finally,the softmax classifier is used for fine-tuning training,and the classification results of power quality disturbance events are output.The simulation and hardware experiments show that this method can keep the recognition accuracy of 94.87%for the disturbance type recognition of complex power quality.In terms of algorithm training speed and recognition accuracy,there are other common deep learning algorithms,which has a good engineering application prospect.