As a result of the widespread use of power electronic equipment and the increase in consumption, the importance of effective energy policies and the smart grid begins to increase. Nonlinear loads and other loads in electric power systems are considered as the main reason for power quality disturbance. Distortions in signal quality and shape due to power quality disturbance cause a decrease in total efficiency. The proposed hybrid convolutional neural network method consists of a 1D convolutional neural network structure and a 2D convolutional neural network structure. The features acquired by these two convolutional neural network architectures are classified using the fully connected layer, which is traditionally used as the classifier of convolutional neural network architectures. Power signals are processed using a 1D convolutional neural network in their original form. Then these signals are converted into images and processed using a 2D convolutional neural network. Then, feature vectors generated by 1D and 2D convolutional neural networks are combined. Finally, this combined vector is classified by a fully connected layer. The proposed method is well suited to the nature of signal processing. It is a novel approach that covers the steps of an expert examining a signal. The proposed framework is compared with other state-of-the-art power quality disturbance classification methods in the literature. While the proposed method's classification performance is relatively high compared to other methods, the computational complexity is almost the same.

由于电力电子设备的广泛使用和能耗的增加，有效的能源政策和智能电网的重要性开始提高。电力系统中的非线性负载和其他负载被认为是造成电能质量扰动的主要原因。由于电能质量干扰而导致的信号质量和形状失真会导致总效率降低。提出的混合卷积神经网络方法由一维卷积神经网络结构和二维卷积神经网络结构组成。这两种卷积神经网络体系结构获得的特征是使用全连接层进行分类的，该层在传统上用作卷积神经网络体系结构的分类器。功率信号使用一维卷积神经网络以其原始形式进行处理。然后将这些信号转换为图像并使用2D卷积神经网络进行处理。然后，将一维和二维卷积神经网络生成的特征向量进行组合。最后，该组合矢量通过完全连接的层进行分类。所提出的方法非常适合于信号处理的性质。这是一种新颖的方法，涵盖了专家检查信号的步骤。所提出的框架与文献中其他最新的电能质量扰动分类方法进行了比较。尽管与其他方法相比，该方法的分类性能较高，但计算复杂度几乎相同。然后将这些信号转换为图像并使用2D卷积神经网络进行处理。然后，将一维和二维卷积神经网络生成的特征向量进行组合。最终，该组合矢量通过完全连接的层进行分类。所提出的方法非常适合于信号处理的性质。这是一种新颖的方法，涵盖了专家检查信号的步骤。所提出的框架与文献中其他最新的电能质量扰动分类方法进行了比较。尽管与其他方法相比，该方法的分类性能较高，但计算复杂度几乎相同。然后将这些信号转换为图像并使用2D卷积神经网络进行处理。然后，将一维和二维卷积神经网络生成的特征向量进行组合。最终，该组合矢量通过完全连接的层进行分类。所提出的方法非常适合于信号处理的性质。这是一种新颖的方法，涵盖了专家检查信号的步骤。所提出的框架与文献中其他最新的电能质量扰动分类方法进行了比较。尽管与其他方法相比，该方法的分类性能较高，但计算复杂度几乎相同。该组合向量由完全连接的层分类。所提出的方法非常适合于信号处理的性质。这是一种新颖的方法，涵盖了专家检查信号的步骤。所提出的框架与文献中其他最新的电能质量扰动分类方法进行了比较。尽管与其他方法相比，该方法的分类性能较高，但计算复杂度几乎相同。该组合向量由完全连接的层分类。所提出的方法非常适合于信号处理的性质。这是一种新颖的方法，涵盖了专家检查信号的步骤。所提出的框架与文献中其他最新的电能质量扰动分类方法进行了比较。尽管与其他方法相比，该方法的分类性能较高，但计算复杂度几乎相同。