针对光伏逆变器一维时序信号输入卷积神经网络时无法充分捕获时间和局部特征的问题,提出一种基于格拉姆角场（GAF）与改进的深度残差网络（ResNet）结合的光伏逆变器开路故障诊断模型。采用双通道GAF编码方法将一维电流信号映射为不同像素分布的二维故障特征图像,将特征图像作为ResNet的输入,保留了数据在时间维度的相关性。ResNet在卷积神经网络中引入残差模块来解决过拟合的问题,加入压缩和激励（SE）注意力机制改进残差模块后进行图像压缩、特征重用,增强了重要特征信息,使ResNet能更深入挖掘图像信息,充分捕获局部特征,结合Swish函数和Ranger优化器优化ResNet,大幅降低模型训练难度。实验结果表明,该方法对光伏逆变器开路故障诊断准确率达99.41%,与其他模型相比,具有更好的特征提取效果和诊断速度。
Abstract
Aiming at the problem that the one-dimensional time series signals of PV inverters cannot adequately capture the temporal and local features when they are input into the convolutional neural network, a PV inverter open-circuit fault diagnosis model based on the combination of Gramain angular fields （GAF） and improved deep residual network （ResNet） is proposed. Utilizing GAF encoding method with two channels, the one-dimensional current signal is mapped into a two-dimensional fault feature image with distinct pixel distributions. Using the feature images as the input to ResNet preserves the temporal correlation of the data. ResNet incorporates residual modules in convolutional neural networks to mitigate overfitting. An improved version of the residual module includes compression and squeeze-and-excitation （SE） attention mechanisms for image compression and feature reuse, enhancing important feature information. These enhancements enable ResNet to delve deeper into image information and fully capture local features. Combining the Swish function and Ranger optimizer to optimize ResNet, the training difficulty of the model is significantly reduced. The experimental results show that the method has an accuracy of 99.41% for diagnosing open circuit faults in PV inverters, and has better feature extraction effect and diagnosis speed compared with other models.
关键词
光伏逆变器 /
故障诊断 /
特征提取 /
格拉姆角场 /
残差网络
Key words
photovoltaic inverter /
fault detection /
feature extraction /
Gramian angular field /
residual network
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基金
河南省科技攻关项目（202102210094）; 国家重点研发计划专项（2016YFC0600906）
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