基于改进ShuffleNetV2模型的光伏板灰尘识别

鉴于灰尘积聚是光伏组件功率损失的主要因素之一, 针对灰尘颗粒的特性及克服利用扫描电子显微镜成本昂贵问题, 提出了一种利用改进的ShuffleNetV2模型来识别光伏板上的灰尘. 以ShuffleNetV2网络模型为基础模型, 采用Mish激活函数, 将更好的特征信息深入神经网络; 然后运用混合深度卷积保证特征提取的丰富性; 最后利用坐标注意力机制模块替换ShuffleNetV2模型中基本单元右分支尾部的逐点卷积, 从而在提高精度的同时也减少了计算量. 实验结果表明, 所提改进的ShuffleNetV2模型与已有的经典模型相比, 准确度更高, 模型复杂度更低, 有效地证明了所给方案是可行的.

Identification of Dust on Photovoltaic Panel Based on Improved ShuffleNetV2 Model

Dust accumulation is one of the main factors of power loss of photovoltaic modules. In view of the characteristics of dust particles and the high cost of using scanning electron microscopy, this study proposes a scheme to identify dust on photovoltaic panels by using the improved ShuffleNetV2 model. On the basis of the ShuffleNetV2 network model, the Mish activation function is used to integrate the better feature information into the neural network; then the mixed depth convolution is used to ensure the richness of feature extraction. Finally, the coordinate attention mechanism module is used to replace the point-by-point convolution of the tail of the right branch of the basic unit in the ShuffleNetV2 model, so as to improve the accuracy and reduce the calculation amount. The experimental results show that the improved ShuffleNetV2 model has higher accuracy and lower complexity than the existing classical model, which effectively proves that the proposed scheme is feasible.