基于注意力机制优化电弧特征的光伏直流电弧故障检测方法

Photovoltaic DC Arc Fault Detection Method Based on Attention Mechanism Optimizing Arc Characteristics

光伏系统中,因绝缘老化或接线松动而出现的直流串联电弧故障极易引发电气火灾。因此,光伏系统必须安装电弧故障检测装置,而其易因阴影遮挡和逆变器启动引发的直流侧高频噪声而误跳闸。将注意力机制和一维卷积神经网络相结合,提出一种基于注意力权重优化电弧特征的电弧故障检测方法。通过可视化电弧特征贡献权重,提取8~18 kHz和28~38 kHz电弧关键特征频段,并剔除8~23 kHz频段中的干扰特征频段。经验证,使用关键电弧特征训练的电弧故障检测模型可以成功避免阴影遮挡和逆变器启动过程带来的误动,最终使电弧检测准确率提高到99.33%。

The DC series arc fault in photovoltaic systems caused by insulation aging or loose wiring is highly prone to electrical fires.Therefore,the arc fault detection devices must be installed in photovoltaic systems.However,the arc fault detection devices easily malfunction due to DC-side high-frequency noise caused by shadow occlusion and inverter startup.A novel arc fault detection method is proposed based on attention weight screening of arc features by combining the attention mechanism with the 1d convolutional neural network.By visualizing the contribution weight of arc features,the critical feature bands of 8~18 kHz and 28~38 kHz are extracted,and the interference arc features bands in the 8~23 kHz frequency band are removed.It has been verified that the arc fault detection model trained with the key arc features can successfully avoid the false activation caused by shadow occlusion and inverter startup,and the an arc detection accuracy of 99.33%is ultimately achieved.