Solar energy plays an increasingly important role in new energy sources, the stable operation of photovoltaic (PV) generation system for the entire energy supply system is gradually highlighted. In this article, the fault types of PV modules are classified into temporary and permanent shadow faults. A fault feature based on the prediction error of PV output and a novel intelligent quantization fault diagnosis method are proposed. First, the PV output sequence is processed by empirical mode decomposition and fine-to-coarse to remove the second-minute disturbance. Then, the clockwork recurrent neural network is used to predict the processed PV output to construct fault features. Finally, the support vector machine is used to identify the fault, so as to realize the diagnosis of shadow fault. The experimental results prove the effectiveness of the proposed diagnosis method, providing a new idea for the related research of PV system fault diagnosis, and further ensure the stable operation of the system.

中文翻译：

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基于光伏出力预测误差定量分析的影子故障诊断方法

太阳能在新能源中发挥着越来越重要的作用，光伏（PV）发电系统对整个能源供应系统的稳定运行逐渐凸显。本文将光伏组件的故障类型分为暂时性阴影故障和永久性阴影故障。提出了一种基于光伏输出预测误差的故障特征和一种新型的智能量化故障诊断方法。首先，PV输出序列经过经验模态分解和细到粗处理，去除第二分钟扰动。然后，利用发条循环神经网络对处理后的光伏输出进行预测，构建故障特征。最后利用支持向量机对故障进行识别，从而实现对影子故障的诊断。