基于时钟循环神经网络的光伏故障诊断

Photovoltaic Fault Diagnosis Based on CW-RNN

光伏电站大多地处恶劣环境,遭受风沙雨雪腐蚀,电池板容易出现多类型故障,如何对故障进行有效识别与定位尤为重要。为此,提出了一种基于时钟循环神经网络(clockwork-recurrent neural network,CW-RNN)的光伏故障诊断策略。首先,建立了光伏阵列系统仿真模型,分析了光伏发电故障的成因,模拟了不同故障下的光伏阵列输出特征;其次,采用CW-RNN方法建立了故障诊断模型,对光伏阵列故障进行识别与定位;最后,基于实时数据库系统搭建了光伏发电故障分析平台,对所提出的故障诊断模型性能进行验证,结果表明其有效性和准确性,对光伏电站高效地进行故障准确识别与定位具有一定参考意义。

Most photovoltaic power stations are located in harsh environment,suffer from wind,sand,rain and snow corrosion,panels are prone to multiple types of failure.How to effectively identify and locate the fault is particularly important.Therefore,a strategy for photovoltaic fault diagnosis based on the clockwork-recurrent neural network(CW-RNN)was proposed.Firstly,a simulation model of photovoltaic array system was established,the causes of photovoltaic power generation faults were analyzed,and the output characteristics of photovoltaic array under different faults were simulated.Then,the CW-RNN method was used to establish a fault diagnosis model to identify and locate photovoltaic array faults.Finally,a photovoltaic power generation fault analysis platform was built based on the real-time database system,and the performance of the proposed fault diagnosis model was verified.The effectiveness and accuracy were verified,which has certain reference significance for the efficient and accurate fault identification and location of photovoltaic power stations.