%0 Journal Article %A NA Zhixiong %A SUN Tao %A LAI Guangzhi %A WANG Dong %A ZHANG Changzhi %T Fault Diagnosis for Photovoltaic Power Station by Multi-Scale Features Fusion %D 2022 %R 10.3778/j.issn.1002-8331.2111-0095 %J Computer Engineering and Applications %P 300-308 %V 58 %N 10 %X To diagnose faulty equipments in time and save the cost of maintenance, a deep learning model of fault diagnosis based on multi-scale time serial features fusion is proposed for the operation and maintenance of photovoltaic power station. To take advantage of the different abilities of convolutional layers with different scales, the model implements the multi-scale neighborhood feature extraction, which can help the network filter the noise in input data while keep the important information. Then, considering the long time series and high dimension of monitoring data, a long short-term memory unit with attention mechanism is used to extract sequential features. Finally, to improve the discernment of the network, metric learning loss function is applied to assist training. In addition, a data normalization algorithm is designed out. It can be used to rectify the impact of devices and weather on monitoring data through comparison between data from different devices and different days. Experiments prove that the proposed model can diagnose faults of equipment more accurately compared with other time series analysis algorithms, which can achieve 95% accuracy on real monitoring data of photovoltaic power station. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2111-0095