基于深度残差网络的光伏故障诊断模型研究

分布式光伏电站的部署环境较为复杂,在实际运行中难免会产生多种故障.针对上述问题,提出了一种基于深度残差网络结构的分布式光伏电站故障诊断模型,对光伏电站的设备运行时序数据进行分析处理,实现对故障类别的快速准确判断.该模型使用一维卷积核感知时序数据特征,通过多级卷积结构提升模型的诊断能力,并采用残差结构解决模型深度增加造成的梯度消失问题,加速了深度模型的训练.光伏电站的测试数据实验结果表明,提出的模型相较于多种常见的智能模型具有较高的故障诊断准确度.该模型的推广使用不仅可以大幅减少光伏电站故障巡检投入,而且还能够提高光伏电站故障诊断效率.

Prediction of crowd massing abnormity based on multi-scale convolutional neural network

The deployment environment of distributed photovoltaic power plants is relatively complicated, and many kinds of faults inevitably occur during the actual operation. In order to solve the above problem, this paper proposes a fault diagnosis model of distributed photovoltaic power stations based on deep residual network. It analyzes and processes the sequence data of equipment operation, and achieves rapid and accurate judgment of fault categories. This model applies a one-dimensional convolution kernel to perceive the characteristics of time series data. Then, it uses a multi-level convolution structure to increase the diagnostic ability. Finally, the residual network is utilized to solve the problem of gradient disappearance caused by the increase of model depth, and accelerate the training of the deep model. The experimental results based on the power station test data show that the residual network model achieves higher fault diagnosis accuracy than several state-of-the-art intelligent models. The application of this model can not only greatly reduce the investment in fault inspection of photovoltaic power plants, but also improve the efficiency of fault diagnosis of photovoltaic power plants.