Since the efficiency of photovoltaic(PV) power is closely related to the weather,many PV enterprises install weather instruments to monitor the working state of the PV power system.With the development of the soft mea...Since the efficiency of photovoltaic(PV) power is closely related to the weather,many PV enterprises install weather instruments to monitor the working state of the PV power system.With the development of the soft measurement technology,the instrumental method seems obsolete and involves high cost.This paper proposes a novel method for predicting the types of weather based on the PV power data and partial meteorological data.By this method,the weather types are deduced by data analysis,instead of weather instrument A better fault detection is obtained by using the support vector machines(SVM) and comparing the predicted and the actual weather.The model of the weather prediction is established by a direct SVM for training multiclass predictors.Although SVM is suitable for classification,the classified results depend on the type of the kernel,the parameters of the kernel,and the soft margin coefficient,which are difficult to choose.In this paper,these parameters are optimized by particle swarm optimization(PSO) algorithm in anticipation of good prediction results can be achieved.Prediction results show that this method is feasible and effective.展开更多
针对无人机对光伏组件的故障(热斑和遮挡)诊断准确率较低和检测速度较慢的问题,提出了使用改进后的ELAN_MSE(Efficient Layer Aggregation Networks_Multipath Selective Enhancement)模块替换YOLOv7(You Only Look Once version 7)网络...针对无人机对光伏组件的故障(热斑和遮挡)诊断准确率较低和检测速度较慢的问题,提出了使用改进后的ELAN_MSE(Efficient Layer Aggregation Networks_Multipath Selective Enhancement)模块替换YOLOv7(You Only Look Once version 7)网络的ELAN模块,提高了有效特征学习的速率和准确率。首先,将YOLOv7的主干特征提取网络中的ELAN结构模块引入通道注意力机制SE(Squeeze-and-Excitation),提高特征提取的精确率;其次,增加了多路径卷积支路,实现中间层的跳跃结构连接,能够对目标特征图进行不同尺度的特征学习,提升故障缺陷识别的精度和检测速度。通过对数据增强后的光伏组件缺陷数据集进行实验验证,改进的YOLOv7算法与传统的YOLOv7和单发多框检测(Single Shot MultiBox Detector, SSD)算法对比,F1 Score分别提高了3.96%和5.98%,检测速度分别提高了1.43 ms和2.25 ms,为光伏组件故障检测提供了更有效的算法。展开更多
基金supported by the National Natural Science Foundation of China(61433004,61473069)IAPI Fundamental Research Funds(2013ZCX14)+1 种基金supported by the Development Project of Key Laboratory of Liaoning Provincethe Enterprise Postdoctoral Fund Projects of Liaoning Province
文摘Since the efficiency of photovoltaic(PV) power is closely related to the weather,many PV enterprises install weather instruments to monitor the working state of the PV power system.With the development of the soft measurement technology,the instrumental method seems obsolete and involves high cost.This paper proposes a novel method for predicting the types of weather based on the PV power data and partial meteorological data.By this method,the weather types are deduced by data analysis,instead of weather instrument A better fault detection is obtained by using the support vector machines(SVM) and comparing the predicted and the actual weather.The model of the weather prediction is established by a direct SVM for training multiclass predictors.Although SVM is suitable for classification,the classified results depend on the type of the kernel,the parameters of the kernel,and the soft margin coefficient,which are difficult to choose.In this paper,these parameters are optimized by particle swarm optimization(PSO) algorithm in anticipation of good prediction results can be achieved.Prediction results show that this method is feasible and effective.
