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
针对光伏系统中的接地故障检测问题,提出一种基于扩展频谱时域反射法(spread spectrum time domain reflectometry,SSTDR)的接地故障检测方法。在分析高频信号下光伏系统等效模型的基础上,将光伏系统整体视为传输线终端阻抗,当发生接地...针对光伏系统中的接地故障检测问题,提出一种基于扩展频谱时域反射法(spread spectrum time domain reflectometry,SSTDR)的接地故障检测方法。在分析高频信号下光伏系统等效模型的基础上,将光伏系统整体视为传输线终端阻抗,当发生接地故障时其阻抗值会产生变化,通过对SSTDR自相关值进行数值分析处理,并与无接地故障光伏系统的自相关值进行比较,实现光伏接地故障检测。相较于常规检测方法,该方法无需测量光伏系统的电压、电流、温度、辐照度等参数,可消除传统检测方法中的检测盲点,且具有较强的鲁棒性。实验验证了方法在不同光照强度、不同接地电阻、双点接地等故障情况下的可行性和有效性。展开更多
This paper examines the progression and advancements in fault detection techniques for photovoltaic (PV) panels, a target for optimizing the efficiency and longevity of solar energy systems. As the adoption of PV tech...This paper examines the progression and advancements in fault detection techniques for photovoltaic (PV) panels, a target for optimizing the efficiency and longevity of solar energy systems. As the adoption of PV technology grows, the need for effective fault detection strategies becomes increasingly paramount to maximize energy output and minimize operational downtimes of solar power systems. These approaches include the use of machine learning and deep learning methodologies to be able to detect the identified faults in PV technology. Here, we delve into how machine learning models, specifically kernel-based extreme learning machines and support vector machines, trained on current-voltage characteristic (I-V curve) data, provide information on fault identification. We explore deep learning approaches by taking models like EfficientNet-B0, which looks at infrared images of solar panels to detect subtle defects not visible to the human eye. We highlight the utilization of advanced image processing techniques and algorithms to exploit aerial imagery data, from Unmanned Aerial Vehicles (UAVs), for inspecting large solar installations. Some other techniques like DeepLabV3 , Feature Pyramid Networks (FPN), and U-Net will be detailed as such tools enable effective segmentation and anomaly detection in aerial panel images. Finally, we discuss implications of these technologies on labor costs, fault detection precision, and sustainability of PV installations.展开更多
光伏故障检测对光伏电站智能运维具有重要意义。针对光伏组件红外图像中热斑目标小、难检测的问题,研究了基于改进Faster R CNN的光伏组件红外热斑故障检测模型。将Swin Transformer作为Faster R CNN模型中的特征提取模块,捕获图像的全...光伏故障检测对光伏电站智能运维具有重要意义。针对光伏组件红外图像中热斑目标小、难检测的问题,研究了基于改进Faster R CNN的光伏组件红外热斑故障检测模型。将Swin Transformer作为Faster R CNN模型中的特征提取模块,捕获图像的全局信息,建立特征之间的依赖关系,提高模型的建模能力;进一步利用BiFPN进行特征融合,改善了热斑故障由于目标小和特征不明显容易被模型忽略掉的问题;同时为了抑制光伏红外图像中背景和噪声的干扰,加入轻量级注意力模块CBAM,使模型更加关注重要通道和关键区域,提高对热斑故障检测精度。在自建光伏组件图像数据集上进行实验,热斑故障检测精度高达915,验证了本文模型对光伏组件热斑故障检测的有效性。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.
文摘针对光伏系统中的接地故障检测问题,提出一种基于扩展频谱时域反射法(spread spectrum time domain reflectometry,SSTDR)的接地故障检测方法。在分析高频信号下光伏系统等效模型的基础上,将光伏系统整体视为传输线终端阻抗,当发生接地故障时其阻抗值会产生变化,通过对SSTDR自相关值进行数值分析处理,并与无接地故障光伏系统的自相关值进行比较,实现光伏接地故障检测。相较于常规检测方法,该方法无需测量光伏系统的电压、电流、温度、辐照度等参数,可消除传统检测方法中的检测盲点,且具有较强的鲁棒性。实验验证了方法在不同光照强度、不同接地电阻、双点接地等故障情况下的可行性和有效性。
文摘This paper examines the progression and advancements in fault detection techniques for photovoltaic (PV) panels, a target for optimizing the efficiency and longevity of solar energy systems. As the adoption of PV technology grows, the need for effective fault detection strategies becomes increasingly paramount to maximize energy output and minimize operational downtimes of solar power systems. These approaches include the use of machine learning and deep learning methodologies to be able to detect the identified faults in PV technology. Here, we delve into how machine learning models, specifically kernel-based extreme learning machines and support vector machines, trained on current-voltage characteristic (I-V curve) data, provide information on fault identification. We explore deep learning approaches by taking models like EfficientNet-B0, which looks at infrared images of solar panels to detect subtle defects not visible to the human eye. We highlight the utilization of advanced image processing techniques and algorithms to exploit aerial imagery data, from Unmanned Aerial Vehicles (UAVs), for inspecting large solar installations. Some other techniques like DeepLabV3 , Feature Pyramid Networks (FPN), and U-Net will be detailed as such tools enable effective segmentation and anomaly detection in aerial panel images. Finally, we discuss implications of these technologies on labor costs, fault detection precision, and sustainability of PV installations.
文摘光伏故障检测对光伏电站智能运维具有重要意义。针对光伏组件红外图像中热斑目标小、难检测的问题,研究了基于改进Faster R CNN的光伏组件红外热斑故障检测模型。将Swin Transformer作为Faster R CNN模型中的特征提取模块,捕获图像的全局信息,建立特征之间的依赖关系,提高模型的建模能力;进一步利用BiFPN进行特征融合,改善了热斑故障由于目标小和特征不明显容易被模型忽略掉的问题;同时为了抑制光伏红外图像中背景和噪声的干扰,加入轻量级注意力模块CBAM,使模型更加关注重要通道和关键区域,提高对热斑故障检测精度。在自建光伏组件图像数据集上进行实验,热斑故障检测精度高达915,验证了本文模型对光伏组件热斑故障检测的有效性。
