针对电缆附件局部放电盲源分离技术依赖高信噪比、常规脉冲波形特征适用性差的问题,提出一种基于均匀流形逼近与投影(uniform manifold approximation and projection,UMAP)的脉冲特征提取技术,以实现在含多种噪声干扰情况下的脉冲信号...针对电缆附件局部放电盲源分离技术依赖高信噪比、常规脉冲波形特征适用性差的问题,提出一种基于均匀流形逼近与投影(uniform manifold approximation and projection,UMAP)的脉冲特征提取技术,以实现在含多种噪声干扰情况下的脉冲信号分离。该方法以局部放电脉冲的时-频谱图为对象,通过UMAP算法对时-频谱图的全局特征进行降维特征提取,省去常规特征提取方法中定义和选择特征的过程,降维后的特征可有效表征不同放电脉冲间的相对差异,实现对不同放电脉冲的区分。根据电缆附件典型缺陷的局部放电实验数据验证可知,该方法可实现在-5 dB信噪比的白噪声、随机脉冲干扰以及窄带干扰下的局部放电脉冲特征提取,有效省去常规盲源分离过程中的部分去噪环节。相较于同类的流形逼近算法,UMAP算法对超参数敏度感较低,降维后的特征值分布较为稳定,有利于聚类算法的执行。展开更多
To attain the goal of carbon peaking and carbon neutralization,the inevitable choice is the open sharing of power data and connection to the grid of high-permeability renewable energy.However,this approach is hindered...To attain the goal of carbon peaking and carbon neutralization,the inevitable choice is the open sharing of power data and connection to the grid of high-permeability renewable energy.However,this approach is hindered by the lack of training data for predicting new grid-connected PV power stations.To overcome this problem,this work uses open and shared power data as input for a short-term PV-power-prediction model based on feature transfer learning to facilitate the generalization of the PV-power-prediction model to multiple PV-power stations.The proposed model integrates a structure model,heat-dissipation conditions,and the loss coefficients of PV modules.Clear-Sky entropy,characterizes seasonal and weather data features,describes the main meteorological characteristics at the PV power station.Taking gate recurrent unit neural networks as the framework,the open and shared PV-power data as the source-domain training label,and a small quantity of power data from a new grid-connected PV power station as the target-domain training label,the neural network hidden layer is shared between the target domain and the source domain.The fully connected layer is established in the target domain,and the regularization constraint is introduced to fine-tune and suppress the overfitting in feature transfer.The prediction of PV power is completed by using the actual power data of PV power stations.The average measures of the normalized root mean square error(NRMSE),the normalized mean absolute percentage error(NMAPE),and the normalized maximum absolute percentage error(NLAE)for the model decrease by 15%,12%,and 35%,respectively,which reflects a much greater adaptability than is possible with other methods.These results show that the proposed method is highly generalizable to different types of PV devices and operating environments that offer insufficient training data.展开更多
基金supported by the NationalNatural Science Foundation of China(No.6180802161)the Educational Commission of Liaoning Province of China(No.JZL201915401)We thank TopEdit(www.topeditsci.com)for its linguistic assistance during the preparation of this manuscript.
文摘To attain the goal of carbon peaking and carbon neutralization,the inevitable choice is the open sharing of power data and connection to the grid of high-permeability renewable energy.However,this approach is hindered by the lack of training data for predicting new grid-connected PV power stations.To overcome this problem,this work uses open and shared power data as input for a short-term PV-power-prediction model based on feature transfer learning to facilitate the generalization of the PV-power-prediction model to multiple PV-power stations.The proposed model integrates a structure model,heat-dissipation conditions,and the loss coefficients of PV modules.Clear-Sky entropy,characterizes seasonal and weather data features,describes the main meteorological characteristics at the PV power station.Taking gate recurrent unit neural networks as the framework,the open and shared PV-power data as the source-domain training label,and a small quantity of power data from a new grid-connected PV power station as the target-domain training label,the neural network hidden layer is shared between the target domain and the source domain.The fully connected layer is established in the target domain,and the regularization constraint is introduced to fine-tune and suppress the overfitting in feature transfer.The prediction of PV power is completed by using the actual power data of PV power stations.The average measures of the normalized root mean square error(NRMSE),the normalized mean absolute percentage error(NMAPE),and the normalized maximum absolute percentage error(NLAE)for the model decrease by 15%,12%,and 35%,respectively,which reflects a much greater adaptability than is possible with other methods.These results show that the proposed method is highly generalizable to different types of PV devices and operating environments that offer insufficient training data.
