Price prediction of power transformer materials based on CEEMD and GRU

The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the prices of power transformer materials manifest as nonsmooth and nonlinear sequences.Hence,estimating the acquisition costs of power grid projects is difficult,hindering the normal operation of power engineering construction.To more accurately predict the price of power transformer materials,this study proposes a method based on complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)network.First,the CEEMD decomposed the price series into multiple intrinsic mode functions(IMFs).Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF.Then,an empirical wavelet transform(EWT)was applied to the aggregation sequence with a large sample entropy,and the multiple subsequences obtained from the decomposition were predicted by the GRU model.The GRU model was used to directly predict the aggregation sequences with a small sample entropy.In this study,we used authentic historical pricing data for power transformer materials to validate the proposed approach.The empirical findings demonstrated the efficacy of our method across both datasets,with mean absolute percentage errors(MAPEs)of less than 1%and 3%.This approach holds a significant reference value for future research in the field of power transformer material price prediction.

考虑特征重组与改进Transformer的风电功率短期日前预测方法

短期日前风电功率预测对电力系统调度计划制定有重要意义,该文为提高风电功率预测的准确性,提出了一种基于Transformer的预测模型Powerformer。模型通过因果注意力机制挖掘序列的时序依赖;通过去平稳化模块优化因果注意力以提高数据本身的可预测性;通过设计趋势增强和周期增强模块提高模型的预测能力;通过改进解码器的多头注意力层,使模型提取周期特征和趋势特征。该文首先对风电数据进行预处理,采用完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)将风电数据序列分解为不同频率的本征模态函数并计算其样本熵,使得风电功率序列重组为周期序列和趋势序列,然后将序列输入到Powerformer模型,实现对风电功率短期日前准确预测。结果表明,虽然训练时间长于已有预测模型,但Poweformer模型预测精度得到提升;同时,消融实验结果验证了模型各模块的必要性和有效性,具有一定的应用价值。

SVMD-PE-BP-Transformer短期光伏功率预测

考虑到光伏功率受气象因素变化影响而波动性大难以预测的问题,将逐次变分模态分解SVMD-排列熵PE与BPTransformer相结合,给出了一种组合预测方法,以下简称SPBT模型。在去除非相关因子的基础上,利用SOM聚类方法,对全年光伏数据进行3种类型的分类;针对光伏发电初始时序中所蕴含的重要信息,利用SVMD自适应K值的方法,对其进行分解。再利用PE方法计算各个子序列的熵值,即序列的起伏复杂程度,根据熵的大小,对频率接近的成分进行重构,将其分为两个区间:复杂度低的部分和复杂度高的部分。最后利用BP网络与Transformer分别对其进行预测,并对预测输出进行综合处理。该文以江苏省一光伏电站观测的气象与功率数据为例,通过比较试验验证了该模型的优势,该模型具有较低的预测误差,有助于提高预测精度。

基于生成对抗Transformer的电力负荷数据异常检测

电力负荷异常数据将给电力系统规划、负荷预测以及用能分析等带来较大的负面影响,因此亟须对负荷数据异常进行检测与识别。首先,针对电力负荷数据异常分类、原因及其特征开展分析。其次,改进传统Transformer编码器结构,采用多头注意力层代替掩码多头注意力层,同时移除前馈网络,以提高模型对负荷时序序列的全局注意力。基于生成对抗网络(generative adversarial networks,GAN)生成器与判别器的博弈结构,提出一种改进的GAN-Transformer模型,以更好地捕捉趋势性特征并加速模型收敛。然后,引入多阶段映射与训练方法,综合焦点分数打分机制,通过分阶段负荷序列重构帮助模型更好地提取负荷数据异常特征。最后,算例分析结果表明,GAN-Transformer模型在负荷数据异常检测精确率、召回率、F_(1)值以及训练时间方面均具有更优的性能,验证了所提方法的有效性和优越性。文中研究工作为基于深度学习进一步实现电力负荷数据异常分类与数据修复提供了有益参考。

基于卷积神经网络与Transformer的电能质量扰动分类方法

复杂电能质量扰动(power quality disturbances, PQD)的智能分类对于智能电网发展具有重要意义。扰动特征的提取与定位、模式识别与分类是电能质量扰动分类方法研究的难点。采用深度学习算法,将具有关注全局信息的Transformer与善于提取局部特征的卷积神经网络相融合,提出一种基于卷积神经网络(convolutional neural network, CNN)与Transformer的电能质量扰动分类方法,即CTranCBA。这种双深度学习模型分类方法主要是通过一维卷积神经网络提取电能质量扰动信号特征,利用Transformer自注意力机制引导模型关注序列中不同位置间的依赖关系,实现对扰动信号局部特征与全局特征的互补,克服了因感受野的限制而带来的识别不清、分类不准等问题。使用23种不同电能质量扰动信号,将CTranCBA与Deep-CNN、CNN-LSTM、CNN-CBAM方法进行比较。结果表明:该方法在分类准确率和抗噪性方面表现优异,可为电能质量扰动智能分类提供一种新的方法。

基于非平稳Transformer的超短期风电功率多步预测

针对风电预测中波动性和随机性造成的风电功率多步预测精确度不高的问题,提出一种基于非平稳Transformer的超短期风电功率多步预测模型。利用皮尔逊相关系数法(PCC)和主成分分析法(PCA)对风电功率及其影响因素的分析确定输入数据,结合可以提升非平稳时序预测效果的非平稳Transformer模型,高效充分地挖掘输入数据与输出功率的复杂关系,构建风电功率超短期预测模型。实例分析表明,所提方法对不同预测步长下的风电功率进行预测时均具有较高的预测精度,且预测结果更稳定。

ViTH:面向医学图像检索的视觉Transformer哈希改进算法

对海量的医学图像进行有效检索会给医学诊断和治疗带来极其重要的意义.哈希方法是图像检索领域中的一种主流方法,但在医学图像领域的应用相对较少.针对此,提出一种面向医学图像检索的视觉Transformer哈希改进算法.首先使用视觉Transformer模型作为基础的特征提取模块,其次在Transformer编码器的前、后端分别加入幂均值变换(Power-Mean Transformation,PMT),进一步增强模型的非线性性能,接着在Transformer编码器内部的多头注意力(Multi-Head Attention,MHA)层引入空间金字塔池化(Spatial Pyramid Pooling,SPP)形成多头空间金字塔池化注意力(Multi-Head Spatial Pyramid Pooling Attention,MHSPA)模块,该模块不仅可以提取全局的上下文特征,而且可以提取多尺度的局部上下文特征,并将不同尺度的特征进行融合.最后在输出幂均值变换层之后将提取到的特征分别通过两个多层感知机(Multi-Layer Perceptrons,MLPs),上分支的MLP用来预测图像的类别,下分支的MLP用来学习图像的哈希码.在损失函数部分,充分考虑了成对损失、量化损失、平衡损失以及分类损失来优化整个模型.在医学图像数据集ChestX-ray14和ISIC 2018上的实验结果表明,该研究所提出的算法相比于经典的哈希算法具有更好的检索效果.

基于CWT和优化Swin Transformer的风电齿轮箱故障诊断方法

针对传统故障诊断方法在风电齿轮箱运行故障诊断应用上的不足,提出一种基于小波变换(continuous wavelet transform, CWT)和优化Swin Transformer的风电齿轮箱故障诊断方法。该方法利用小波变换将风电齿轮箱振动信号转换为时频图;使用SuperMix数据增强算法对样本进行扩充;利用迁移学习技术将模型预训练参数用于训练和优化Swin Transformer模型;将训练完成的优化Swin Transformer模型应用于风场实际运维数据进行对比验证,分类准确率达到99.67%。验证结果表明该方法能够有效地实现风电齿轮箱故障诊断,并提高模型的识别准确率。

基于Transformer的分段供电故障诊断方法

分段供电系统是长初级直线电机的重要组成部分,对其应用有效的故障诊断方法有助于电机的正常工作和故障检修。根据实测三相电流波形特点,提出一种结合幅值特征序列提取算法和Transformer深度学习模型的分段供电故障诊断方法。除此之外,引入深度学习模型的解释性方法,实现模型对异常进行自监督辅助定位。最后,将以上方法在某型分段供电直线电机的试验数据上进行应用,并验证这些方法的有效性和可靠性。

基于Transformer改进强化学习的无人机电力巡检规划

为实现无人机电力巡检过程的全自主决策,针对传统强化学习轨迹规划存在的收敛速度慢、易陷入局部最优的问题,基于Transformer模型改进深度强化学习,设计了电量约束下的无人机充电巡检决策算法。首先建立对电力巡检任务场景的能耗模型和马尔可夫决策模型。然后分别设计了基于图神经网络的静态编码器和基于门控循环的动态编码器以提取不同类型环境数据,同时设计了基于多头注意力机制的解码器,输出不定长的全局充电巡检策略序列以预测未来奖励。最后对收敛后的推理模型在电力巡检仿真环境进行验证。仿真结果表明,相比于传统强化学习,所提算法可以提取地图深层状态特征,路径能耗降低了26.61%,并具有更好的收敛性。

基于双塔Transformer的电力系统暂态稳定评估

基于数据驱动的方法在电力系统暂态稳定评估的效率和精度提升上已经取得了一些研究成果。然而电力系统暂态过程中涉及多维度时序特征的变化,常规算法对特征的提取能力不足且缺乏可解释性,难以反映系统暂态过程中的动态行为。因此,构建了一个具有双塔结构的Transformer模型,以Transformer编码器作为特征提取器,考虑同一时刻不同维度的特征以及每一维度特征在不同时间步对系统暂态稳定的影响,并将其分别作为双塔结构Transformer模型的输入,训练和学习各特征通道和时间步对系统暂态稳定性的影响。通过融合机制,建立了由系统特征到系统稳定性的端到端的映射模型,实现了暂态稳定高精度的评估,并通过注意力热图可视化解释模型的决策过程。最后,在IEEE-39节点系统验证了所提方法的有效性。

Research on the longitudinal protection of a through-type cophase traction direct power supply system based on the empirical wavelet transform

This paper proposes a longitudinal protection scheme utilizing empirical wavelet transform(EWT)for a through-type cophase traction direct power supply system,where both sides of a traction network line exhibit a distinctive boundary structure.This approach capitalizes on the boundary’s capacity to attenuate the high-frequency component of fault signals,resulting in a variation in the high-frequency transient energy ratio when faults occur inside or outside the line.During internal line faults,the high-frequency transient energy at the checkpoints located at both ends surpasses that of its neighboring lines.Conversely,for faults external to the line,the energy is lower compared to adjacent lines.EWT is employed to decompose the collected fault current signals,allowing access to the high-frequency transient energy.The longitudinal protection for the traction network line is established based on disparities between both ends of the traction network line and the high-frequency transient energy on either side of the boundary.Moreover,simulation verification through experimental results demonstrates the effectiveness of the proposed protection scheme across various initial fault angles,distances to faults,and fault transition resistances.

融合Transformer和DeepLabv3+的电力线语义分割网络

为解决目前语义分割算法在电力线分割领域存在预测速度缓慢和分割精度不高的双重问题,提出一种电力线分割网络STDC-DeepLabv3+。首先,为提升网络预测速度,在编码器部分采用Swin Transformer V2设计轻量化的主干特征提取网络;其次,为提高分割精度,针对电力线的细长结构以及贯穿整幅图片的特点,提出动态蛇形空间金字塔池化(DSASPP)模块,同时,在解码器部分设计多尺度特征融合模块,使网络更好地利用不同层次的语义信息提取电力线特征,减少网络对电力线的漏分割现象;最后,引入坐标注意力(CA)机制减少背景干扰,进一步提升分割的准确率。实验结果表明,改进后的算法平均交并比(MIoU)和平均像素精度(MPA)分别达到了84.18%和92.85%,与现有分割算法相比,分割精度和预测速度均有所提升,预测速度与DeepLabv3+相比提升了93.92%。

Power Transformer Fault Diagnosis Using Random Forest and Optimized Kernel Extreme Learning Machine

Power transformer is one of the most crucial devices in power grid.It is significant to determine incipient faults of power transformers fast and accurately.Input features play critical roles in fault diagnosis accuracy.In order to further improve the fault diagnosis performance of power trans-formers,a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study.Firstly,the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration,gas ratio and energy-weighted dissolved gas analysis.Afterwards,a kernel extreme learning machine tuned by the Aquila optimization algorithm is implemented to adjust crucial parameters and select the optimal feature subsets.The diagnosis accuracy is used to assess the fault diagnosis capability of concerned feature subsets.Finally,the optimal feature subsets are applied to establish fault diagnosis model.According to the experimental results based on two public datasets and comparison with 5 conventional approaches,it can be seen that the average accuracy of the pro-posed method is up to 94.5%,which is superior to that of other conventional approaches.Fault diagnosis performances verify that the optimum feature subset obtained by the presented method can dramatically improve power transformers fault diagnosis accuracy.

基于SF-Transformer的智能教育平台短期电力负荷预测研究

建设智能教育平台是推动教育智能化的一个重要过程,但智能教育平台依赖的人工智能模型在训练过程中会消耗大量电力,因此,开展短期电力负荷预测对建设智能教育平台具有重要意义.针对在考虑多个属性开展短期电力负荷预测时,由于部分属性与电力负荷数据的相关性不强并且Transformer无法捕捉电力负荷数据的时间相关性,而导致电力负荷预测不够准确的问题,基于SR(Székely and Rizzo)距离相关系数、融合时间定位编码和Transformer,提出了一种短期电力负荷预测模型SF-Transformer.SF-Transformer通过SR距离相关系数对影响电力负荷数据的属性进行筛选,选择与电力负荷数据之间SR距离相关系数较大的属性.SF-Transformer采用一种全局时间编码与局部位置编码相结合的融合时间定位编码,有助于模型全面获取电力负荷数据的时间定位信息.在数据集上开展了实验,实验结果表明SF-Transformer与其他模型相比,在两种时长上进行电力负荷预测具有更低的均方根误差和平均绝对误差.

Fault Diagnosis of Power Transformer Based on Improved ACGAN Under Imbalanced Data

The imbalance of dissolved gas analysis(DGA)data will lead to over-fitting,weak generalization and poor recognition performance for fault diagnosis models based on deep learning.To handle this problem,a novel transformer fault diagnosis method based on improved auxiliary classifier generative adversarial network(ACGAN)under imbalanced data is proposed in this paper,which meets both the requirements of balancing DGA data and supplying accurate diagnosis results.The generator combines one-dimensional convolutional neural networks(1D-CNN)and long short-term memories(LSTM),which can deeply extract the features from DGA samples and be greatly beneficial to ACGAN’s data balancing and fault diagnosis.The discriminator adopts multilayer perceptron networks(MLP),which prevents the discriminator from losing important features of DGA data when the network is too complex and the number of layers is too large.The experimental results suggest that the presented approach can effectively improve the adverse effects of DGA data imbalance on the deep learning models,enhance fault diagnosis performance and supply desirable diagnosis accuracy up to 99.46%.Furthermore,the comparison results indicate the fault diagnosis performance of the proposed approach is superior to that of other conventional methods.Therefore,the method presented in this study has excellent and reliable fault diagnosis performance for various unbalanced datasets.In addition,the proposed approach can also solve the problems of insufficient and imbalanced fault data in other practical application fields.

基于轻量化Transformer模型的多变量风电功率预测

风电功率预测对电力调度和新能源管理极为重要。为准确高效预测多变量风电功率,提出一种基于轻量化Transformer(Light Transformer)的风电功率预测方法。首先,采用滚动序列建模方法,确定输入数据,然后采用Transformer预测模型,改进和精简原始结构,在前馈网络模块中使用GeLU激活函数来代替传统的ReLU激活函数,提升模型的质量,轻量化网络结构,利用多头注意力机制,加快模型训练速度,提升预测模型精度。

A Temperature Prediction Model for Oil-immersed Transformer Based on Thermal-circuit Theory

This paper proposed an improved temperature prediction model for oil-immersed transformer.The influences of the environmental temperature and heat-sinking capability changing with temperature were considered.When calculating the heat dissipation from the transformer tank to surroundings,the average oil temperature was selected as the node value in the thermal circuit.The new thermal models will be validated with the delivery experimental data of three transformers: a 220 kV-300 MV.A unit,a 110 kV40 MV.A unit and a 220 kV-75 MV.A unit.Meanwhile,the results from the proposed model were also compared with two methods recommended in the IEC loading guide.

基于强化学习和Transformer的输电线路缺陷智能检测方法研究

为了解决传统输电线路缺陷检测方法的不足,该文提出了一种基于强化学习和Transformer的输电线路缺陷智能识别方法。首先,采用具有较大感受野的空洞卷积网络(deterministic networking, DetNet)对输电线路巡检缺陷图像进行特征提取,继而使用深度Q网络(deepQ-network,DQN)筛选出包含前景信息的重要区域。其次,基于双线性注意力机制对背景区域特征向量进行投影压缩,使得融合特征向量聚焦于目标区域。最后,针对不确定缺陷检测结果定义可信度评测指标,构建Transformer网络编码层级的自适应调整机制,建立具有不同编码层级的Transformer模型库,以获取多模态缺陷的多层次差异化特征,采用Soft-NMS获取集成检测结果,提升识别模型的鲁棒性。通过对输电线路缺陷航拍图像进行了实验研究,该文方法检测精度平均值为89.7%,与其他算法相比具有更优的检测精度和泛化能力。

基于多尺度时间序列块自编码Transformer神经网络模型的风电超短期功率预测

风电超短期功率预测过程中对时间依赖性的有效捕捉与建模,将直接影响风电功率时间序列预测模型的稳定性和泛化性。为此,提出一种新型时序Transformer风电功率预测模型。模型架构在逻辑上分为时间块自编码、隐空间Transformer自注意力时序自回归、随机方差缩减梯度(stochastic variance reduce gradient,SVRG)优化3个部分。首先,依稀疏约束及低秩近似规则,风电功率时空数据被半监督映射至隐空间;其次,隐空间编码经由多头自注意力网络完成时序自回归预测;最后,模型采用方差缩减SVRG优化算法降低噪声,达到更高预测效能。实验结果表明,所提新型Transformer架构能稳定有效进行超短期风电功率预测,预测结果在准确性、泛化性方面相较于传统机器学习模型都有明显提升。