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。

SIMULATING METHOD OF MAGNETIZING INRUSH CURRENT OF POWER TRANSFORMERS USING CONCEPT OF INSTANTANEOUS POWER

In this paper,a new simulating method is presented,using only the normal magnetizing curve (B-H) of the transformer core material,its geometric dimensions,the no-load power loss data and the concept of instantaneous power. At the end of this paper the simulating calculation using EMTP has been also performed for the same transformer. The comparison shows that the two sets of results are very close to each other,and proves the correctness of the new method. The new method presented in this paper is helpful to verify the correctness of the power transformer design,analyze the behavior of the transformer protection under switching and study the new transformer protection principles.

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.

Extracting Power Transformer Vibration Features by a Time-Scale-Frequency Analysis Method

In order to take advantage of the merits of WPT and HHT in feature extraction from vibration signals of power transformer, a time-scale-frequency analysis method is developed based on the combination of these two techniques. This method consists of two steps. First, the desirable wavelet packet nodes corresponding to characteristic frequency bands of power transformer are selected through a Correlation Degree Threshold Screening (CDTS) technique for reconstructing a time-domain signal that contains useful information of power transformer. Second, the HHT is then conducted on the reconstructed signal to track the instantaneous frequencies corresponding to natural characteristics of power transformer. Experimental results are provided by analyzing a real power transformer vibration signal. Compared with the features extracted by directly using HHT, the features obtained by the proposed method reveal clearer condition pattern of the transformer, which shows the potential of this method in condition monitoring of power transformer.

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。

Application of fuzzy analytic hierarchy process and neural network in power transformer risk assessment

In operation,risk arising from power transformer faults is of much uncertainty and complicacy.To timely and objectively control the risks,a transformer risk assessment method based on fuzzy analytic hierarchy process(FAHP) and artificial neural network(ANN) from the perspective of accuracy and quickness is proposed.An analytic hierarchy process model for the transformer risk assessment is built by analysis of the risk factors affecting the transformer risk level and the weight relation of each risk factor in transformer risk calculation is analyzed by application of fuzzy consistency judgment matrix;with utilization of adaptive ability and nonlinear mapping ability of the ANN,the risk factors with large weights are used as input of neutral network,and thus intelligent quantitative assessment of transformer risk is realized.The simulation result shows that the proposed method increases the speed and accuracy of the risk assessment and can provide feasible decision basis for the transformer risk management and maintenance decisions.

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。

Large Power Transformer Fault Diagnosis and Prognostic Based on DBNC and D-S Evidence Theory

Power transformer is a core equipment of power system, which undertakes the important functions of power transmission and transformation, and its safe and stable operation has great significance to the normal operation of the whole power system. Due to the complex structure of the transformer, the use of single information for condition-based maintenance (CBM) has certain limitations, with the help of advanced sensor monitoring and information fusion technology, multi-source information is applied to the prognostic and health management (PHM) of power transformer, which is an important way to realize the CBM of power transformer. This paper presents a method which combine deep belief network classifier (DBNC) and D-S evidence theory, and it is applied to the PHM of the large power transformer. The experimental results show that the proposed method has a high correct rate of fault diagnosis for the power transformer with a large number of multi-source data.

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 power electronic transformer applied in AC/DC hybrid distribution networks

The AC/DC hybrid distribution network is one of the trends in distribution network development, which poses great challenges to the traditional distribution transformer. In this paper, a new topology suitable for AC/DC hybrid distribution network is put forward according to the demands of power grid, with advantages of accepting DG and DC loads, while clearing DC fault by blocking the clamping double sub-module(CDSM) of input stage. Then, this paper shows the typical structure of AC/DC distribution network that is hand in hand. Based on the new topology, this paper designs the control and modulation strategies of each stage, where the outer loop controller of input stage is emphasized for its twocontrol mode. At last, the rationality of new topology and the validity of control strategies are verified by the steady and dynamic state simulation. At the same time, the simulation results highlight the role of PET in energy regulation.

Power Transformer No-Load Loss Prediction with FEM Modeling and Building Factor Optimization

Estimation of power transformer no-load loss is a critical issue in the design of distribution transformers. Any deviation in estimation of the core losses during the design stage can lead to a financial penalty for the transformer manufacturer. In this paper an effective and novel method is proposed to determine all components of the iron core losses applying a combination of the empirical and numerical techniques. In this method at the first stage all computable components of the core losses are calculated, using Finite Element Method (FEM) modeling and analysis of the transformer iron core. This method takes into account magnetic sheets anisotropy, joint losses and stacking holes. Next, a Quadratic Programming (QP) optimization technique is employed to estimate the incomputable components of the core losses. This method provides a chance for improvement of the core loss estimation over the time when more measured data become available. The optimization process handles the singular deviations caused by different manufacturing machineries and labor during the transformer manufacturing and overhaul process. Therefore, application of this method enables different companies to obtain different results for the same designs and materials employed, using their historical data. Effectiveness of this method is verified by inspection of 54 full size distribution transformer measurement data.

Detection of Mechanical Deformation in Old Aged Power Transformer Using Cross Correlation Co-Efficient Analysis Method

Detection of minor faults in power transformer active part is essential because minor faults may develop and lead to major faults and finally irretrievable damages occur. Sweep Frequency Response Analysis (SFRA) is an effective low-voltage, off-line diagnostic tool used for finding out any possible winding displacement or mechanical deterioration inside the Transformer, due to large electromechanical forces occurring from the fault currents or due to Transformer transportation and relocation. In this method, the frequency response of a transformer is taken both at manufacturing industry and concern site. Then both the response is compared to predict the fault taken place in active part. But in old aged transformers, the primary reference response is unavailable. So Cross Correlation Co-Efficient (CCF) measurement technique can be a vital process for fault detection in these transformers. In this paper, theoretical background of SFRA technique has been elaborated and through several case studies, the effectiveness of CCF parameter for fault detection has been represented.

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.

A Discrete State Event Driven Simulation based Losses Analysis for Multi-terminal Megawatt Power Electronic Transformer

At present,power electronic transformers(PETs)have been widely used in power systems.With the increase of PET capacity to the megawatt level.the problem of increased losses need to be taken seriously.As an important indicator of power electronic device designing,losses have always been the focus of attention.At present,the losses are generally measured through experiments,but it takes a lot of time and is difficult to quantitatively analyze the internal distribution of PET losses.To solve the above problems,this article first qualitatively analyzes the losses of power electronic devices and proposes a loss calculation method based on pure simulation.This method uses the Discrete State Event Driven(DSED)modeling method to solve the problem of slow simulation speed of large-capacity power electronic devices and uses a loss calculation method that considers the operating conditions of the device to improve the calculation accuracy.For the PET prototype in this article,a losses model of the PET is established.The comparison of experimental and simulation results verifies the feasibility of the losses model.Then the losses composition of PET was analyzed to provide reference opinions for actual operation.It can help pre-analyze the losses distribution of PET,thereby providing a potential method for improving system efficiency.