基于TensorFlow框架的有源配电网深度学习故障定位方法

随着大规模分布式电源(DG)接入配电网,配电网的结构由传统的辐射型变为多端电源结构,传统的故障定位方法不再完全满足含DG的配电网系统,对此提出一种基于深度学习的有源配电网故障定位方法。首先通过馈线监控终端采集过电流故障数据与节点电压数据,结合各电源出力数据,形成故障数据向量;然后使用Tensorflow构建基于全连接网络的深度神经网络模型,挖掘故障数据向量与故障支路之间的映射联系,形成故障定位模型;最后利用该模型在线定位故障并验证其有效性。模型测试结果表示,与反向传播神经网络、学习向量量化神经网络模型相比,深度学习模型收敛速度更快,故障定位准确率更高,同时在数据畸变或缺失时,模型具有较高的容错性。     

基于深度学习网络的轴承工件自动检测

针对实际生产中轴承滚子原始故障数据量少,数据集不平衡的问题,提出一种数据增强策略对原始的数据集进行扩充,并结合U-Net框架和轻量级深度学习模型构建了一个端到端的轴承滚子语义分割模型方法.通过结合U-Net框架和轻量级深度学习模型MobileNetV1、DenseNet121构建了端到端的轴承滚子语义分割模型LS-MobileNetV1、LS-DenseNet121,将所提模型基于迁移学习策略进行了训练,与其他模型进行对比实验分析.结果表明,与现有方法相比,本文方法在具有更少参数量的情况下实现了更高的分割精度与更具鲁棒性的检测效果,验证了所提方法的有效性.     

基于深度学习网络的轴承工件自动检测

针对实际生产中轴承滚子原始故障数据量少,数据集不平衡的问题,提出一种数据增强策略对原始的数据集进行扩充,并结合U-Net框架和轻量级深度学习模型构建了一个端到端的轴承滚子语义分割模型方法.通过结合U-Net框架和轻量级深度学习模型MobileNetV1、DenseNet121构建了端到端的轴承滚子语义分割模型LS-MobileNetV1、LS-DenseNet121,将所提模型基于迁移学习策略进行了训练,与其他模型进行对比实验分析.结果表明,与现有方法相比,本文方法在具有更少参数量的情况下实现了更高的分割精度与更具鲁棒性的检测效果,验证了所提方法的有效性.     

基于深度学习GRU网络的配电网理论线损计算方法

由于实际应用中对理论线损的精度要求不高,且传统计算方法所需电气参数较多,计算过程繁琐.因此,文中提出了一种基于深度学习门控循环单元(Gated Recurrent Unit,GRU)网络的配电网理论线损计算方法.为综合考虑主客观因素,将互信息理论和层次分析法相结合,进而确定所选电气参数对理论线损的影响权重;按照权重的大...     

Adapted random sampling patterns for accelerated MRI

Objective  

Variable density random sampling patterns have recently become increasingly popular for accelerated imaging strategies, as they lead to incoherent aliasing artifacts. However, the design of these sampling patterns is still an open problem. Current strategies use model assumptions like polynomials of different order to generate a probability density function that is then used to generate the sampling pattern. This approach relies on the optimization of design parameters which is very time consuming and therefore impractical for daily clinical use.     

A dynamic-model-based fault diagnosis method for a wind turbine planetary gearbox using a deep learning network

The planetary gearbox is a critical part of wind turbines, and has great significance for their safety and reliability. Intelligent fault diagnosis methods for these gearboxes have made some achievements based on the availability of large quantities of labeled data. However, the data collected from the diagnosed devices are always unlabeled, and the acquisition of fault data from real gearboxes is time-consuming and laborious. As some gearbox faults can be conveniently simulated by a relatively precise dynamic model, the data from dynamic simulation containing some features are related to those from the actual machines. As a potential tool, transfer learning adapts a network trained in a source domain to its application in a target domain. Therefore, a novel fault diagnosis method combining transfer learning with dynamic model is proposed to identify the health conditions of planetary gearboxes. In the method, a modified lumped-parameter dynamic model of a planetary gear train is established to simulate the resultant vibration signal, while an optimized deep transfer learning network based on a one-dimensional convolutional neural network is built to extract domain-invariant features from different domains to achieve fault classification. Various groups of transfer diagnosis experiments of planetary gearboxes are carried out, and the experimental results demonstrate the effectiveness and the reliability of both the dynamic model and the proposed method.     

互联电网频率响应标准

从电力系统一次调频原理出发,通过采集华东电网事故时的频率数据,详细分析华东电网的频率响应特性,用初始频率、准稳态频率、最大动态频率偏差等参数加以表征,解构了频率变化的不同阶段及其特性,揭示了电网一次调频能力的重要性。指出互联电网必须对发电机组的频率响应作必要的约束和规范,才能保证系统的一次调频能力在事故时完全发挥出来,否则容易导致系统低周减载装置动作,造成不必要的负荷损失,大幅度降低系统的可靠性。并结合电网的频率管理体系,指出了电网频率响应标准的具体发展方向。     

基于强化学习的互联电网CPS自校正控制

AGC是一个动态多级决策问题--马尔可夫决策过程(MDP),应用强化学习算法可有效地实现控制策略的在线学习和动态优化决策.引入Q学习算法作为强化学习核心算法,将CPS值看作包含AGC的电力系统"环境"所给的"奖励",依靠奖励值Q函数与CPS控制动作形成的闭环控制结构实现在线学习.学习目标是使CPS控制动作从环境获得的长期积累奖励值最大,从而快速自动地在线优化CPS控制系统的输出.仿真研究显示,引入强化学习自校正控制后显著增强了整个AGC系统的鲁棒性和适应性,有效提高了CPS考核合格率.     

A novel framework for evaluating the image accuracy of dynamic MRI and the application on accelerated breast DCE MRI

Objective

To develop a novel framework for evaluating the accuracy of quantitative analysis on dynamic contrast-enhanced (DCE) MRI with a specific combination of imaging technique, scanning parameters, and scanner and software performance and to test this framework with breast DCE MRI with Time-resolved angiography WIth Stochastic Trajectories (TWIST).

Materials and methods

Realistic breast tumor phantoms were 3D printed as cavities and filled with solutions of MR contrast agent. Full k-space raw data of individual tumor phantoms and a uniform background phantom were acquired. DCE raw data were simulated by sorting the raw data according to TWIST view order and scaling the raw data according to the enhancement based on pharmaco-kinetic (PK) models. The measured spatial and temporal characteristics from the images reconstructed using the scanner software were compared with the original PK model (ground truth).

Results

Images could be reconstructed using the manufacturer’s platform with the modified ‘raw data.’ Compared with the ‘ground truth,’ the RMS error in all images was <10% in most cases. With increasing view-sharing acceleration, the error of the initial uptake slope decreased while the error of peak enhancement increased. Deviations of PK parameters varied with the type of enhancement.

Conclusion

A new framework has been developed and tested to more realistically evaluate the quantitative measurement errors caused by a combination of the imaging technique, parameters and scanner and software performance in DCE-MRI.

     

基于深度强化学习的配电网负荷转供控制方法

随着城市规模的快速扩张以及电能替代的不断推进,配电网节点数大量增加,结构愈加复杂,发生故障后拓扑变化不确定性较大,传统负荷转供方法难以在短时间内给出高质量的解决方案。为此,提出基于深度强化学习的配电网负荷转供控制方法。将负荷转供过程视为一个马尔可夫决策过程,与配电网实时电气、拓扑数据进行交互,对联络开关与分段开关进行控制。为了提高算法的精度与泛化能力,针对算法动作策略加入了预模拟机制,调整了动作与学习的比例并采用自适应优化算法进行求解。算例分析表明,所提方法能够应对不同故障下配电网的拓扑变化,即时给出负荷恢复量、电网损耗、开关动作次数多方面最优的转供控制方案,这对于减小故障后的停电损失与提高用户满意度有着重要意义。     

交直流互联电网动态等值的实用化方法

针对同调等值法等值过程中各个环节存在的若干问题,提出了相应的实用化解决方法:通过母线电压波动曲线的相关性分析,基于母线电压同调辨识出外网保留节点;针对网络化简过程中出现的不符合物理规律的参数,采用人工删除高阻抗支路并适当微调附近节点上的负荷参数,或将负电阻参数调整为0使负电阻提供的有功功率分配到线路两端节点上,并调整两端节点有功负荷进行修正,从而提高动态等值精度。针对等值后动态响应效果,提出了分时段量化评估方法。以南方电网为实例,分析验证了所提方法的有效性。     

A multi-agent deep reinforcement learning based energy management for behind-the-meter resources

The future communities are becoming more and more electrically connected via increased penetrations of behind-the-meter (BTM) resources, specifically, electric vehicles (EVs), smart buildings (SBs), and distributed renewables. The electricity infrastructure is thus seeing increased challenges in its reliable, secure, and economic operation and control with increased and hard to predict demands (due to EV charging and demand management of SBs), fluctuating generation from renewables, as well as their plug-N-play dynamics. Reinforcement learning has been extensively used to enable network entities to obtain optimal policies. The recent development of deep learning has enabled deep reinforcement learning (DRL) to drive optimal policies for sophisticated and capable agents, which can outperform conventional rule-based operation policies in applications such as games, natural language processing, and biology. Furthermore, DRL has shown promising results in many resource management tasks. Numerous studies have been conducted on the application of single-agent DRL to energy management. In this paper, a fully distributed energy management framework based on multi-agent deep reinforcement learning (MADRL) is proposed to optimize the BTM resource operations and improve essential service delivery to community residents.     

基于深度学习的直流配电网分布鲁棒优化调度方法

随着分布式资源的大规模接入,直流配电网能量损耗小、控制灵活的优点凸显。针对直流配电网传统物理优化模型效率低的问题,提出了一种基于深度学习的直流配电网分布鲁棒优化(DRO)调度方法,其采用深度学习方法替代了基于场景的DRO模型的迭代求解过程,通过直接预测典型场景的最恶劣概率分布来提高模型求解效率。构建直流配电网基于场景的DRO物理模型,采用列与约束生成算法迭代求解生成深度学习的训练数据;以光伏出力、负荷、范数置信度为输入,以最恶劣概率分布为输出,构建深度神经网络模型;基于训练好的神经网络预测实时输入的光伏出力、负荷、范数置信度的最恶劣概率分布,构建最恶劣概率分布下的单层随机规划模型,获取等效的基于场景的DRO调度策略;采用33节点直流配电网系统为算例,验证所提方法在求解效率和计算精度方面的有效性。     

基于短时傅里叶变换和深度学习的牵引网过电压辨识

牵引网过电压严重影响电气化铁路正常运行,对牵引网过电压进行类型辨识有利于提高牵引供电系统的可靠性.针对牵引网过电压的非线性和不稳定性,本文利用短时傅里叶变换将过电压时域波形转化为二维的时频图;先通过局部特征提取和设置阈值,实现对铁磁谐振过电压的快速识别;再利用卷积神经网络的自学习能力挖掘时频图特征与牵引网过电压信号的深层次关系,实现对机车进出分相、断路器开闭操作过电压和高频谐振过电压的识别.实验结果表明,该方法的准确度在90％以上.     

dynoNet: A neural network architecture for learning dynamical systems

This article introduces a network architecture, called dynoNet, utilizing linear dynamical operators as elementary building blocks. Owing to the dynamical nature of these blocks, dynoNet networks are tailored for sequence modeling and system identification purposes. The back-propagation behavior of the linear dynamical operator with respect to both its parameters and its input sequence is defined. This enables end-to-end training of structured networks containing linear dynamical operators and other differentiable units, exploiting existing deep learning software. Examples show the effectiveness of the proposed approach on well-known system identification benchmarks.     

基于特征降维与分块的输电网概率最优潮流深度学习方法

概率最优潮流需要对非线性最优潮流问题进行重复求解,计算量较大,从而限制了其应用。提出一种基于特征降维、分块和深度神经网络辅助预测的最优潮流两阶段求解方法。在第一阶段,提出基于深度神经网络的最优潮流部分关键决策变量的优先辨识策略,以解决深度学习中因特征维度过高而导致的数值湮没问题,进而以最优潮流的结果特征为导向,基于关联性分析和聚类分析挖掘最优潮流输入与输出特征的关联性匹配度,并构建样本数据的分块特征库,以降低学习难度。在第二阶段,利用深度神经网络完成部分关键决策变量的分块映射,基于潮流模型恢复剩余状态变量,并对计算结果不收敛、不满足约束的情况进行修正,以恢复可行性。根据最优潮流两阶段求解方法构建概率最优潮流求解方法。仿真结果表明所提方法在最优潮流、概率最优潮流的求解速度和求解精度上均有较好的表现。     

基于深度强化学习的城市配电网多级动态重构优化运行方法

随着配电网分布式电源的大量接入以及城市区域负荷的快速发展,使得配电网运行环境愈发复杂。同时由于配电网重构涉及大量的开关状态二进制零散变量,现有优化方法很难求解大规模城市配电网重构问题。基于此,提出一种基于深度强化学习的城市配电网多级动态重构方法。首先,建立基于深度学习的配电网多级重构快速判断模型,通过该模型实现对重构级别在线决策,并对智能体动作空间进行降维。其次,使用含参数冻结和经验回放机制的深度Q网络对预测负荷、光伏能源输出功率等环境信息进行学习。以运行成本、电压偏移度以及负荷均衡度最优为目标,通过习得的策略集对配电网进行动态重构与运行优化。建立多智能体强化学习模型,对各个时段的不同重构主体进行联合优化。最后,通过算例分析验证了所提方法的有效性。     

一种基于深度强化学习算法的电网有功安全校正方法

电力系统有功安全校正对于保障电网安全运行具有重要意义。传统有功安全校正方法无法综合考虑系统潮流分布状态和机组的调整性能，求解效率低、涉及调整的机组多，存在调整反复的现象，在实际应用中具有一定困难。因此，采用深度强化学习算法，提出一种基于深度Q网络(Deep Q Network, DQN)的有功安全校正策略。首先，建立系统有功安全校正模型。其次，采用卷积神经网络(Convolutional Neural Networks, CNN)挖掘电网运行状态深层特征。进一步利用DQN算法通过“状态-动作”机制，以“奖励”为媒介，构建电网运行状态与最优调整机组组合的映射模型，确定调整机组。最后，根据过载线路对调整机组的灵敏度，计算得到调整量。IEEE39节点系统的验证结果表明，所提出的有功安全校正策略在处理多线路过载时可综合考虑系统潮流分布的总体状况和机组调节性能，高效地消除线路过载。     

广东—香港—南方电网的频率特性

分析了南方互联电网在不同备用水平在高峰负荷和低谷负荷时段的频率响应特性,以及核电机组和蓄能机组跳闸对联网系统的影响,指出在系统备用不足的情况下,广东电网频率可能降低。     

基于深度学习网络的输电线路异物入侵监测和识别方法

为解决输电线路异物入侵在线监测图像样本量较小的问题,针对异物图像特点,提出了一种基于深度学习的输电线路异物入侵监测和识别方法.首先选取典型正常运行输电线路图像和目标异物图像,采用条件生成对抗网络算法对有异物入侵的输电线路图像进行样本扩充.然后将Dense-net网络替代YOLOv3网络中倒数第二层网络,建立Dense-...