我们提出了一种基于图形神经网络(GNN)的端到端框架,以平衡通用网格中的功率流。优化被帧为监督的顶点回归任务,其中GNN培训以预测每个网格分支的电流和功率注入,从而产生功率流量平衡。通过将电网表示为与顶点的分支的线图,我们可以培训一个更准确和强大的GNN来改变底层拓扑。此外,通过使用专门的GNN层,我们能够构建一个非常深的架构,该架构占图表上的大街区,同时仅实现本地化操作。我们执行三个不同的实验来评估:i)使用深入GNN模型时使用本地化而不是全球运营的好处和趋势; ii)图形拓扑中对扰动的弹性;和iii)能力同时在多个网格拓扑上同时培训模型以及新的看不见网格的概括性的改进。拟议的框架是有效的,而且与基于深度学习的其他求解器相比,不仅对网格组件上的物理量而且对拓扑的物理量具有鲁棒性。
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状态估计(SE)算法的目标是基于电力系统中的可用测量集来估计复杂的总线电压作为状态变量。因为相量测量单元(PMU)越来越多地用于传输电力系统,所以需要一种快速SE求解器,可以利用PMU高采样率。本文提出培训图形神经网络(GNN),以了解给PMU电压和电流测量作为输入的估计,目的是在评估阶段期间获得快速和准确的预测。使用合成数据集接受GNN,由电力系统中的随机采样的测量集创建并用使用带有PMU求解器的线性SE获得的解决方案来标记它们。所呈现的结果显示了各种测试场景中GNN预测的准确性,并将预测的灵敏度解决对缺失的输入数据。
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非线性状态估计(SE)的目的是根据电力系统中所有可用的测量值估算复杂的总线电压,通常使用迭代的高斯 - 纽顿方法来解决。在考虑来自相组量测量单元以及监督控制和数据采集系统的输入时,非线性SE会带来一些困难。这些包括数值不稳定性,收敛时间取决于迭代方法的起点以及单个迭代在状态变量数量方面的二次计算复杂性。本文在非线性功率系统SE的增强因子图上介绍了基于图形神经网络的原始SE实现,能够在分支机构和总线上进行测量,以及相法和遗留测量。提出的回归模型在一旦训练的推理时间内具有线性计算复杂性,并且有可能实现分布式。由于该方法是非词语且基于非矩阵的,因此它对高斯求解器容易出现的问题具有弹性。除了测试集的预测准确性外,提出的模型在模拟网络攻击和由于沟通不规则引起的不可观察的情况时表现出了鲁棒性。在这种情况下,预测错误在本地持续存在,对电力系统的其余结果没有影响。
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能源过渡向可再生能源的成功的主要挑战之一是分析电网的动态稳定性。但是,动态解决方案非常棘手,对于大网格而言非常昂贵。图形神经网络(GNN)是一种有前途的方法,可以减少预测功率网格动态稳定性的计算工作,但是尚不存在适当的复杂性和大小的数据集。我们介绍了两个合成生成电网的新数据集。对于每个网格,使用蒙特卡洛模拟估算了动态稳定性。数据集的网格比以前发布的网格高10倍。为了评估现实世界应用的潜力,我们证明了在德克萨斯电力电网模型上的成功预测。通过在更多数据上训练更复杂的模型,可以将性能提高到令人惊讶的高水平。此外,调查的网格具有不同的尺寸,从而使分发评估的应用并从小域中转移到大型域。我们邀请社区改善我们的基准模型,从而通过更好的工具来帮助能源过渡。
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深度学习技术的普及更新了能够处理可以使用图形的复杂结构的神经结构的兴趣,由图形神经网络(GNN)的启发。我们将注意力集中在最初提出的Scarselli等人的GNN模型上。 2009,通过迭代扩散过程编码图表的节点的状态,即在学习阶段,必须在每个时期计算,直到达到学习状态转换功能的固定点,传播信息邻近节点。基于拉格朗日框架的约束优化,我们提出了一种在GNNS中学习的新方法。学习转换功能和节点状态是联合过程的结果,其中通过约束满足机制隐含地表达了状态会聚过程,避免了迭代巨头程序和网络展开。我们的计算结构在由权重组成的伴随空间中搜索拉格朗日的马鞍点,节点状态变量和拉格朗日乘法器。通过加速扩散过程的多个约束层进一步增强了该过程。实验分析表明,该方法在几个基准上的流行模型有利地比较。
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作为一种高度复杂和集成的网络物理系统,现代电网暴露于网络攻击。假数据注入攻击(FDIAS),具体地,通过针对测量数据的完整性来表示对智能电网的主要类别威胁。虽然已经提出了各种解决方案来检测那些网络攻击,但绝大多数作品忽略了电网测量的固有图结构,并仅验证了其检测器,仅针对小于几百辆公共汽车的小型测试系统。为了更好地利用智能电网测量的空间相关性,本文提出了使用Chebyshev Graph卷积网络(CGCN)的大规模交流电网中的网络内人检测深度学习模型。通过降低光谱滤波器的复杂性并使它们本地化,CGCN提供了一种快速高效的卷积操作,以模拟图形结构智能电网数据。我们在数值上验证所提出的CGCN的探测器在7.86以7.86以7.67以带有2848辆总线的大型电网的误报率的7.86以7.86的误报。所值得注意的是,所提出的方法检测为2848辆总线系统的4毫秒下的网络攻击,这使其成为大型系统中的网络内攻击的良好候选者。
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这项研究采用无限脉冲响应(IIR)图神经网络(GNN),有效地对智能网格数据的固有图形网络结构进行建模,以解决网络攻击本地化问题。首先,我们通过数值分析有限脉冲响应(FIR)和IIR图过滤器(GFS)的经验频率响应,以近似理想的光谱响应。我们表明,对于相同的滤波器顺序,IIR GF可以更好地近似所需的光谱响应,并且由于其合理类型的滤镜响应,它们也与较低阶GF的近似值相同。其次,我们提出了一个IIR GNN模型,以有效预测总线上的网络攻击的存在。最后,我们在样本(SW)和BUS(BW)水平的各种网络攻击下评估了模型,并将结果与​​现有架构进行比较。经过实验验证的是,所提出的模型的表现分别优于最先进的FIR GNN模型,分别在SW和BW定位方面分别优于9.2%和14%。
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Graph classification is an important area in both modern research and industry. Multiple applications, especially in chemistry and novel drug discovery, encourage rapid development of machine learning models in this area. To keep up with the pace of new research, proper experimental design, fair evaluation, and independent benchmarks are essential. Design of strong baselines is an indispensable element of such works. In this thesis, we explore multiple approaches to graph classification. We focus on Graph Neural Networks (GNNs), which emerged as a de facto standard deep learning technique for graph representation learning. Classical approaches, such as graph descriptors and molecular fingerprints, are also addressed. We design fair evaluation experimental protocol and choose proper datasets collection. This allows us to perform numerous experiments and rigorously analyze modern approaches. We arrive to many conclusions, which shed new light on performance and quality of novel algorithms. We investigate application of Jumping Knowledge GNN architecture to graph classification, which proves to be an efficient tool for improving base graph neural network architectures. Multiple improvements to baseline models are also proposed and experimentally verified, which constitutes an important contribution to the field of fair model comparison.
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Reliability Assessment Commitment (RAC) Optimization is increasingly important in grid operations due to larger shares of renewable generations in the generation mix and increased prediction errors. Independent System Operators (ISOs) also aim at using finer time granularities, longer time horizons, and possibly stochastic formulations for additional economic and reliability benefits. The goal of this paper is to address the computational challenges arising in extending the scope of RAC formulations. It presents RACLEARN that (1) uses Graph Neural Networks (GNN) to predict generator commitments and active line constraints, (2) associates a confidence value to each commitment prediction, (3) selects a subset of the high-confidence predictions, which are (4) repaired for feasibility, and (5) seeds a state-of-the-art optimization algorithm with the feasible predictions and the active constraints. Experimental results on exact RAC formulations used by the Midcontinent Independent System Operator (MISO) and an actual transmission network (8965 transmission lines, 6708 buses, 1890 generators, and 6262 load units) show that the RACLEARN framework can speed up RAC optimization by factors ranging from 2 to 4 with negligible loss in solution quality.
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要将计算负担从实时到延迟关键电源系统应用程序的脱机,最近的作品招待使用深神经网络(DNN)的想法来预测一次呈现的AC最佳功率流(AC-OPF)的解决方案负载需求。随着网络拓扑可能改变的,以样本有效的方式训练该DNN成为必需品。为提高数据效率,这项工作利用了OPF数据不是简单的训练标签,而是构成参数优化问题的解决方案。因此,我们倡导培训一个灵敏度通知的DNN(SI-DNN),不仅可以匹配OPF优化器,而且还匹配它们的部分导数相对于OPF参数(负载)。结果表明,所需的雅可比矩阵确实存在于温和条件下,并且可以从相关的原始/双解决方案中容易地计算。所提出的Si-DNN与广泛的OPF溶剂兼容,包括非凸出的二次约束的二次程序(QCQP),其半纤维程序(SDP)放松和MatPower;虽然Si-DNN可以在其他学习到OPF方案中无缝集成。三个基准电源系统的数值测试证实了SI-DNN在传统训练的DNN上预测的OPF解决方案的高级泛化和约束满意度,尤其是在低数据设置中。
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在过去的几年中,已经开发了图形绘图技术,目的是生成美学上令人愉悦的节点链接布局。最近,利用可区分损失功能的使用已为大量使用梯度下降和相关优化算法铺平了道路。在本文中,我们提出了一个用于开发图神经抽屉(GND)的新框架,即依靠神经计算来构建有效且复杂的图的机器。 GND是图形神经网络(GNN),其学习过程可以由任何提供的损失函数(例如图形图中通常使用的损失函数)驱动。此外,我们证明,该机制可以由通过前馈神经网络计算的损失函数来指导,并根据表达美容特性的监督提示,例如交叉边缘的最小化。在这种情况下,我们表明GNN可以通过位置功能很好地丰富与未标记的顶点处理。我们通过为边缘交叉构建损失函数来提供概念验证,并在提议的框架下工作的不同GNN模型之间提供定量和定性的比较。
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Optimal Power Flow (OPF) is a very traditional research area within the power systems field that seeks for the optimal operation point of electric power plants, and which needs to be solved every few minutes in real-world scenarios. However, due to the nonconvexities that arise in power generation systems, there is not yet a fast, robust solution technique for the full Alternating Current Optimal Power Flow (ACOPF). In the last decades, power grids have evolved into a typical dynamic, non-linear and large-scale control system, known as the power system, so searching for better and faster ACOPF solutions is becoming crucial. Appearance of Graph Neural Networks (GNN) has allowed the natural use of Machine Learning (ML) algorithms on graph data, such as power networks. On the other hand, Deep Reinforcement Learning (DRL) is known for its powerful capability to solve complex decision-making problems. Although solutions that use these two methods separately are beginning to appear in the literature, none has yet combined the advantages of both. We propose a novel architecture based on the Proximal Policy Optimization algorithm with Graph Neural Networks to solve the Optimal Power Flow. The objective is to design an architecture that learns how to solve the optimization problem and that is at the same time able to generalize to unseen scenarios. We compare our solution with the DCOPF in terms of cost after having trained our DRL agent on IEEE 30 bus system and then computing the OPF on that base network with topology changes
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Pre-publication draft of a book to be published byMorgan & Claypool publishers. Unedited version released with permission. All relevant copyrights held by the author and publisher extend to this pre-publication draft.
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Graph Neural Networks (GNNs) are deep learning models designed to process attributed graphs. GNNs can compute cluster assignments accounting both for the vertex features and for the graph topology. Existing GNNs for clustering are trained by optimizing an unsupervised minimum cut objective, which is approximated by a Spectral Clustering (SC) relaxation. SC offers a closed-form solution that, however, is not particularly useful for a GNN trained with gradient descent. Additionally, the SC relaxation is loose and yields overly smooth cluster assignments, which do not separate well the samples. We propose a GNN model that optimizes a tighter relaxation of the minimum cut based on graph total variation (GTV). Our model has two core components: i) a message-passing layer that minimizes the $\ell_1$ distance in the features of adjacent vertices, which is key to achieving sharp cluster transitions; ii) a loss function that minimizes the GTV in the cluster assignments while ensuring balanced partitions. By optimizing the proposed loss, our model can be self-trained to perform clustering. In addition, our clustering procedure can be used to implement graph pooling in deep GNN architectures for graph classification. Experiments show that our model outperforms other GNN-based approaches for clustering and graph pooling.
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组合优化是运营研究和计算机科学领域的一个公认领域。直到最近,它的方法一直集中在孤立地解决问题实例,而忽略了它们通常源于实践中的相关数据分布。但是,近年来,人们对使用机器学习,尤其是图形神经网络(GNN)的兴趣激增,作为组合任务的关键构件,直接作为求解器或通过增强确切的求解器。GNN的电感偏差有效地编码了组合和关系输入,因为它们对排列和对输入稀疏性的意识的不变性。本文介绍了对这个新兴领域的最新主要进步的概念回顾,旨在优化和机器学习研究人员。
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由于负载和可再生能源的不确定性日益增长,对现代电网的安全和最佳运行产生了突出的挑战。随机最佳功率流(SOPF)制剂提供了一种通过计算在不确定性下保持可行性的派遣决策和控制政策来处理这些不确定性的机制。大多数SOPF配方考虑了简单的控制策略,例如数学上简单的仿射策略,类似于当前实践中使用的许多策略。通过机器学习(ML)算法的功效和一般控制政策的潜在好处的效果,我们提出了一个深度神经网络(DNN)基础的政策,该政策是实时预测发电机调度决策的不确定。使用解决SOPF的随机原始双重更新来学习DNN的权重,而无需先前一代训练标签,并且可以明确地解释SOPF中的可行性约束。 DNN政策对更简单的政策和它们在执行安全限制和产生附近的近最佳解决方案中的功效的优点在于机会在许多测试用例上受到限制的制定的情况下。
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图表神经网络(GNNS)最近在人工智能(AI)领域的普及,这是由于它们作为输入数据相对非结构化数据类型的独特能力。尽管GNN架构的一些元素在概念上类似于传统神经网络(以及神经网络变体)的操作中,但是其他元件代表了传统深度学习技术的偏离。本教程通过整理和呈现有关GNN最常见和性能变种的动机,概念,数学和应用的细节,将GNN的权力和新颖性暴露给AI从业者。重要的是,我们简明扼要地向实际示例提出了本教程,从而为GNN的主题提供了实用和可访问的教程。
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Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art graph neural networks into four categories, namely recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and spatial-temporal graph neural networks. We further discuss the applications of graph neural networks across various domains and summarize the open source codes, benchmark data sets, and model evaluation of graph neural networks. Finally, we propose potential research directions in this rapidly growing field.
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这本数字本书包含在物理模拟的背景下与深度学习相关的一切实际和全面的一切。尽可能多,所有主题都带有Jupyter笔记本的形式的动手代码示例,以便快速入门。除了标准的受监督学习的数据中,我们将看看物理丢失约束,更紧密耦合的学习算法,具有可微分的模拟,以及加强学习和不确定性建模。我们生活在令人兴奋的时期:这些方法具有从根本上改变计算机模拟可以实现的巨大潜力。
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功率流分析用于评估电力系统网络中的电流。功率流量计算用于确定系统的稳态变量,例如每个总线的电压幅度/相位角以及每个分支上的主动/无功流量。直流电流模型是一种流行的线性电流模型,广泛应用于电力行业。虽然它是快速且稳健的,但它可能导致一些关键传输线的线流量产生不准确的线流。可以通过利用历史网格配置文件的数据驱动方法部分地解决该缺陷。在本文中,训练了神经网络(NN)模型以预测使用历史电力系统数据来预测电力流量结果。虽然培训过程可能需要时间,但一旦训练,估计线流是非常快的。采用了所提出的基于NN的功率流模型和传统的直流电流模型之间的综合性能分析。可以得出结论,所提出的基于NN的电力流模型可以比直流电流模型快速更准确地找到解决方案。
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