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Data Information integrated Neural Network (DINN) algorithm for modelling and interpretation performance analysis for energy systems 用于能源系统建模和性能分析解释的数据信息集成神经网络 (DINN) 算法
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-03-25 DOI: 10.1016/j.egyai.2024.100363
Waqar Muhammad Ashraf, Vivek Dua

Developing a well-predictive machine learning model that also offers improved interpretability is a key challenge to widen the application of artificial intelligence in various application domains. In this work, we present a Data Information integrated Neural Network (DINN) algorithm that incorporates the correlation information present in the dataset for the model development. The predictive performance of DINN is also compared with a standard artificial neural network (ANN) model. The DINN algorithm is applied on two case studies of energy systems namely energy efficiency cooling (ENC) & energy efficiency heating (ENH) of the buildings, and power generation from a 365 MW capacity industrial gas turbine. For ENC, DINN presents lower mean RMSE for testing datasets (RMSE_test = 1.23 %) in comparison with the ANN model (RMSE_test = 1.41 %). Similarly, DINN models have presented better predictive performance to model the output variables of the two case studies. The input perturbation analysis following the Gaussian distribution for noise generation reveals the order of significance of the variables, as made by DINN, can be better explained by the domain knowledge of the power generation operation of the gas turbine. This research work demonstrates the potential advantage to integrate the information present in the data for the well-predictive model development complemented with improved interpretation performance thereby opening avenues for industry-wide inclusion and other potential applications of machine learning.

要在各个应用领域拓宽人工智能的应用范围,开发一个具有良好预测能力的机器学习模型并提高其可解释性是一项关键挑战。在这项工作中,我们提出了一种数据信息集成神经网络(DINN)算法,该算法结合了数据集中的相关信息来开发模型。我们还将 DINN 的预测性能与标准人工神经网络(ANN)模型进行了比较。DINN 算法应用于两个能源系统案例研究,即建筑物的节能制冷(ENC)和节能供热(ENH),以及 365 兆瓦容量的工业燃气轮机发电。就 ENC 而言,与 ANN 模型(RMSE_test = 1.41 %)相比,DINN 模型的测试数据集平均 RMSE 更低(RMSE_test = 1.23 %)。同样,DINN 模型对两个案例研究的输出变量建模的预测性能更好。根据高斯分布进行的输入扰动噪声分析表明,DINN 所做的变量重要性排序可以更好地用燃气轮机发电运行的领域知识来解释。这项研究工作展示了整合数据信息的潜在优势,以开发预测性更强的模型,并提高解释性能,从而为整个行业和机器学习的其他潜在应用开辟道路。
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引用次数: 0
Utilizing Artificial intelligence to identify an Optimal Machine learning model for predicting fuel consumption in Diesel engines 利用人工智能确定预测柴油发动机油耗的最佳机器学习模型
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-03-18 DOI: 10.1016/j.egyai.2024.100360
Amirali Shateri, Zhiyin Yang, Jianfei Xie

This paper describes the utilization of artificial intelligence (AI) techniques to identify an optimal machine learning (ML) model for predicting dodecane fuel consumption in diesel combustion. The study incorporates sensitivity analysis to assess the impact levels of various parameters on fuel consumption, thereby highlighting the most influential factors. In addition, this study addresses the impact of noise and implements data cleaning techniques to ensure the reliability of the obtained results. To validate the accuracy of the predictions, the study performs several metrics and validation process, including comparisons with computational fluid dynamics (CFD) results and experimental data. Comprehensive comparisons are made among neural networks (NN), random forest regression (RFR), and Gaussian process regression (GPR) models, taking into account the complexity associated with fuel consumption predictions. The findings demonstrate that the GPR model outperforms the others in terms of accuracy, as evidenced by metrics such as mean absolute error (MAE), mean squared error (MSE), Pearson coefficient (PC), and R-squared (R2). The GPR model exhibits superior predictive ability, accurately detecting and predicting even individual data points that deviate from the overall trend. The significantly lower absolute error values also consistently indicate its higher accuracy compared with the NN and RFR models. Furthermore, the GPR model shows a remarkable speedup, approximately 1.7 times faster than traditional CFD solvers, and physically captures the momentum and thermal characteristics in a surface field prediction. Finally, the target optimization is assessed using the Euclidean distance as a fitness function, ensuring the reliability of predicted data.

本文介绍了如何利用人工智能(AI)技术确定最佳机器学习(ML)模型,以预测柴油燃烧中的十二烷燃料消耗量。研究结合了敏感性分析,以评估各种参数对燃料消耗的影响程度,从而突出最有影响力的因素。此外,本研究还考虑了噪声的影响,并采用了数据清理技术,以确保所获结果的可靠性。为了验证预测的准确性,本研究执行了多个指标和验证过程,包括与计算流体动力学(CFD)结果和实验数据进行比较。考虑到与油耗预测相关的复杂性,对神经网络 (NN)、随机森林回归 (RFR) 和高斯过程回归 (GPR) 模型进行了综合比较。研究结果表明,从平均绝对误差 (MAE)、平均平方误差 (MSE)、皮尔逊系数 (PC) 和 R 平方 (R2) 等指标来看,GPR 模型的准确性优于其他模型。GPR 模型显示出卓越的预测能力,即使是偏离整体趋势的单个数据点也能准确检测和预测。与 NN 和 RFR 模型相比,绝对误差值明显降低,这也一致表明其准确性更高。此外,GPR 模型的速度显著加快,比传统的 CFD 求解器快约 1.7 倍,并在表面场预测中物理地捕捉了动量和热量特征。最后,使用欧氏距离作为拟合函数对目标优化进行了评估,确保了预测数据的可靠性。
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引用次数: 0
Review of the development of power system out-of-step splitting control and some thoughts on the impact of large-scale access of renewable energy 电力系统级外分流控制的发展回顾及对可再生能源大规模接入影响的几点思考
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-03-13 DOI: 10.1016/j.egyai.2024.100357
Shuai Zhang

Out-of-step oscillation is a very destructive physical phenomenon in power system, which could directly cause big blackout accompanied by serious sociology-economic impacts. Out-of-step splitting control is an indispensable means, which could protect the system from major shocks of out-of-step oscillation. After years of development, it has achieved certain amount of research results. Have the existing methods been able to meet the requirements of out-of-step splitting? What improvements are needed? Under this background, this review is written. It combs the development of out-of-step splitting control technologies and analyzes the technical routes and characteristics of different methods. It points out the contradiction between rapidity and optimality is the biggest technical problem, existing in both the traditional local measurement based out-of-step splitting protection and the wide-area information based out-of-step splitting protection. It further points out that the advantages of the two types of protections can be combined with the unique physical characteristics of the out-of-step center to form a more advantageous splitting strategy. Besides, facing the fact of large-scale renewable energy access to power grid in recent years, this review also analyzes the challenges brought by it and provides some corresponding suggestions. It is hoped to provide some guidance for the subsequent research work.

失步振荡是电力系统中一种破坏性很强的物理现象,可直接导致大停电,并伴随着严重的社会经济影响。失步分裂控制是保护系统免受失步振荡重大冲击的一种不可或缺的手段。经过多年的发展,它已经取得了一定的研究成果。现有的方法能否满足步外分裂的要求?还需要做哪些改进?在此背景下,我们撰写了这篇综述。它梳理了台阶外分裂控制技术的发展脉络,分析了不同方法的技术路线和特点。文章指出,快速与优化之间的矛盾是最大的技术问题,无论是基于局部测量的传统步外分裂保护,还是基于广域信息的步外分裂保护,都存在这一问题。报告进一步指出,可以将这两种保护方式的优势与台阶外中心独特的物理特性相结合,形成更具优势的分路策略。此外,面对近年来可再生能源大规模接入电网的事实,本综述还分析了其带来的挑战,并提出了一些相应的建议。希望能为后续的研究工作提供一些指导。
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引用次数: 0
Explainability and Interpretability in Electric Load Forecasting Using Machine Learning Techniques – A Review 利用机器学习技术进行电力负荷预测的可解释性和可解读性 - 综述
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-03-12 DOI: 10.1016/j.egyai.2024.100358
Lukas Baur , Konstantin Ditschuneit , Maximilian Schambach , Can Kaymakci , Thomas Wollmann , Alexander Sauer

Electric Load Forecasting (ELF) is the central instrument for planning and controlling demand response programs, electricity trading, and consumption optimization. Due to the increasing automation of these processes, meaningful and transparent forecasts become more and more important. Still, at the same time, the complexity of the used machine learning models and architectures increases.

Because there is an increasing interest in interpretable and explainable load forecasting methods, this work conducts a literature review to present already applied approaches regarding explainability and interpretability for load forecasts using Machine Learning. Based on extensive literature research covering eight publication portals, recurring modeling approaches, trends, and modeling techniques are identified and clustered by properties to achieve more interpretable and explainable load forecasts.

The results on interpretability show an increase in the use of probabilistic models, methods for time series decomposition and the use of fuzzy logic in addition to classically interpretable models. Dominant explainable approaches are Feature Importance and Attention mechanisms. The discussion shows that a lot of knowledge from the related field of time series forecasting still needs to be adapted to the problems in ELF. Compared to other applications of explainable and interpretable methods such as clustering, there are currently relatively few research results, but with an increasing trend.

电力负荷预测 (ELF) 是规划和控制需求响应计划、电力交易和消费优化的核心工具。由于这些流程的自动化程度不断提高,有意义且透明的预测变得越来越重要。然而,与此同时,所使用的机器学习模型和架构的复杂性也在增加。由于人们对可解释和可说明的负荷预测方法的兴趣与日俱增,本研究通过文献综述,介绍了已应用的有关使用机器学习进行负荷预测的可解释性和可说明性的方法。可解释性方面的研究结果表明,除了经典的可解释性模型外,概率模型、时间序列分解方法和模糊逻辑的使用也在增加。主要的可解释方法是特征重要性和注意力机制。讨论表明,时间序列预测相关领域的许多知识仍需加以调整,以适应 ELF 中的问题。与聚类等其他可解释和可解释方法的应用相比,目前的研究成果相对较少,但有增加的趋势。
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引用次数: 0
Improved deep residual shrinkage network for a multi-cylinder heavy-duty engine fault detection with single channel surface vibration 利用单通道表面振动检测多缸重型发动机故障的改进型深度残余收缩网络
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-03-04 DOI: 10.1016/j.egyai.2024.100356
Xiaolong Zhu , Junhong Zhang , Xinwei Wang , Hui Wang , Yedong Song , Guobin Pei , Xin Gou , Linlong Deng , Jiewei Lin

The health monitoring and fault diagnosis of heavy-duty engines are increasingly important for energy storage ecosystem. During operation, vibration characters corresponding to the specific fault need to be extracted from the overall system vibration. Faulty characteristics emanating from one single cylinder are also mixed with those from other cylinders. Besides, the change of working condition brings strong nonlinearities in surface vibration. To solve these problems, an improved deep residual shrinkage network (IDRSN) is developed for detecting diverse engine faults at various degrees using single channel surface vibration signal. Within IDRSN, a wide convolution kernel is utilized in first convolution layer to capture the long-term fault-related impacts and eliminate the short-time random impact. The residual network module is adopted to enhance the focus the relevant components of vibration signals. Mini-batch training strategy is used to improve the model stability. Meanwhile, Gradient-weighted class activation map is adopted to assess the consistency between the learned knowledge and the fault-related information. The IDRSN is implemented to diagnosing a diesel engine under various faults, faulty degrees and operating speeds. Comparisons with existing models are analyzed in terms of hyper-parameters, training samples, noise resistance, and visualization. Results demonstrate the proposed IDRSN's superior performance on fault diagnosis accuracy, stability, anti-noise performance, and anti-interference performance. An average accuracy rate of 98.38 % was achieved by the proposed IDRSN, in comparison to 96.64 % and 93.56 % achieved by the DRSN and the wide-kernel deep convolutional neural network respectively. These results highlight the proposed IDRSN's superiority in diagnosing multiple faults under various working conditions, offering a low-cost, highly effective, and applicable approach for complex fault diagnosis tasks.

对于储能生态系统而言,重型发动机的健康监测和故障诊断越来越重要。在运行过程中,需要从整个系统振动中提取与特定故障相对应的振动特征。来自单个气缸的故障特征也会与其他气缸的故障特征混合在一起。此外,工况的变化也会给表面振动带来强烈的非线性。为了解决这些问题,我们开发了一种改进的深度残余收缩网络(IDRSN),利用单通道表面振动信号检测不同程度的各种发动机故障。在 IDRSN 中,第一卷积层采用了宽卷积核,以捕捉与故障相关的长期影响并消除短时随机影响。残差网络模块用于加强对振动信号相关成分的关注。采用小批量训练策略来提高模型的稳定性。同时,采用梯度加权类激活图评估学习知识与故障相关信息的一致性。IDRSN 被用于诊断柴油发动机在各种故障、故障程度和运行速度下的情况。从超参数、训练样本、抗噪能力和可视化等方面分析了与现有模型的比较。结果表明,所提出的 IDRSN 在故障诊断准确性、稳定性、抗噪声性能和抗干扰性能等方面表现出色。与 DRSN 和宽核深度卷积神经网络分别达到的 96.64% 和 93.56% 的准确率相比,所提出的 IDRSN 达到了 98.38% 的平均准确率。这些结果凸显了所提出的 IDRSN 在各种工作条件下诊断多种故障的优越性,为复杂的故障诊断任务提供了一种低成本、高效和适用的方法。
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引用次数: 0
Research on the technical scheme of multi-stack common rail fuel cell engine based on the demand of commercial vehicle 基于商用车需求的多叠层共轨燃料电池发动机技术方案研究
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-02-28 DOI: 10.1016/j.egyai.2024.100353
Ji Pu , Qianya Xie , Jun Li , Ziliang Zhao , Junming Lai , Kang Li , Fojin Zhou

At present, most fuel cell engines are single-stack systems, and high-power single-stack systems have bottlenecks in meeting the power requirements of heavy-duty trucks, mainly because the increase in the single active area and the excessive number of cells will lead to poor distribution uniformity of water, gas and heat in the stack, which will cause local attenuation and reduce the performance of the stack. This paper introduces the design concept of internal combustion engine, takes three-stack fuel cell engine as an example, designs multi-stack fuel cell system scheme and serialized high-voltage scheme. Through Intelligent control technology of independent hydrogen injection based on multi-stack coupling, the hydrogen injection inflow of each stack is controlled online according to the real-time anode pressure to achieve accurate fuel injection of a single stack and ensure the consistency between multiple stacks. proves the performance advantage of multi-stack fuel cell engine through theoretical design, intelligent control and test verification, and focuses on analyzing the key technical problems that may exist in multi-stack consistency. The research results provide a reference for the design of multi-stack fuel cell engines, and have important reference value for the powertrain design of long-distance heavy-duty and high-power fuel cell trucks.

目前,大多数燃料电池发动机都是单叠片系统,大功率单叠片系统在满足重型卡车的动力需求方面存在瓶颈,主要原因是单个活性面积的增大和电池数量过多会导致叠片内水、气、热分布均匀性差,造成局部衰减,降低叠片性能。本文介绍了内燃机的设计理念,以三叠层燃料电池发动机为例,设计了多叠层燃料电池系统方案和系列化高压方案。通过基于多堆栈耦合的独立喷氢智能控制技术,根据实时阳极压力在线控制各堆栈的喷氢流入量,实现单堆栈的精确喷油,保证多堆栈之间的一致性。通过理论设计、智能控制和试验验证,证明了多堆栈燃料电池发动机的性能优势,并重点分析了多堆栈一致性可能存在的关键技术问题。研究成果为多堆栈燃料电池发动机的设计提供了参考,对长途重载大功率燃料电池卡车的动力总成设计具有重要的参考价值。
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引用次数: 0
Machine learning based state-of-charge prediction of electrochemical green hydrogen production: Zink-Zwischenschritt-Elektrolyseur (ZZE) 基于机器学习的电化学绿色制氢充电状态预测:Zink-Zwischenschritt-Elektrolyseur (ZZE)
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-02-28 DOI: 10.1016/j.egyai.2024.100355
Daniel Vila , Elisabeth Hornberger , Christina Toigo

The intermittency of renewable energy is a key limiting factor for the successful decarbonization of both energy producing and consuming sectors. Green hydrogen has the potential to act as the central energy vector connecting hard-to-abate sectors to renewable power. However, combining energy storage and conversion for a holistic electrolyzer system remains challenging. Here, we show the innovative Zink-Zwischenschritt Elektrolyseur (ZZE), or Zinc Intermediate step Electrolyzer in English, that temporarily decouples the water splitting reaction and uses zinc to store electrical energy in chemical form. To perform optimal operation of a ZZE system, machine learning models were applied to predict the state of charge of a lab scale ZZE system. Using various models, we were able to determine the effectiveness of the prediction and contrast it to state of charge predictions of other energy storage systems. We show that a bi-directional long short-term memory neural network approach has the lowest error within the testing environment. This work serves to perform further ZZE development as well as state of charge prediction for other novel energy storage technologies.

可再生能源的间歇性是限制能源生产和消费部门成功实现去碳化的关键因素。绿色氢气有可能成为连接难以消减的部门与可再生能源的核心能源载体。然而,将能量储存和转换结合起来以形成一个整体的电解槽系统仍然具有挑战性。在这里,我们展示了创新的 Zink-Zwischenschritt Elektrolyseur (ZZE),即英文中的 Zinc Intermediate step Electrolyzer(锌中间步骤电解槽),它可以暂时分离水分裂反应,并利用锌以化学形式储存电能。为了优化 ZZE 系统的运行,我们应用机器学习模型来预测实验室规模的 ZZE 系统的电荷状态。利用各种模型,我们能够确定预测的有效性,并将其与其他储能系统的电荷状态预测进行对比。我们发现,在测试环境中,双向长短期记忆神经网络方法的误差最小。这项工作有助于进一步开发 ZZE 以及预测其他新型储能技术的充电状态。
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引用次数: 0
A review of image processing and quantification analysis for solid oxide fuel cell 固体氧化物燃料电池图像处理和量化分析综述
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-02-28 DOI: 10.1016/j.egyai.2024.100354
Kar Shen Tan , Chee Kiang Lam , Wee Choon Tan , Heap Sheng Ooi , Zi Hao Lim

The purpose of this study is to investigate the approaches applied to analyze solid oxide fuel cell (SOFC) microstructural properties. Both manual and automated image processing approaches applied on SOFC microstructural images which are obtained from several types of tomography such as dual-beam focused ion beam with scanning electron microscopy (FIB-SEM), Electron Backscatter Diffraction (EBSD) and others are discussed. In fact, to achieve a realistic and accurate SOFC microstructural properties, such as average diameter, volume fraction, triple phase boundary (TPB), area interface density and tortuosity factor, the approaches of image processing and quantification are crucial for a reliable image generation for quantification purposes. The microstructural properties are optimized to improve SOFC electrode performance. Therefore, the image processing and quantification approaches are outlined and reviewed. Despite the automated image processing and quantification algorithms significantly outperform manual image processing and quantification approaches in terms of computing speed when evaluating and measuring microstructural properties, the efficiency and productivity are still extremely taken into concern. As a result, image processing and quantification approaches are concluded and presented respectively in this paper.

本研究的目的是调查用于分析固体氧化物燃料电池(SOFC)微观结构特性的方法。研究讨论了手动和自动图像处理方法,这些方法适用于从多种层析成像技术(如双光束聚焦离子束扫描电子显微镜(FIB-SEM)、电子背散射衍射(EBSD)等)获得的 SOFC 微观结构图像。事实上,要获得真实准确的 SOFC 微结构特性,如平均直径、体积分数、三相边界(TPB)、面积界面密度和迂回因子,图像处理和量化方法对于生成可靠的量化图像至关重要。微结构特性的优化可提高 SOFC 电极的性能。因此,本文对图像处理和量化方法进行了概述和评述。尽管在评估和测量微观结构特性时,自动图像处理和量化算法在计算速度方面明显优于手动图像处理和量化方法,但效率和生产率仍是极为重要的考虑因素。因此,本文对图像处理和量化方法分别进行了总结和介绍。
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引用次数: 0
Diagnosing health in composite battery electrodes with explainable deep learning and partial charging data 利用可解释深度学习和部分充电数据诊断复合电池电极的健康状况
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-02-27 DOI: 10.1016/j.egyai.2024.100352
Haijun Ruan , Niall Kirkaldy , Gregory J. Offer , Billy Wu

Lithium-ion batteries with composite anodes of graphite and silicon are increasingly being used. However, their degradation pathways are complicated due to the blended nature of the electrodes, with graphite and silicon degrading at different rates. Here, we develop a deep learning health diagnostic framework to rapidly quantify and separate the different degradation rates of graphite and silicon in composite anodes using partial charging data. The convolutional neural network (CNN), trained with synthetic data, uses experimental partial charging data to diagnose electrode-level health of tested batteries, with errors of less than 3.1% (corresponding to the loss of active material reaching ∼75%). Sensitivity analysis of the capacity-voltage curve under different degradation modes is performed to provide a physically informed voltage window for diagnostics with partial charging data. By using the gradient-weighted class activation mapping approach, we provide explainable insights into how these CNNs work; highlighting regions of the voltage-curve to which they are most sensitive. Robustness is validated by introducing noise to the data, with no significant negative impact on the diagnostic accuracy for noise levels below 10 mV, thus highlighting the potential for deep learning approaches in the diagnostics of lithium-ion battery performance under real-world conditions. The framework presented here can be generalised to other cell formats and chemistries, providing robust and explainable battery diagnostics for both conventional single material electrodes, but also the more challenging composite electrodes.

使用石墨和硅复合阳极的锂离子电池越来越多。然而,由于电极的混合性质,它们的降解途径非常复杂,石墨和硅的降解速度各不相同。在此,我们开发了一种深度学习健康诊断框架,利用部分充电数据快速量化和区分复合阳极中石墨和硅的不同降解率。利用合成数据训练的卷积神经网络(CNN)使用实验性部分充电数据来诊断测试电池的电极级健康状况,误差小于 3.1%(相当于活性材料损耗达到 75%)。对不同降解模式下的容量-电压曲线进行了灵敏度分析,从而为使用部分充电数据进行诊断提供了一个物理意义上的电压窗口。通过使用梯度加权类激活映射方法,我们对这些 CNN 的工作原理提供了可解释的见解;突出了它们最敏感的电压曲线区域。通过在数据中引入噪声验证了鲁棒性,噪声水平低于 10 mV 时对诊断准确性没有明显的负面影响,从而突出了深度学习方法在真实世界条件下诊断锂离子电池性能的潜力。本文介绍的框架可推广到其他电池形式和化学物质,为传统的单一材料电极以及更具挑战性的复合电极提供稳健且可解释的电池诊断。
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引用次数: 0
Unraveling fundamental properties of power system resilience curves using unsupervised machine learning 利用无监督机器学习揭示电力系统弹性曲线的基本特性
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-02-18 DOI: 10.1016/j.egyai.2024.100351
Bo Li, Ali Mostafavi

Power system is vital to modern societies, while it is susceptible to hazard events. Thus, analyzing resilience characteristics of power system is important. The standard model of infrastructure resilience, the resilience triangle, has been the primary way of characterizing and quantifying resilience in infrastructure systems for more than two decades. However, the theoretical model provides a one-size-fits-all framework for all infrastructure systems and specifies general characteristics of resilience curves (e.g., residual performance and duration of recovery). Little empirical work has been done to delineate infrastructure resilience curve archetypes and their fundamental properties based on observational data. Most of the existing studies examine the characteristics of infrastructure resilience curves based on analytical models constructed upon simulated system performance. There is a dire dearth of empirical studies in the field, which hindered our ability to fully understand and predict resilience characteristics in infrastructure systems. To address this gap, this study examined more than two hundred power-grid resilience curves related to power outages in three major extreme weather events in the United States. Through the use of unsupervised machine learning, we examined different curve archetypes, as well as the fundamental properties of each resilience curve archetype. The results show two primary archetypes for power grid resilience curves, triangular curves, and trapezoidal curves. Triangular curves characterize resilience behavior based on three fundamental properties: 1. critical functionality threshold, 2. critical functionality recovery rate, and 3. recovery pivot point. Trapezoidal archetypes explain resilience curves based on 1. duration of sustained function loss and 2. constant recovery rate. The longer the duration of sustained function loss, the slower the constant rate of recovery. The findings of this study provide novel perspectives enabling better understanding and prediction of resilience performance of power system infrastructure in extreme weather events.

电力系统对现代社会至关重要,但也容易受到灾害事件的影响。因此,分析电力系统的复原力特征非常重要。二十多年来,基础设施复原力的标准模型--复原力三角一直是描述和量化基础设施系统复原力的主要方法。然而,该理论模型为所有基础设施系统提供了一个放之四海而皆准的框架,并规定了复原力曲线的一般特征(如剩余性能和恢复持续时间)。基于观测数据来划分基础设施复原力曲线原型及其基本特性的实证工作还很少。大多数现有研究都是根据模拟系统性能建立的分析模型来研究基础设施复原力曲线的特性。该领域的实证研究极为匮乏,这阻碍了我们全面了解和预测基础设施系统复原力特征的能力。为了弥补这一不足,本研究考察了美国三次重大极端天气事件中与断电相关的两百多条电网复原力曲线。通过使用无监督机器学习,我们研究了不同的曲线原型,以及每种弹性曲线原型的基本属性。结果显示,电网复原力曲线有两种主要原型:三角形曲线和梯形曲线。三角形曲线基于三个基本特性来描述恢复能力行为:临界功能阈值、临界功能恢复率和恢复支点。梯形原型根据 1.持续功能丧失持续时间和 2.恒定恢复率来解释弹性曲线。持续功能丧失时间越长,恒定恢复速度越慢。这项研究的结果提供了新的视角,有助于更好地理解和预测电力系统基础设施在极端天气事件中的恢复能力。
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