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Distributionally robust optimization configuration method for island microgrid considering extreme scenarios 考虑极端情况的岛屿微电网分布式鲁棒优化配置方法
IF 9.6 Q1 Engineering Pub Date : 2024-06-13 DOI: 10.1016/j.egyai.2024.100389
Qingzhu Zhang, Yunfei Mu, Hongjie Jia, Xiaodan Yu, Kai Hou

The marine climate conditions are intricate and variable. In scenarios characterized by high proportions of wind and solar energy access, the uncertainty regarding the energy sources for island microgrid is significantly exacerbated, presenting challenges to both the economic viability and reliability of the capacity configuration for island microgrids. To address this issue, this paper proposes a distributionally robust optimization (DRO) method for island microgrids, considering extreme scenarios of wind and solar conditions. Firstly, to address the challenge of determining the probability distribution functions of wind and solar in complex island climates, a conditional generative adversarial network (CGAN) is employed to generate a scenario set for wind and solar conditions. Then, by combining k-means clustering with an extreme scenario selection method, typical scenarios and extreme scenarios are selected from the generated scenario set, forming the scenario set for the DRO model of island microgrids. On this basis, a DRO model based on multiple discrete scenarios is constructed with the objective of minimizing the sum of investment costs, operation and maintenance costs, fuel purchase costs, penalty costs of wind and solar curtailment, and penalty costs of load loss. The model is subjected to equipment operation and power balance constraints, and solved using the columns and constraints generation (CCG) algorithm. Finally, through typical examples, the effectiveness of this paper’s method in balancing the economic viability and robustness of the configuration scheme for the island microgrid, as well as reducing wind and solar curtailment and load loss, is verified.

海洋气候条件复杂多变。在风能和太阳能接入比例较高的情况下,岛屿微电网能源来源的不确定性大大增加,这对岛屿微电网容量配置的经济可行性和可靠性都提出了挑战。针对这一问题,本文提出了一种考虑风能和太阳能极端情况的岛屿微电网分布式鲁棒优化(DRO)方法。首先,为了解决在复杂的海岛气候条件下确定风能和太阳能概率分布函数的难题,本文采用了条件生成对抗网络(CGAN)来生成风能和太阳能条件的情景集。然后,通过将 k-means 聚类与极端情景选择方法相结合,从生成的情景集中选择典型情景和极端情景,形成海岛微电网 DRO 模型的情景集。在此基础上,以投资成本、运行和维护成本、燃料采购成本、风能和太阳能削减惩罚成本以及负荷损失惩罚成本之和最小化为目标,构建了基于多个离散情景的 DRO 模型。该模型受到设备运行和电力平衡约束,并使用列和约束生成(CCG)算法求解。最后,通过典型实例验证了本文方法在平衡岛屿微电网配置方案的经济可行性和鲁棒性,以及减少风能和太阳能削减和负荷损失方面的有效性。
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引用次数: 0
Machine learning and Bayesian optimization for performance prediction of proton-exchange membrane fuel cells 用于质子交换膜燃料电池性能预测的机器学习和贝叶斯优化技术
Q1 Engineering Pub Date : 2024-06-08 DOI: 10.1016/j.egyai.2024.100380
Soufian Echabarri , Phuc Do , Hai-Canh Vu , Bastien Bornand

Proton-exchange membrane fuel cells (PEMFCs) are critical components of zero-emission electro-hydrogen generators. Accurate performance prediction is vital to the optimal operation management and preventive maintenance of these generators. Polarization curve remains one of the most important features representing the performance of PEMFCs in terms of efficiency and durability. However, predicting the polarization curve is not trivial as PEMFCs involve complex electrochemical reactions that feature multiple nonlinear relationships between the operating variables as inputs and the voltage as outputs. Herein, we present an artificial-intelligence-based approach for predicting the PEMFCs’ performance. In that way, we propose first an explainable solution for selecting the relevant features based on kernel principal component analysis and mutual information. Then, we develop a machine learning approach based on XGBRegressor and Bayesian optimization to explore the complex features and predict the PEMFCs’ performance. The performance and the robustness of the proposed machine learning based prediction approach is tested and validated through a real industrial dataset including 10 PEMFCs. Furthermore, several comparison studies with XGBRegressor and the two popular machine learning-based methods in predicting PEMFC performance, such as artificial neural network (ANN) and support vector machine regressor (SVR) are also conducted. The obtained results show that the proposed approach is more robust and outperforms the two conventional methods and the XGBRegressor for all the considered PEMFCs. Indeed, according to the coefficient of determination criterion, the proposed model gains an improvement of 6.35%, 6.8%, and 4.8% compared with ANN, SVR, and XGBRegressor respectively.

质子交换膜燃料电池(PEMFC)是零排放电氢发电机的关键部件。准确的性能预测对于这些发电机的优化运行管理和预防性维护至关重要。极化曲线仍然是代表 PEMFC 在效率和耐用性方面性能的最重要特征之一。然而,预测极化曲线并非易事,因为 PEMFCs 涉及复杂的电化学反应,在作为输入的操作变量和作为输出的电压之间存在多种非线性关系。在此,我们提出了一种基于人工智能的 PEMFC 性能预测方法。为此,我们首先提出了一种基于内核主成分分析和互信息选择相关特征的可解释解决方案。然后,我们开发了一种基于 XGBRegressor 和贝叶斯优化的机器学习方法,以探索复杂特征并预测 PEMFC 的性能。我们通过一个包括 10 个 PEMFC 的真实工业数据集测试和验证了所提出的基于机器学习的预测方法的性能和稳健性。此外,还与 XGBRegressor 以及人工神经网络(ANN)和支持向量机回归器(SVR)等两种常用的基于机器学习的 PEMFC 性能预测方法进行了比较研究。结果表明,就所有考虑的 PEMFC 而言,所提出的方法更加稳健,性能优于两种传统方法和 XGBRegressor。事实上,根据判定系数标准,与 ANN、SVR 和 XGBRegressor 相比,所提出的模型分别提高了 6.35%、6.8% 和 4.8%。
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引用次数: 0
Transfer learning from synthetic data for open-circuit voltage curve reconstruction and state of health estimation of lithium-ion batteries from partial charging segments 利用合成数据进行转移学习,从部分充电片段重建开路电压曲线并评估锂离子电池的健康状况
Q1 Engineering Pub Date : 2024-06-06 DOI: 10.1016/j.egyai.2024.100382
Tobias Hofmann , Jacob Hamar , Bastian Mager , Simon Erhard , Jan Philipp Schmidt

Data-driven models for battery state estimation require extensive experimental training data, which may not be available or suitable for specific tasks like open-circuit voltage (OCV) reconstruction and subsequent state of health (SOH) estimation. This study addresses this issue by developing a transfer-learning-based OCV reconstruction model using a temporal convolutional long short-term memory (TCN-LSTM) network trained on synthetic data from an automotive nickel cobalt aluminium oxide (NCA) cell generated through a mechanistic model approach. The data consists of voltage curves at constant temperature, C-rates between C/30 to 1C, and a SOH-range from 70 % to 100 %. The model is refined via Bayesian optimization and then applied to four use cases with reduced experimental nickel manganese cobalt oxide (NMC) cell training data for higher use cases. The TL models’ performances are compared with models trained solely on experimental data, focusing on different C-rates and voltage windows. The results demonstrate that the OCV reconstruction mean absolute error (MAE) within the average battery electric vehicle (BEV) home charging window (30 % to 85 % state of charge (SOC)) is less than 22 mV for the first three use cases across all C-rates. The SOH estimated from the reconstructed OCV exhibits an mean absolute percentage error (MAPE) below 2.2 % for these cases. The study further investigates the impact of the source domain on TL by incorporating two additional synthetic datasets, a lithium iron phosphate (LFP) cell and an entirely artificial, non-existing, cell, showing that solely the shifting and scaling of gradient changes in the charging curve suffice to transfer knowledge, even between different cell chemistries. A key limitation with respect to extrapolation capability is identified and evidenced in our fourth use case, where the absence of such comprehensive data hindered the TL process.

用于电池状态估计的数据驱动模型需要大量实验训练数据,而这些数据可能无法获得或不适合开路电压(OCV)重建和后续健康状态(SOH)估计等特定任务。为解决这一问题,本研究开发了基于迁移学习的开路电压重构模型,该模型采用时序卷积长短期记忆(TCN-LSTM)网络,通过机理模型方法生成的汽车镍钴铝氧化物(NCA)电池合成数据对其进行训练。数据包括恒温下的电压曲线、C/30 到 1C 之间的 C 率以及 70% 到 100% 的 SOH 范围。通过贝叶斯优化法对该模型进行了改进,然后将其应用于四种使用情况,并在较高使用情况下减少了镍锰钴氧化物(NMC)电池的实验训练数据。将 TL 模型的性能与仅根据实验数据训练的模型进行了比较,重点关注不同的 C 速率和电压窗口。结果表明,在所有 C 速率的前三种使用情况下,平均电池电动汽车 (BEV) 家庭充电窗口(30 % 至 85 % 充电状态 (SOC))内的 OCV 重建平均绝对误差 (MAE) 小于 22 mV。在这些情况下,根据重建的 OCV 估算的 SOH 平均绝对百分比误差 (MAPE) 低于 2.2%。该研究通过纳入两个额外的合成数据集(磷酸铁锂(LFP)电池和完全人造的不存在的电池),进一步研究了源域对 TL 的影响,结果表明,即使在不同的电池化学成分之间,仅靠充电曲线梯度变化的移动和缩放就足以实现知识转移。在我们的第四个使用案例中,我们发现并证明了外推能力方面的一个关键限制,即缺乏此类全面的数据阻碍了 TL 过程。
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引用次数: 0
Opportunities for large language models and discourse in engineering design 大型语言模型和话语在工程设计中的机遇
Q1 Engineering Pub Date : 2024-06-04 DOI: 10.1016/j.egyai.2024.100383
Jan Göpfert , Jann M. Weinand , Patrick Kuckertz , Detlef Stolten

In recent years, large language models have achieved breakthroughs on a wide range of benchmarks in natural language processing and continue to increase in performance. Recently, the advances of large language models have raised interest outside the natural language processing community and could have a large impact on daily life. In this paper, we pose the question: How will large language models and other foundation models shape the future product development process? We provide the reader with an overview of the subject by summarizing both recent advances in natural language processing and the use of information technology in the engineering design process. We argue that discourse should be regarded as the core of engineering design processes, and therefore should be represented in a digital artifact. On this basis, we describe how foundation models such as large language models could contribute to the design discourse by automating parts thereof that involve creativity and reasoning, and were previously reserved for humans. We describe how simulations, experiments, topology optimizations, and other process steps can be integrated into a machine-actionable, discourse-centric design process. As an example, we present a design discourse on the optimization of wind turbine blades. Finally, we outline the future research that will be necessary for the implementation of the conceptualized framework.

近年来,大型语言模型在自然语言处理的各种基准测试中取得了突破性进展,其性能还在不断提高。最近,大型语言模型的进步引起了自然语言处理界以外的关注,并可能对日常生活产生巨大影响。在本文中,我们提出了一个问题:大型语言模型和其他基础模型将如何塑造未来的产品开发流程?我们通过总结自然语言处理的最新进展和工程设计过程中信息技术的应用,为读者提供了这一主题的概览。我们认为,话语应被视为工程设计流程的核心,因此应该用数字工具来表示。在此基础上,我们描述了大型语言模型等基础模型如何通过将其中涉及创造力和推理的部分自动化来促进设计话语,而这些部分以前都是由人类来完成的。我们介绍了如何将模拟、实验、拓扑优化和其他流程步骤整合到机器可执行的、以话语为中心的设计流程中。作为一个例子,我们介绍了风力涡轮机叶片优化的设计论述。最后,我们概述了实施概念化框架所需的未来研究。
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引用次数: 0
Evasive attacks against autoencoder-based cyberattack detection systems in power systems 针对电力系统中基于自动编码器的网络攻击检测系统的规避性攻击
Q1 Engineering Pub Date : 2024-06-04 DOI: 10.1016/j.egyai.2024.100381
Yew Meng Khaw , Amir Abiri Jahromi , Mohammadreza F.M. Arani , Deepa Kundur

The digital transformation process of power systems towards smart grids is resulting in improved reliability, efficiency and situational awareness at the expense of increased cybersecurity vulnerabilities. Given the availability of large volumes of smart grid data, machine learning-based methods are considered an effective way to improve cybersecurity posture. Despite the unquestionable merits of machine learning approaches for cybersecurity enhancement, they represent a component of the cyberattack surface that is vulnerable, in particular, to adversarial attacks. In this paper, we examine the robustness of autoencoder-based cyberattack detection systems in smart grids to adversarial attacks. A novel iterative-based method is first proposed to craft adversarial attack samples. Then, it is demonstrated that an attacker with white-box access to the autoencoder-based cyberattack detection systems can successfully craft evasive samples using the proposed method. The results indicate that naive initial adversarial seeds cannot be employed to craft successful adversarial attacks shedding insight on the complexity of designing adversarial attacks against autoencoder-based cyberattack detection systems in smart grids.

电力系统向智能电网的数字化转型过程提高了可靠性、效率和态势感知能力,但同时也增加了网络安全漏洞。鉴于大量智能电网数据的可用性,基于机器学习的方法被认为是改善网络安全态势的有效途径。尽管机器学习方法在增强网络安全方面的优点毋庸置疑,但它们代表了网络攻击面的一个组成部分,特别容易受到敌对攻击。在本文中,我们研究了智能电网中基于自动编码器的网络攻击检测系统对恶意攻击的鲁棒性。首先提出了一种基于迭代的新方法来制作对抗性攻击样本。然后,研究证明,攻击者只要有白盒访问基于自动编码器的网络攻击检测系统的权限,就能利用所提出的方法成功制作出逃避攻击的样本。结果表明,天真的初始对抗性种子无法成功制作对抗性攻击样本,从而揭示了针对智能电网中基于自动编码器的网络攻击检测系统设计对抗性攻击的复杂性。
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引用次数: 0
Deep Weighted Fusion Learning (DWFL)-based multi-sensor fusion model for accurate building occupancy detection 基于深度加权融合学习(DWFL)的多传感器融合模型,用于准确检测建筑物占用情况
Q1 Engineering Pub Date : 2024-05-24 DOI: 10.1016/j.egyai.2024.100379
Md. Rumman Rafi , Fei Hu , Shuhui Li , Aijun Song , Xingli Zhang , Zheng O’Neill

With the advancement of artificial intelligence, the dominance of deep learning (DL) models over ordinary machine learning (ML) algorithms has become a reality in recent years due to its capability of handling complex pattern recognition without manual feature pre-definition. With the growing demands for power savings, building energy loss reduction could benefit from DL techniques. For buildings/rooms with the varying number of occupants, heating, ventilation, and air conditioning (HVAC) systems are often found in operations without much necessity. To reduce the building’s energy loss, accurate occupancy detection/prediction (ODP) results could be used to control the proper operations of HVACs. However, ODP is a challenging issue due to multiple reasons, such as improper selection/deployment of sensors, inefficient learning algorithms for pattern recognition, varying room conditions, etc. To overcome the above challenges, we propose a DL-based framework, i.e., Deep Weighted Fusion Learning (DWFL), to detect and predict occupancy counts with optimal multi-sensor fusion structure. DWFL fuses the extracted features from multiple types of sensors with the priority/weight assignment to each sensor. Such weight assignment considers different room conditions and the pros/cons of each type of sensor. To evaluate DWFL model in terms of occupancy prediction accuracy, we have set up an experimental testbed with low-cost cameras, carbon dioxide (CO2), and passive infrared (PIR) sensors. Among the recently proposed occupancy detection models, DeepFusion utilized deep learning model on heterogeneous sensor data and achieved 88% accuracy in occupancy count estimation (Xue et al., 2019). Another deep learning-based model MI-PIR achieved 91% accuracy on raw analog data from PIR sensors (Andrews et al., 2020). Our research outcome is 94%. Therefore, the experiment results show that our DWFL scheme outperforms the state-of-the-art ODP methods by 3%.

近年来,随着人工智能的发展,深度学习(DL)模型因其无需人工预先定义特征即可处理复杂模式识别的能力,在普通机器学习(ML)算法中占据了主导地位。随着节电需求的不断增长,减少建筑能耗可从 DL 技术中获益。对于居住人数不等的楼宇/房间来说,供暖、通风和空调系统(HVAC)在运行时往往没有太多必要。为了减少建筑物的能源损耗,可以利用精确的占用检测/预测(ODP)结果来控制暖通空调系统的正常运行。然而,由于传感器选择/部署不当、模式识别学习算法效率低下、房间条件多变等多种原因,占用检测是一个具有挑战性的问题。为了克服上述挑战,我们提出了一种基于 DL 的框架,即深度加权融合学习(DWFL),以最优的多传感器融合结构来检测和预测占用率。DWFL 将从多种类型传感器中提取的特征与每个传感器的优先级/权重分配相融合。这种权重分配考虑了不同的房间条件和每种传感器的优缺点。为了评估 DWFL 模型的占用预测准确性,我们利用低成本摄像头、二氧化碳(CO2)传感器和被动红外(PIR)传感器建立了一个实验测试平台。在最近提出的占用检测模型中,DeepFusion 在异构传感器数据上使用了深度学习模型,在占用计数估计方面达到了 88% 的准确率(Xue 等人,2019 年)。另一个基于深度学习的模型 MI-PIR 在 PIR 传感器的原始模拟数据上取得了 91% 的准确率(Andrews 等人,2020 年)。我们的研究成果是 94%。因此,实验结果表明,我们的 DWFL 方案比最先进的 ODP 方法高出 3%。
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引用次数: 0
Smart optimization in battery energy storage systems: An overview 电池储能系统的智能优化:概述
Q1 Engineering Pub Date : 2024-05-22 DOI: 10.1016/j.egyai.2024.100378
Hui Song , Chen Liu , Ali Moradi Amani , Mingchen Gu , Mahdi Jalili , Lasantha Meegahapola , Xinghuo Yu , George Dickeson

The increasing drive towards eco-friendly environment motivates the generation of energy from renewable energy sources (RESs). The rising share of RESs in power generation poses potential challenges, including uncertainties in generation output, frequency fluctuations, and insufficient voltage regulation capabilities. As a solution to these challenges, energy storage systems (ESSs) play a crucial role in storing and releasing power as needed. Battery energy storage systems (BESSs) provide significant potential to maximize the energy efficiency of a distribution network and the benefits of different stakeholders. This can be achieved through optimizing placement, sizing, charge/discharge scheduling, and control, all of which contribute to enhancing the overall performance of the network. In this paper, we provide a comprehensive overview of BESS operation, optimization, and modeling in different applications, and how mathematical and artificial intelligence (AI)-based optimization techniques contribute to BESS charging and discharging scheduling. We also discuss some potential future opportunities and challenges of the BESS operation, AI in BESSs, and how emerging technologies, such as internet of things, AI, and big data impact the development of BESSs.

对生态友好型环境的日益推动促使人们利用可再生能源(RES)发电。可再生能源在发电中所占比例的不断提高带来了潜在的挑战,包括发电输出的不确定性、频率波动和电压调节能力不足。作为应对这些挑战的解决方案,储能系统(ESS)在根据需要储存和释放电能方面发挥着至关重要的作用。电池储能系统(BESS)在最大限度地提高配电网络能效和不同利益相关者的利益方面具有巨大潜力。这可以通过优化布局、大小、充放电调度和控制来实现,所有这些都有助于提高配电网的整体性能。在本文中,我们将全面概述不同应用中的 BESS 运行、优化和建模,以及基于数学和人工智能(AI)的优化技术如何促进 BESS 充放电调度。我们还讨论了 BESS 运行、BESS 中的人工智能以及新兴技术(如物联网、人工智能和大数据)如何影响 BESS 的发展的一些潜在的未来机遇和挑战。
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引用次数: 0
Dynamic knowledge graph approach for modelling the decarbonisation of power systems 电力系统去碳化建模的动态知识图谱方法
Q1 Engineering Pub Date : 2024-05-20 DOI: 10.1016/j.egyai.2024.100359
Wanni Xie , Feroz Farazi , John Atherton , Jiaru Bai , Sebastian Mosbach , Jethro Akroyd , Markus Kraft

This paper presents a dynamic knowledge graph approach that offers a reusable, interoperable, and extensible framework for modelling power systems. Domain ontologies have been developed to support a linked data representation of infrastructure data, socio-demographic data, areal attributes like demand, and models describing power systems. The knowledge graph links the data with a hierarchical representation of administrative regions, supporting geospatial queries to retrieve information about the population within the vicinity of a power plant, the number of power plants, total generation capacity, and demand within specific areas. Computational agents were developed to operate on the knowledge graph. The agents performed tasks including data uploading, updating, retrieval, processing, model construction and scenario analysis. A derived information framework was used to track the provenance of information calculated by agents involved in each scenario. The knowledge graph was populated with data describing the UK power system. Two alternative models of the transmission grid with different levels of structural resolution were instantiated, providing the foundation for the power system simulation and optimisation tasks performed by the agents. The application of the dynamic knowledge graph was demonstrated via a case study that investigates clean energy transition trajectories based on the deployment of Small Modular Reactors in the UK.

本文介绍了一种动态知识图谱方法,为电力系统建模提供了一个可重复使用、可互操作和可扩展的框架。已开发的领域本体支持基础设施数据、社会人口数据、需求等区域属性的链接数据表示,以及描述电力系统的模型。知识图谱将数据与行政区域的分级表示法联系起来,支持地理空间查询,以检索特定区域内发电厂附近的人口、发电厂数量、总发电量和需求等信息。开发的计算代理可在知识图谱上运行。代理执行的任务包括数据上传、更新、检索、处理、模型构建和情景分析。衍生信息框架用于跟踪每个方案中的代理计算信息的出处。知识图谱中填充了描述英国电力系统的数据。两个具有不同结构分辨率的输电网替代模型被实例化,为代理执行电力系统仿真和优化任务奠定了基础。动态知识图谱的应用通过一个案例研究得以展示,该案例研究以英国小型模块化反应堆的部署为基础,调查了清洁能源的过渡轨迹。
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引用次数: 0
Dynamic control of district heating networks with integrated emission modelling: A dynamic knowledge graph approach 利用综合排放建模对区域供热网络进行动态控制:动态知识图谱方法
Q1 Engineering Pub Date : 2024-05-18 DOI: 10.1016/j.egyai.2024.100376
Markus Hofmeister , Kok Foong Lee , Yi-Kai Tsai , Magnus Müller , Karthik Nagarajan , Sebastian Mosbach , Jethro Akroyd , Markus Kraft

This paper presents a knowledge graph-based approach for the dynamic control of a district heating network with integrated emission dispersion modelling. We propose an interoperable and extensible implementation to forecast the anticipated heat demand of a municipal heating network, minimise associated total generation cost based on a previously devised methodology, and couple it with dispersion simulations for induced airborne pollutants to provide automatic insights into air quality implications of various heat sourcing strategies. We create cross-domain interoperability in the nexus of energy and air quality via newly developed ontologies and semantic software agents, which can be chained together via The World Avatar dynamic knowledge graph to resemble the behaviour of complex systems. Furthermore, we integrate the City Energy Analyst into this ecosystem to provide building-level insights into energy demand and renewable generation potential to foster strategic analyses and scenario planning. Underlying calculations use building and weather data from the knowledge graph in place of inherent assumptions in the official software release, facilitating a more data-driven approach. All use cases are implemented for a mid-size town in Germany as a proof-of-concept, and a unified visualisation interface is provided, allowing for the examination of 3D buildings alongside their corresponding energy demand and supply time series, as well as emission dispersion data. With this work, we outline the potential of Semantic Web technologies to connect digital twins for holistic energy modelling in smart cities, thereby addressing the increasing complexity of interconnected energy systems.

本文介绍了一种基于知识图谱的方法,用于对区域供热网络进行动态控制,并集成了排放扩散建模。我们提出了一种具有互操作性和可扩展性的实施方案,用于预测市政供热网络的预期热需求,根据先前设计的方法最大限度地降低相关的总发电成本,并将其与诱导空气传播污染物的扩散模拟相结合,自动深入分析各种热源策略对空气质量的影响。我们通过新开发的本体论和语义软件代理,在能源和空气质量之间建立了跨领域互操作性,这些本体论和语义软件代理可以通过 "世界阿凡达 "动态知识图谱串联起来,以类似于复杂系统的行为。此外,我们还将 "城市能源分析仪 "整合到这一生态系统中,提供建筑层面的能源需求和可再生能源发电潜力,以促进战略分析和情景规划。基础计算使用知识图谱中的建筑和天气数据来代替正式软件版本中的固有假设,从而促进了更多的数据驱动方法。作为概念验证,我们在德国的一个中型城镇实施了所有用例,并提供了一个统一的可视化界面,允许在检查三维建筑物的同时,检查其相应的能源需求和供应时间序列以及排放分散数据。通过这项工作,我们概述了语义网技术在连接数字孪生系统以在智慧城市中进行整体能源建模方面的潜力,从而解决相互关联的能源系统日益复杂的问题。
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引用次数: 0
An enhanced semi-supervised learning method with self-supervised and adaptive threshold for fault detection and classification in urban power grids 用于城市电网故障检测和分类的自监督和自适应阈值增强型半监督学习方法
Q1 Engineering Pub Date : 2024-05-16 DOI: 10.1016/j.egyai.2024.100377
Jiahao Zhang , Lan Cheng , Zhile Yang , Qinge Xiao , Sohail Khan , Rui Liang , Xinyu Wu , Yuanjun Guo

With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intricate power grid systems. Although artificial intelligence technologies offer new solutions for power grid fault diagnosis, the difficulty in acquiring labeled grid data limits the development of AI technologies in this area. In response to these challenges, this study proposes a semi-supervised learning framework with self-supervised and adaptive threshold (SAT-SSL) for fault detection and classification in power grids. Compared to other methods, our method reduces the dependence on labeling data while maintaining high recognition accuracy. First, we utilize frequency domain analysis on power grid data to filter abnormal events, then classify and label these events based on visual features, to creating a power grid dataset. Subsequently, we employ the Yule–Walker algorithm extract features from the power grid data. Then we construct a semi-supervised learning framework, incorporating self-supervised loss and dynamic threshold to enhance information extraction capabilities and adaptability across different scenarios of the model. Finally, the power grid dataset along with two benchmark datasets are used to validate the model’s functionality. The results indicate that our model achieves a low error rate across various scenarios and different amounts of labels. In power grid dataset, When retaining just 5% of the labels, the error rate is only 6.15%, which proves that this method can achieve accurate grid fault detection and classification with a limited amount of labeled data.

随着城市电网的快速发展和可再生能源的大规模集成,传统的电网故障诊断技术难以解决错综复杂的电网系统中故障诊断的复杂性。虽然人工智能技术为电网故障诊断提供了新的解决方案,但获取标注电网数据的困难限制了人工智能技术在这一领域的发展。为了应对这些挑战,本研究提出了一种带有自监督和自适应阈值(SAT-SSL)的半监督学习框架,用于电网故障检测和分类。与其他方法相比,我们的方法减少了对标记数据的依赖,同时保持了较高的识别准确率。首先,我们利用电网数据的频域分析来过滤异常事件,然后根据视觉特征对这些事件进行分类和标记,从而创建一个电网数据集。随后,我们采用 Yule-Walker 算法从电网数据中提取特征。然后,我们构建了一个半监督学习框架,结合自监督损失和动态阈值来增强信息提取能力和模型在不同场景下的适应性。最后,我们使用电网数据集和两个基准数据集来验证模型的功能。结果表明,我们的模型在不同场景和不同标签量下都能实现较低的错误率。在电网数据集中,当仅保留 5%的标签时,错误率仅为 6.15%,这证明该方法可以在有限的标签数据量下实现准确的电网故障检测和分类。
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Energy and AI
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