Energy and AI最新文献_第8页

An integrated agent-based modelling and artificial intelligence framework for enhancing the experience of minority ethnic communities in digital energy services 一个集成的基于主体的建模和人工智能框架，用于增强少数民族社区在数字能源服务中的体验

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-09-22 DOI: 10.1016/j.egyai.2025.100624

Mennan Guder, Nazmiye Balta-Ozkan

Digitalisation plays a pivotal role in enhancing energy efficiency; however, it also highlights significant governance challenges and exacerbates various forms of energy injustice. This study explores how technological injustice exacerbates energy poverty, particularly via disparities in digital service access. The focus is on understanding and addressing challenges faced by minority ethnic (ME) communities, who often encounter heightened barriers to essential online energy services. While previous research has noted barriers ME communities face in energy markets, this study broadens this literature to analyse these issues for access to digital energy services.

The study integrates modelling, simulation, and AI to address these inequalities. The framework comprises three core modules: AI, Environment Configuration, and Agent-Based Modelling (ABM) and Simulation. Its primary aim is to identify effective strategies, policy changes, and adjustments that enhance online service experiences while addressing the unique challenges faced by these communities.

The AI Module uses ensemble-based ML pipelines to develop region-specific models. It addresses issues such as high dimensionality and overfitting by incorporating methods like Principal Component Analysis, Recursive Feature Elimination, and hyperparameter optimization.

The Environment Configuration Module supports tailored simulations by adapting datasets and regional characteristics, ensuring the accuracy and relevance of the simulations to the target communities.

The ABM and Simulation Module facilitates in-depth analysis of policy impacts and service provider attributes.

This framework offers valuable insights into improving online service delivery, promoting fairness, and addressing disparities in digital experiences. This work advances energy justice research by quantifying how socio-technical barriers disproportionately affect ME communities.

数字化在提高能源效率方面发挥着关键作用；然而，它也凸显了重大的治理挑战，并加剧了各种形式的能源不公平。本研究探讨了技术不公正如何加剧能源贫困，特别是通过数字服务获取方面的差异。重点是了解和解决少数民族（ME）社区面临的挑战，他们在获得基本的在线能源服务方面经常遇到更高的障碍。虽然以前的研究已经注意到中小企业社区在能源市场上面临的障碍，但本研究扩大了这一文献，分析了这些问题，以获得数字能源服务。该研究整合了建模、仿真和人工智能来解决这些不平等问题。该框架包括三个核心模块：人工智能、环境配置和基于代理的建模（ABM）和仿真。其主要目的是确定有效的战略、政策变化和调整，以增强在线服务体验，同时解决这些社区面临的独特挑战。AI模块使用基于集成的ML管道来开发特定区域的模型。它通过结合主成分分析、递归特征消除和超参数优化等方法来解决诸如高维数和过拟合等问题。环境配置模块通过调整数据集和区域特征来支持量身定制的模拟，确保模拟与目标社区的准确性和相关性。ABM和仿真模块有助于对策略影响和服务提供者属性进行深入分析。该框架为改善在线服务交付、促进公平和解决数字体验中的差异提供了宝贵的见解。这项工作通过量化社会技术障碍如何不成比例地影响ME社区来推进能源正义研究。

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引用次数: 0

Explainable and generalizable AI for AGC dispatch with heterogeneous generation units: A case study using graph convolutional networks 具有异构发电单元的AGC调度的可解释和可推广的人工智能：使用图卷积网络的案例研究

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-09-19 DOI: 10.1016/j.egyai.2025.100621

Xiaoshun Zhang , Kun Zhang , Zhengxun Guo , Penggen Wang , Penghui Xiong , Mingyu Wang

Automatic generation control (AGC) dispatch is essential for maintaining frequency stability and power balance in modern grids with high renewable penetration. Conventional optimization and machine learning methods either incur heavy computational costs or act as black-box models, which limits interpretability and generalization in safety–critical operations. To overcome these gaps, we propose an explainable and generalizable framework that integrates graph convolutional networks (GCNs) with Shapley additive explanations (SHAP). SHAP provides quantitative feature attributions, revealing spatiotemporal variability and redundancy, while the derived insights are used to iteratively optimize the GCN adjacency matrix and capture inter-generator dependencies more effectively. This closed-loop design enhances both model transparency and robustness. Case studies on a two-area load frequency control (LFC) system and a provincial power grid in China show consistent improvements: in the LFC model, frequency deviation, power deviation, and ACE are reduced by 14.30%, 58.95%, and 29.22%, respectively; in the provincial grid, ACE overshoot decreases by 99.52%, frequency deviation by 80.67%, and power overshoot is eliminated, with correction distance reduced by up to 55.24%. These results demonstrate that explainability-driven graph learning can significantly improve the reliability and adaptability of AI-based AGC dispatch in complex, heterogeneous power systems.

在可再生能源普及率高的现代电网中，自动发电控制（AGC）调度对于保持频率稳定和功率平衡至关重要。传统的优化和机器学习方法要么产生大量的计算成本，要么充当黑盒模型，这限制了安全关键操作的可解释性和通用性。为了克服这些差距，我们提出了一个可解释和可推广的框架，该框架将图卷积网络（GCNs）与Shapley加性解释（SHAP）集成在一起。SHAP提供定量特征归因，揭示时空变异性和冗余，而派生的见解用于迭代优化GCN邻接矩阵并更有效地捕获生成器之间的依赖关系。这种闭环设计提高了模型透明性和鲁棒性。两区负荷频率控制（LFC）系统和中国省级电网的案例研究表明，在LFC模型下，频率偏差、功率偏差和ACE分别降低了14.30%、58.95%和29.22%；省网ACE超调减小99.52%，频率偏差减小80.67%，消除功率超调，校正距离减小55.24%。这些结果表明，可解释性驱动的图学习可以显著提高复杂异构电力系统中基于ai的AGC调度的可靠性和适应性。

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引用次数: 0

Energy-GNoME: A living database of selected materials for energy applications energy - gnome：能源应用中选定材料的活数据库

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-09-16 DOI: 10.1016/j.egyai.2025.100605

Paolo De Angelis , Giulio Barletta , Giovanni Trezza , Pietro Asinari , Eliodoro Chiavazzo

Artificial Intelligence (AI) in materials science is driving significant advancements in the discovery of advanced materials for energy applications. The recent GNoME protocol identifies over 380,000 novel stable crystals. From this, we identify over 38,500 materials with potential as energy materials forming the core of the Energy-GNoME database. Our unique combination of Machine Learning (ML) and Deep Learning (DL) tools mitigates cross-domain data bias using feature spaces, thus identifying potential candidates for thermoelectric materials, novel battery cathodes, and novel perovskites. First, classifiers with both structural and compositional features detect domains of applicability, where we expect enhanced reliability of regressors. Here, regressors are trained to predict key materials properties, like thermoelectric figure of merit (

z T

), band gap (

E_{g}

), and cathode voltage (

Δ V_{c}

). This method significantly narrows the pool of potential candidates, serving as an efficient guide for experimental and computational chemistry investigations and accelerating the discovery of materials suited for electricity generation, energy storage and conversion.

材料科学中的人工智能（AI）正在推动能源应用先进材料的发现取得重大进展。最近的GNoME协议确定了超过38万个新的稳定晶体。由此，我们确定了超过38,500种有潜力成为能源材料的材料，形成了energy - gnome数据库的核心。我们独特的机器学习（ML）和深度学习（DL）工具组合使用特征空间减轻了跨域数据偏差，从而确定了热电材料、新型电池阴极和新型钙钛矿的潜在候选材料。首先，具有结构和组成特征的分类器检测适用性领域，我们期望在这些领域增强回归器的可靠性。在这里，回归量被训练来预测关键的材料性能，如热电性能图（zT）、带隙（Eg）和阴极电压（ΔVc）。这种方法大大缩小了潜在候选材料的范围，为实验和计算化学研究提供了有效的指导，并加速了适合发电、储能和转换的材料的发现。

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引用次数: 0

Sequential constrained optimization for multi-entity operation of integrated electricity-gas distribution systems 电-气一体化系统多实体运行的序贯约束优化

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-09-13 DOI: 10.1016/j.egyai.2025.100619

Yeong Geon Son, Sung-Yul Kim

The reliable and coordinated operation of energy systems is becoming increasingly important as renewable energy penetration grows and electricity and gas infrastructures become more interconnected. This study addresses the challenge of aligning multiple stakeholders’ objectives in integrated electricity and gas distribution systems by proposing a sequential constrained optimization method. The method solves the multi-objective optimization problem by sequentially prioritizing each entity’s objective while incorporating others as adaptive-weighted sub-objectives and constraints. This process ensures that all entities participate in a fair and balanced decision-making procedure, ultimately converging to a consensus-based solution. The algorithm is validated using IEEE 33-bus and 118-bus test systems coupled with gas networks. Results show that the proposed method improves optimal resource allocation effectiveness by up to 3.66 compared to individual-objective or aggregated-objective benchmarks. Specifically, the method achieves performance improvements ranging from 0.02 pu to 1.7 pu across four distinct entities, highlighting its superiority in balancing conflicting operational goals. Moreover, the method demonstrates low computational delay and converges in fewer than 15 iterations for all tested cases. The algorithm adapts flexibly to different system configurations and maintains solution stability even under asymmetric stakeholder preferences. These findings indicate that the proposed sequential constrained optimization framework is a scalable and effective approach for equitable, multi-agent coordination in integrated multi-energy systems.

随着可再生能源普及率的提高以及电力和天然气基础设施的相互联系日益紧密，能源系统的可靠和协调运行变得越来越重要。本研究通过提出一种顺序约束优化方法，解决了在综合电力和天然气分配系统中协调多个利益相关者目标的挑战。该方法通过对每个实体的目标进行排序，同时将其他实体的目标作为自适应加权子目标和约束，解决了多目标优化问题。这一进程确保所有实体参与公平和平衡的决策程序，最终达成基于协商一致的解决办法。采用IEEE 33总线和118总线测试系统，结合燃气网络对算法进行了验证。结果表明，与个体目标基准和聚合目标基准相比，所提方法的最优资源分配效率提高了3.66。具体来说，该方法在四个不同的实体上实现了从0.02到1.7 pu的性能改进，突出了其在平衡冲突的操作目标方面的优势。此外，该方法具有较低的计算延迟，并且在所有测试用例的15次迭代以内收敛。该算法能够灵活适应不同的系统配置，即使在利益相关者偏好不对称的情况下也能保持解的稳定性。这些结果表明，所提出的顺序约束优化框架是一种可扩展且有效的方法，可用于集成多能系统中公平的多智能体协调。

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引用次数: 0

Predictive maintenance for wind turbines: A physics-driven reinforcement learning strategy with economic-reliability collaborative optimization 风力涡轮机的预测性维护：具有经济可靠性协同优化的物理驱动强化学习策略

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-09-13 DOI: 10.1016/j.egyai.2025.100620

Jianghao Zhu , Wei Chen , Le Su , Bin Lan , Tingting Pei , Long Jin

Wind turbine maintenance optimization faces challenges in balancing economic efficiency with operational reliability under environmental uncertainty. Traditional maintenance approaches exhibit limitations in adaptive decision-making, leading to increased operational costs and reliability risks. This study develops a physics-informed reinforcement learning framework that integrates established domain knowledge with adaptive decision algorithms. The approach embeds physical principles—including Weibull wind dynamics and multi-stage degradation models—into a reinforcement learning architecture, while introducing bidirectional temperature-degradation coupling for enhanced failure prediction. A high-fidelity simulation environment enables policy training through Proximal Policy Optimization, capturing complex interactions between environmental variability and equipment deterioration. The framework was validated through case study implementation using northern China wind farm operational data. Results demonstrate zero-failure operation over simulated 19-year lifecycles, with economic performance improvements of 109.3 % and 54.5 % compared to conventional periodic and threshold-based maintenance strategies. By integrating physical constraints with intelligent algorithms, the method achieves adaptive maintenance decisions based on multi-dimensional state information.

在环境不确定性条件下，风电机组维护优化面临着平衡经济效益与运行可靠性的挑战。传统的维护方法在自适应决策方面存在局限性，导致运营成本和可靠性风险增加。本研究开发了一个基于物理的强化学习框架，该框架将已建立的领域知识与自适应决策算法集成在一起。该方法将物理原理（包括威布尔风动力学和多阶段退化模型）嵌入到强化学习架构中，同时引入双向温度-退化耦合以增强故障预测。高保真仿真环境通过近端策略优化实现策略训练，捕获环境可变性和设备退化之间的复杂相互作用。通过对中国北方风电场运行数据的案例研究，对该框架进行了验证。结果表明，在模拟的19年生命周期内，零故障运行，与传统的定期和基于阈值的维护策略相比，经济性能提高了109.3%和54.5%。该方法将物理约束与智能算法相结合，实现了基于多维状态信息的自适应维修决策。

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引用次数: 0

Consumer phase identification in distribution grids using Graph Neural Networks based on synthetic and measured power profiles 基于综合和实测功率分布的图神经网络在配电网中的用户相位识别

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-09-10 DOI: 10.1016/j.egyai.2025.100607

Chandra Sekhar Charan Dande , Nikolaos A. Efkarpidis , Matthias Christen , Mirko Ginocchi , Antonello Monti

Most distribution system operators may not accurately record or completely maintain the phase connections for the numerous LV customers. Different consumer phase identification (CPI) approaches based on voltages, powers or other measurements are proposed in the literature. Due to the technical challenges in collecting voltage measurements, power measurement based approaches are preferable. Hence, this paper proposes a novel power based CPI methodology applying Graph Neural Networks (GNNs). The CPI methodology generates synthetic transformer power profiles assuming random combinations of phases for the measured load profiles, which are used altogether to train the GNN model. The GNN model is then tested using measured transformer and load power profiles. The performance of the methodology is evaluated in a test low voltage grid of 55 loads under various conditions of Photovoltaic penetration, Photovoltaics under maintenance, measurement errors, unmetered consumption, uncertain grid asset parameters and inaccurate phase connections. Further tests on a real low voltage grid with 111 loads prove the scalability of the methodology. The attained results show that the GNN model can achieve accuracy above 90% in most cases, outperforming various state-of-the-art methods.

大多数配电系统操作员可能无法准确记录或完整地维护众多低压客户的相位连接。不同的消费者相识别（CPI）方法基于电压，功率或其他测量在文献中提出。由于收集电压测量的技术挑战，基于功率测量的方法是优选的。因此，本文提出了一种应用图神经网络（GNNs）的基于功率的CPI方法。CPI方法生成综合变压器功率曲线，假设所测负载曲线的相位随机组合，这些曲线一起用于训练GNN模型。然后使用测量的变压器和负载功率曲线对GNN模型进行测试。在光伏渗透、光伏维护、测量误差、未计量消耗、电网资产参数不确定和相连接不准确等多种条件下，对55个负载的低压电网进行了性能评估。在111负荷的低压电网上的进一步测试证明了该方法的可扩展性。结果表明，在大多数情况下，GNN模型的准确率可以达到90%以上，优于各种最新的方法。

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引用次数: 0

A hybrid Artificial Intelligence method for estimating flicker in power systems 电力系统闪变估计的混合人工智能方法

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-09-10 DOI: 10.1016/j.egyai.2025.100614

Javad Enayati , Pedram Asef , Alexandre Benoit

This paper introduces a novel hybrid method combining H-

\infty

filtering and an adaptive linear neuron (ADALINE) network for flicker component estimation in power distribution systems. The proposed method leverages the robustness of the H-

\infty

filter to extract the voltage envelope under uncertain and noisy conditions, followed by the use of ADALINE to accurately identify the relative amplitudes of flicker components (

Δ V_{i} / V_{t}

) at standard IEC-defined frequencies embedded in the envelope. This synergy enables efficient time-domain estimation with rapid convergence and noise resilience, addressing key limitations of existing frequency-domain approaches. Unlike conventional techniques, this hybrid model handles complex power disturbances without prior knowledge of noise characteristics or extensive training. To validate the method’s performance, we conduct simulation studies based on IEC Standard 61000-4-15, supported by statistical analysis, Monte Carlo simulations, and real-world data. Results demonstrate superior accuracy, robustness, and reduced computational load compared to Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT)-based estimators.

介绍了一种结合H-∞滤波和自适应线性神经元（ADALINE）网络的配电系统闪变分量估计新方法。该方法利用H-∞滤波器的鲁棒性提取不确定和噪声条件下的电压包络，然后使用ADALINE准确识别嵌入在包络中的iec定义的标准频率下闪烁分量的相对幅度（ΔVi/Vt）。这种协同作用使有效的时域估计具有快速收敛和噪声弹性，解决了现有频域方法的主要局限性。与传统技术不同，这种混合模型处理复杂的功率干扰，而不需要事先了解噪声特性或广泛的训练。为了验证该方法的性能，我们基于IEC标准61000-4-15进行了仿真研究，并通过统计分析、蒙特卡罗模拟和实际数据进行了支持。结果表明，与基于快速傅立叶变换（FFT）和基于离散小波变换（DWT）的估计器相比，具有更高的精度、鲁棒性和更少的计算负荷。

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引用次数: 0

Machine-learning-accelerated screening of multi-element doped CuSb catalysts for enhanced C2+ selectivity in CO2 electroreduction 机器学习加速筛选多元素掺杂CuSb催化剂以增强CO2电还原中C2+的选择性

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-09-10 DOI: 10.1016/j.egyai.2025.100613

Xin Cheng , Hang Wang , Xun Zhu , Yang Wang , Qian Fu

Electrochemical CO₂ reduction (CO₂RR) to value-added fuels and chemicals offers a promising route toward carbon neutrality. However, developing efficient and selective catalysts for the generation of multi-carbon (C₂₊) products remains a significant challenge. In this work, we propose a combined density functional theory (DFT) and machine learning (ML) approach to systematically screen CuSb-based catalysts with varying surface Sb atomic fractions and non-metal dopants (O, N, S, Se, and P) on the Cu₂Sb(100) surface for CO₂RR. Approximately 200 representative adsorption configurations were randomly selected for DFT calculations, which were then used to train a predictive ML model. This model enables high-accuracy predictions of the adsorption energies of key intermediates (*CO and *H) for the remaining uncalculated configurations. By integrating the K-means clustering analysis and the optimal adsorption energy selection criteria based on the Sabatier principle, the candidate configuration with the best potential for C₂₊ product formation was identified: O-doped CuSb with a surface Sb atomic fraction of 3/12. Mechanistic studies further reveal that O doping significantly strengthens *CO adsorption while suppressing *H adsorption by modulating the electronic structure, thereby lowering the CO₂RR energy barrier and improving the thermodynamic selectivity toward C₂₊ products. This work not only elucidates the synergistic effect of surface Sb atomic fraction and non-metal dopants on CO₂RR activity, but also establishes a scalable ML prediction and screening framework, providing theoretical support and methodological pathways for the design of high-performance CuSb-based catalysts.

电化学二氧化碳还原（CO2RR）为增值燃料和化学品提供了一条有前途的碳中和途径。然而，开发高效和选择性的催化剂来生成多碳（C2+）产品仍然是一个重大挑战。在这项工作中，我们提出了一种结合密度泛函理论（DFT）和机器学习（ML）的方法来系统地筛选Cu2Sb（100）表面上具有不同表面Sb原子分数和非金属掺杂物（O， N， S， Se和P）的cu基催化剂。随机选择大约200个代表性的吸附构型进行DFT计算，然后用于训练预测ML模型。该模型能够高精度地预测剩余未计算构型的关键中间体（*CO和*H）的吸附能。结合K-means聚类分析和基于Sabatier原理的最佳吸附能选择标准，确定了最有可能形成C2+产物的候选构型：表面Sb原子分数为3/12的o掺杂CuSb。机理研究进一步表明，O掺杂通过调节电子结构显著增强*CO吸附，抑制*H吸附，从而降低CO2RR能垒，提高对C2+产物的热力学选择性。本研究不仅阐明了表面Sb原子分数和非金属掺杂物对CO2RR活性的协同作用，而且建立了可扩展的ML预测和筛选框架，为设计高性能cusb基催化剂提供了理论支持和方法途径。

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引用次数: 0

Deep kernel Bayesian optimisation for closed-loop electrode microstructure design with user-defined properties 深核贝叶斯优化闭环电极微结构设计与用户自定义的属性

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-09-10 DOI: 10.1016/j.egyai.2025.100608

Andrea Gayon-Lombardo , Ehecatl A. del Rio-Chanona , Catalina A. Pino-Muñoz , Nigel P. Brandon

The generation of multiphase porous electrode microstructures with optimum morphological and transport properties is essential in the design of improved electrochemical energy storage devices, such as lithium-ion batteries. Electrode characteristics directly influence battery performance by acting as the main sites where the electrochemical reactions coupled with transport processes occur. This work presents a generation-optimisation closed-loop algorithm for the design of microstructures with tailored properties. A deep convolutional Generative Adversarial Network is used as a deep kernel and employed to generate synthetic three-phase three-dimensional images of a porous lithium-ion battery cathode material. A Gaussian Process Regression uses the latent space of the generator and serves as a surrogate model to correlate the morphological and transport properties of the synthetic microstructures. This surrogate model is integrated into a deep kernel Bayesian optimisation framework, which optimises cathode properties as a function of the latent space of the generator. A set of objective functions were defined to perform the maximisation of morphological properties (e.g., volume fraction, specific surface area) and transport properties (relative diffusivity). We demonstrate the ability to perform simultaneous maximisation of correlated properties (specific surface area and relative diffusivity), as well as constrained optimisation of these properties. This is the maximisation of morphological or transport properties constrained by constant values of the volume fraction of the phase of interest. Visualising the optimised latent space reveals its correlation with morphological properties, enabling the fast generation of visually realistic microstructures with customised properties.

生成具有最佳形态和输运特性的多相多孔电极微结构对于改进电化学储能装置（如锂离子电池）的设计至关重要。电极特性作为电化学反应和输运过程发生的主要场所，直接影响电池的性能。这项工作提出了一种生成优化闭环算法，用于设计具有定制特性的微结构。采用深度卷积生成对抗网络作为深度核，合成多孔锂离子电池正极材料的三相三维图像。高斯过程回归使用生成器的潜在空间，并作为代理模型来关联合成微观结构的形态和传输特性。该代理模型集成到一个深度核贝叶斯优化框架中，该框架将阴极特性作为发生器潜在空间的函数进行优化。我们定义了一组目标函数，以实现形态特性（如体积分数、比表面积）和输运特性（相对扩散率）的最大化。我们展示了同时最大化相关属性（比表面积和相对扩散率）以及这些属性的约束优化的能力。这是受感兴趣的相的体积分数恒定值约束的形态或输运性质的最大化。可视化优化的潜在空间揭示了其与形态特性的相关性，从而能够快速生成具有定制特性的视觉逼真的微结构。

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引用次数: 0

An uncertainty-aware bi-level multitask SqueezeNet for dynamic security assessment in power systems with focus on critical generator identification under small and imbalanced datasets 基于不确定感知的双级多任务SqueezeNet的电力系统动态安全评估，重点研究小数据和不平衡数据下关键发电机的识别

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Energy and AI

Pub Date : 2025-09-09 DOI: 10.1016/j.egyai.2025.100618

Sasan Azad , Mohammad Taghi Ameli , Amir Reza Shafieinejad , Hossein Ameli , Goran Strbac

Deep learning (DL)-based methods in pre-fault dynamic security assessment (DSA) have provided significant results, contributing to the safe operation of power systems. However, power systems often suffer from insufficient, small, and imbalanced datasets, which significantly impact the performance of DL-based DSA models. Existing DSA frameworks typically operate as two-class black-box models, assessing only overall system security without providing insights into the causes of insecurity or identifying critical generators (CGs), and they fail to quantify prediction uncertainty. These challenges hinder the implementation of current methods in real-world power systems and reduce operators' confidence in them. To address these issues, this paper proposes an uncertainty-aware bi-level multitask learning framework based on transfer learning and SqueezeNet architecture. The framework assesses system security, identifies CGs during instability, and leverages fine-tuning of a pre-trained SqueezeNet model to facilitate training with limited data. Additionally, evidential deep learning is incorporated to quantify classification uncertainty. Without relying on the complex and challenging data augmentation method, this framework uses a simple technique called optimal classification threshold determination to mitigate the negative impact of imbalanced data on model performance. The optimal threshold is determined by maximizing the area under the receiver operating characteristic (ROC) curve. The application of the proposed method to the IEEE 118-bus system shows its strong performance. These results offer crucial technical insights for the implementation of DL-based DSA in real-world power systems.

基于深度学习（DL）的故障前动态安全评估（DSA）方法在电力系统安全运行方面取得了重要成果。然而，电力系统经常受到数据集不足、小和不平衡的影响，这严重影响了基于dl的DSA模型的性能。现有的DSA框架通常作为两类黑盒模型运行，仅评估整体系统安全性，而没有提供对不安全原因的深入了解或识别关键生成器（cg），并且它们无法量化预测的不确定性。这些挑战阻碍了当前方法在实际电力系统中的实施，并降低了运营商对这些方法的信心。为了解决这些问题，本文提出了一种基于迁移学习和SqueezeNet架构的不确定性感知双级多任务学习框架。该框架评估系统安全性，识别不稳定期间的cg，并利用预训练的SqueezeNet模型进行微调，以促进有限数据的训练。此外，证据深度学习被纳入量化分类不确定性。该框架不依赖于复杂且具有挑战性的数据增强方法，而是使用一种称为最优分类阈值确定的简单技术来减轻不平衡数据对模型性能的负面影响。最佳阈值是通过使受试者工作特征（ROC）曲线下面积最大化来确定的。在IEEE 118总线系统中的应用表明，该方法具有良好的性能。这些结果为在现实世界的电力系统中实现基于dl的DSA提供了关键的技术见解。

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引用次数: 0