International Journal of Electrical Power & Energy Systems最新文献_第8页

Leveraging the charging flexibility of electric vehicles in sequential electricity markets with frequency regulation limit 在有频率调节限制的序贯电力市场中，利用电动汽车的充电灵活性

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-01-22 DOI: 10.1016/j.ijepes.2026.111592

Shuang Gao , Shengyu Huang , Xiaolong Jin , Yuming Zhao , Wenjun Tang

As the intraday market plays an important role in absorbing uncertainty from renewable energy sources, deriving trading decisions that optimizing economic benefits across sequential day-ahead and real-time markets become increasingly complex. This paper develops a multi-time scale charging strategy for electric vehicles (EVs) to participate in different electricity market segments. The EV charging optimization is done in two stages: first in a day-ahead scheduling of energy and regulation capacity, and then refined during the day when close to real-time delivery. The EV charging energy and the capacity reserved for frequency regulation are optimized in the day-ahead market. The trading decisions for EV charging energy and regulation capacity in the real-time market are determined considering the uncertainties of EV charging behaviors and the energy deviation from actual delivery of frequency regulation. A dynamic EV control model for frequency regulation is used to quantify the regulation capacity during unforeseen contingencies, which is added to the EV charging optimization as a security constraint. Real data of market prices and regulation signals from PJM (ISO in the United States) is used to analyze the flexibility of EV charging and market revenue potentials by considering all market segments as a whole. Numerical results reveal that providing frequency regulation achieves a cost saving up to 59% for EV charging. Around 27% of the cost saving is obtained by energy transactions in the real-time market. Furthermore, the negative impacts of uncertainties from EV availability and the deployment of frequency regulation are also effectively mitigated by integrating real-time market bidding process into the proposed multi-scale optimization.

由于日内市场在吸收可再生能源的不确定性方面发挥着重要作用，因此在连续的前一天和实时市场中得出优化经济效益的交易决策变得越来越复杂。针对不同的电力细分市场，提出了电动汽车多时间尺度充电策略。电动汽车充电优化分两个阶段进行，首先是在一天前的能量调度和调节容量，然后在接近实时交付的白天进行细化。在日前市场下，对电动汽车充电能量和调频预留容量进行了优化。考虑电动汽车充电行为的不确定性和频率调节实际交付的能量偏差，确定实时市场中电动汽车充电能量和调节容量的交易决策。建立了电动汽车动态频率调节模型，量化了不可预见事件下的调节能力，并将其作为安全约束加入到电动汽车充电优化中。采用PJM（美国ISO）的真实市场价格数据和监管信号，综合考虑各个细分市场，分析电动汽车充电的灵活性和市场收入潜力。数值结果表明，提供频率调节可为电动汽车充电节省高达59%的成本。大约27%的成本节约是通过实时市场的能源交易获得的。此外，通过将实时市场竞价过程整合到所提出的多尺度优化中，有效地缓解了电动汽车可用性和频率调节部署不确定性的负面影响。

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引用次数: 0

Flexible mesh-shaped distribution network topology with adaptive geographical layered-clustered probabilistic planning 具有自适应地理分层聚类概率规划的柔性网格型配电网拓扑

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-01-22 DOI: 10.1016/j.ijepes.2026.111583

Shaohan Lu , Hong Liu , Qizhe Li , Peng Zhang , Bo Peng , Bin Xu

With the large-scale integration of distributed renewable energy, traditional distribution network topologies lack the flexibility to fully exploit wide-area source-load complementarity and flexible resources, limiting the efficient and secure accommodation of distributed generation (DG). To address this, we propose a mesh-shaped distribution network topology with a three-terminal soft open point (SOP) as the core device, balancing existing grid features with retrofit complexity. Given the high-dimensional, complex MILP resulting from planning under strong stochasticity, we introduce a novel geographical layered-clustered planning method. This method, utilizing an improved fast-unfolding algorithm, considers spatio-temporal source-load distribution, customer reliability, and line-construction needs during clustering, effectively decomposing the planning problem into parallelizable subproblems to improve efficiency. A real-world case study validates the proposed method, identifying the most suitable application scenarios for the mesh-shaped topology. Compared to conventional feeder-partition-based planning, our method accommodates current target structures’ construction requirements while saving over 80% of computation time with only a 1.71% loss in optimality.

随着分布式可再生能源的大规模集成，传统配电网拓扑结构缺乏充分利用广域源荷互补性和灵活资源的灵活性，限制了分布式发电的高效安全适配。为了解决这一问题，我们提出了一种以三端软开点（SOP）为核心设备的网格状配电网拓扑结构，以平衡现有电网的特征和改造的复杂性。针对强随机性规划导致的高维、复杂的MILP问题，提出了一种新的地理分层聚类规划方法。该方法利用改进的快速展开算法，在聚类过程中考虑了时空源负荷分布、客户可靠性和线路建设需求，有效地将规划问题分解为可并行化的子问题，提高了效率。一个实际案例研究验证了所提出的方法，确定了最适合网格形状拓扑的应用场景。与传统的基于馈线分区的规划相比，我们的方法在满足当前目标结构的施工要求的同时节省了80%以上的计算时间，而最优性仅损失了1.71%。

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引用次数: 0

Control of multiple parallel HVDC systems for frequency response sharing: A study based on synchronous frequency operation of the asynchronously interconnected systems in China 多并联高压直流系统频率响应共享控制——基于中国异步互联系统同步频率运行的研究

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-01-21 DOI: 10.1016/j.ijepes.2026.111586

Yan Guo , Chunguang Zhou , Shengmin Qiu , Ke Wang , Yiping Chen , Zhixuan Li

To enhance frequency stability, the integration of High Voltage Direct Current (HVDC) systems for frequency control has been widely employed. In 2023, a frequency control strategy termed co-frequency control was deployed in the LUXI Back-to-Back (BTB) VSC-HVDC system of the China Southern Power Gird (CSG) to mitigate frequency deviations between interconnected asynchronous grids. Nevertheless, reliance on a single HVDC system implementing co-frequency control presents three major challenges: limited frequency regulation headroom, the absence of backup control capability during HVDC maintenance, and the occurrence of low-frequency DC power oscillations (LFPO). Consequently, the control falls short of fully satisfying the operational expectations of CSG. Since incorporating additional HVDC systems into co-frequency control is regarded as an effective measure to address the first two challenges, this paper proposes a coordinated scheme for multiple parallel HVDC systems participating in co-frequency control. The proposed scheme is formulated as an optimization problem that calculates and updates the frequency control coefficients of the HVDC systems. These coefficients are obtained by solving the developed optimization problem, which accounts for the power headroom of each HVDC system, the N-1 HVDC blocking fault security criterion, and the stability requirements of the HVDC system. As a result, the scheme ensures the secure operation of multiple parallel HVDC systems in co-frequency control during both load variations and HVDC outages. The effectiveness of the proposed method is validated through Real-Time Digital Simulator (RTDS).

为了提高频率的稳定性，集成高压直流（HVDC）系统进行频率控制已得到广泛应用。2023年，在中国南方电网（CSG）的LUXI背对背（BTB） VSC-HVDC系统中部署了一种称为共频控制的频率控制策略，以减轻互联异步电网之间的频率偏差。然而，依靠单一的高压直流系统实现共频控制存在三个主要挑战：有限的频率调节空间，在高压直流维护期间缺乏备用控制能力，以及低频直流功率振荡（LFPO）的发生。因此，控制不能完全满足CSG的操作期望。由于将额外的高压直流系统纳入共频控制被认为是解决前两个挑战的有效措施，因此本文提出了多个并联高压直流系统参与共频控制的协调方案。本文提出的方案是一个计算和更新高压直流系统频率控制系数的优化问题。这些系数是通过求解所建立的优化问题得到的，该优化问题考虑了各直流系统的功率净空、N-1直流阻塞故障安全准则和直流系统的稳定性要求。该方案保证了多个并联高压直流系统在负荷变化和高压直流停电情况下的共频控制安全运行。通过实时数字仿真（RTDS）验证了该方法的有效性。

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引用次数: 0

A feature-guided adaptive frequency band optimization framework for fault diagnosis of wind turbine pitch bearings 风电节距轴承故障诊断的特征引导自适应频带优化框架

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-01-21 DOI: 10.1016/j.ijepes.2026.111585

Lei Hu , Longda Yao , Xiaoli Tang , Shuo Zhang , Yuandong Xu

Ensuring the reliability of pitch bearings is critical for the safe and efficient operation of wind turbines within modern power systems. However, fault diagnosis under low-speed reciprocating conditions remains extremely challenging due to weak fault-induced impulses and non-stationary dynamics. This paper proposes a Feature-Guided Adaptive Frequency Band Optimization (FAFBO) for fault diagnosis of low-speed pitch bearings. In the proposed diagnostic strategy, a signal reconstruction method is developed to eliminate the effects of reciprocating dynamics and extract relatively stationary signals for further analysis using the encoder zero-position alignment. The reconstructed signals are then analyzed by the Refined Fault Harmonics Index (RFHI) to suppress reversal impacts and stitching artifacts and extract effective fault features through two-stage coarse-to-fine grid search optimization algorithm. Experimental studies demonstrate that the FAFBO is effective and robust for pitch bearing fault diagnosis at low signal-to-noise ratios. The proposed framework provides a reliable and computationally efficient tool for condition monitoring and preventive maintenance of wind turbine pitch systems, contributing to improved reliability of wind energy generation.

在现代电力系统中，确保节距轴承的可靠性对于风力涡轮机的安全高效运行至关重要。然而，在低速往复工况下，由于较弱的故障诱发脉冲和非平稳动力学，故障诊断仍然极具挑战性。提出了一种基于特征引导的自适应频带优化方法（FAFBO）用于低速节距轴承故障诊断。在所提出的诊断策略中，开发了一种信号重建方法，以消除往复动力学的影响，并提取相对平稳的信号，以便使用编码器零位置对准进行进一步分析。利用改进的故障谐波指数（RFHI）对重构信号进行分析，抑制反转影响和拼接伪像，并通过两阶段粗到细网格搜索优化算法提取有效的故障特征。实验研究表明，在低信噪比条件下，FAFBO对螺距轴承故障诊断具有良好的鲁棒性和有效性。所提出的框架为风力发电机桨距系统的状态监测和预防性维护提供了可靠且计算效率高的工具，有助于提高风力发电的可靠性。

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引用次数: 0

Intelligent optimal stepwise inertial control for wind power frequency regulation: a fuzzy logic and SDAE-DNN framework 风电频率调节的智能最优逐步惯性控制：模糊逻辑和SDAE-DNN框架

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-01-21 DOI: 10.1016/j.ijepes.2026.111584

Tao Zhou , Yalun Wang , Siqi Bu , Dejian Yang

The rapid integration of wind power into modern power systems, while essential for decarbonization, significantly reduces grid inertia, leading to critical frequency stability challenges following disturbances. Traditional stepwise inertial control (SIC) strategies for wind turbines (WT) often fail to prevent secondary frequency drops (SFD) due to their fixed parameters and lack of synchronization with synchronous generator (SG) recovery dynamics. To address these limitations, this paper proposes a novel optimal SIC framework that leverages fuzzy logic control and a stacked denoising autoencoder-deep neural network (SDAE-DNN) to dynamically adapt wind turbine power output in response to real-time grid conditions. The fuzzy controller intelligently balances grid frequency support with turbine safety by prioritizing rotor speed recovery when approaching critical limits, effectively eliminating SFD. The SDAE-DNN enhances adaptability by learning optimal control parameters across diverse operating scenarios, enabling real-time, computationally efficient implementation. Extensive simulations on the IEEE 39-bus system demonstrate that the proposed strategy can effectively improve the frequency nadir compared to conventional methods and completely avoid SFD. The framework is successfully scaled to wind farm level, ensuring coordinated, secure, and optimal frequency support under high renewable penetration, offering a practical solution for enhancing grid resilience.

风能与现代电力系统的快速整合虽然对脱碳至关重要，但也显著降低了电网惯性，从而导致干扰后的关键频率稳定性挑战。传统的风力发电机组逐步惯性控制策略由于参数固定且与同步发电机恢复动态不同步，往往无法有效防止二次频降。为了解决这些限制，本文提出了一种新的最优SIC框架，该框架利用模糊逻辑控制和堆叠去噪自编码器深度神经网络（SDAE-DNN）来动态适应风力涡轮机的功率输出，以响应实时电网条件。模糊控制器通过在接近临界极限时优先考虑转子转速恢复，智能地平衡电网频率支持与涡轮机安全，有效地消除了SFD。SDAE-DNN通过学习不同操作场景的最优控制参数来增强适应性，实现实时、计算效率高的实现。在IEEE 39总线系统上的大量仿真表明，与传统方法相比，该策略可以有效地改善频率最低点，完全避免SFD。该框架已成功扩展到风力发电场水平，确保在可再生能源高渗透率下的协调、安全和最佳频率支持，为增强电网弹性提供了实用的解决方案。

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引用次数: 0

Fast diffusion-driven data augmentation for imbalanced power-system stability classification 不平衡电力系统稳定性分类的快速扩散驱动数据增强

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-01-21 DOI: 10.1016/j.ijepes.2026.111559

Hangdong An, Jin Ma

The increasing integration of intermittent renewable energies amplifies uncertainty in power system operation, complicating stability assessment and limiting the effectiveness of traditional model-based approaches. To address this challenge, data-driven methods have emerged as flexible alternatives, but they often suffer from severe class imbalance, with unstable cases being relatively rare. Generative models like Generative Adversarial Models (GANs) and Variational Autoencoders (VAEs) have been explored for sample augmentation, yet frequently encounter issues such as mode collapse and insufficient diversity. More recently, Denoising diffusion probabilistic models (DDPMs) have shown promise, but their high computational cost—often requiring hundreds of denoising steps—hinders practical deployment. To overcome these limitations, this paper proposes an FDDA that directly learns the residuals between noise samples at adjacent time steps, thereby eliminating the need for larger attention mechanism modules. This design significantly reduces the number of diffusion steps by more than 60% compared to standard DDPMs—without compromising sample quality. Combined with support vector machine(SVM) and random forest(RF) classifiers, our approach is evaluated on multiple benchmark power system cases. Results demonstrate improved generalization, robustness to class imbalance, and lower computational cost compared to conventional augmentation techniques, thus providing a scalable and efficient data-driven strategy for enhancing power system stability assessment under high uncertainty.

间歇性可再生能源的不断增加增加了电力系统运行的不确定性，使稳定性评估复杂化，限制了传统的基于模型的方法的有效性。为了应对这一挑战，数据驱动的方法作为灵活的替代方案出现了，但它们经常遭受严重的类不平衡，不稳定的情况相对较少。生成对抗模型（gan）和变分自编码器（VAEs）等生成模型已经被用于样本扩增，但经常遇到模式崩溃和多样性不足等问题。最近，去噪扩散概率模型（ddpm）显示出了希望，但其高昂的计算成本——通常需要数百个去噪步骤——阻碍了实际部署。为了克服这些限制，本文提出了一种直接学习相邻时间步噪声样本间残差的FDDA，从而消除了对更大的注意机制模块的需求。与标准ddpm相比，该设计显着减少了60%以上的扩散步骤，而不影响样品质量。结合支持向量机（SVM）和随机森林（RF）分类器，我们的方法在多个基准电力系统案例上进行了评估。结果表明，与传统的增强技术相比，该方法具有更好的泛化能力、对类不平衡的鲁棒性和更低的计算成本，从而为提高高不确定性下的电力系统稳定性评估提供了一种可扩展和高效的数据驱动策略。

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引用次数: 0

Blockchain-based energy conservation mechanism in smart grids using adaptive weighted average consensus 基于自适应加权平均共识的区块链智能电网节能机制

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-01-21 DOI: 10.1016/j.ijepes.2026.111605

Wei Xiao , Shuang Duan , Xiaohan Ren , Qiongyan Fang , Wangyan Li , Fuwen Yang

Smart grids have been widely studied for their ability to improve efficiency and reliability, with prior research focusing on demand response, secure data sharing, and distributed optimization. However, existing approaches often address privacy protection and energy efficiency separately, leaving a gap in simultaneously achieving both within a scalable and incentive-compatible framework. To address this challenge, this work proposes a blockchain-based energy conservation mechanism that integrates an adaptive weighted average consensus scheme, optimized via Non-Dominated Sorting Genetic Algorithm II, with a dynamic incentive contract supported by cryptocurrency rewards. The mechanism allows prosumers to validate transactions by exchanging only the percentage of power change, thereby preserving privacy, while the incentive design motivates active participation in energy scheduling. Simulation results show that the proposed approach reduces average daily energy consumption per prosumer by 7.5 kWh (30-node case) and 8.8 kWh (300-node case), and decreases 24-hour weighted electricity costs by up to 8.66%. These findings highlight the effectiveness of the mechanism in achieving measurable energy and cost savings.

智能电网因其提高效率和可靠性的能力而受到广泛研究，先前的研究主要集中在需求响应、安全数据共享和分布式优化方面。然而，现有的方法往往分别解决隐私保护和能源效率问题，在可扩展和激励兼容的框架内同时实现这两个目标方面存在差距。为了应对这一挑战，本工作提出了一种基于区块链的节能机制，该机制集成了一种自适应加权平均共识方案，该方案通过非支配排序遗传算法II进行优化，并采用加密货币奖励支持的动态激励合约。该机制允许产消者仅通过交换电力变化的百分比来验证交易，从而保护隐私，而激励设计则激励积极参与能源调度。仿真结果表明，该方法可使产消者日平均能耗分别降低7.5 kWh（30节点情况）和8.8 kWh（300节点情况），24小时加权电力成本降低高达8.66%。这些发现突出了该机制在实现可衡量的能源和成本节约方面的有效性。

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引用次数: 0

Optimal scheduling of virtual power plant for short term operating reserve considering EV battery swapping 考虑电动汽车电池交换的短期运行储备虚拟电厂优化调度

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-01-21 DOI: 10.1016/j.ijepes.2026.111598

Tianlu Gao , Xiao Wang , Jing Zhang , Jun Zhang , Alessandra Parisio

Virtual power plants aggregate flexible resources for market participations. This plays an important role for the secure operations of power networks with high renewable penetrations. The paper proposes a two-stage robust optimization model for the VPP considering the battery swapping service for electric vehicles. The swapping service is conducted in a distributed manner through a central battery charging station and multiple battery distribution stations, which are connected through the transportation truck. The VPP is operated to participate in the energy and reserve markets. The technical requirements of reserve calling are modeled considering the secondary response capability for the short term operating reserve (STOR) as defined by the Britain grid. The nested column and constraint generation (NCCG) algorithm is used to solve the model considering the binary variables in the second stage. Simulation with two study cases demonstrate the effectiveness of the proposed approach. It indicates that the VPP provides reserve capacity fulfilling arbitrary reserve usage, and the VPP rewards is decreased with the increased level of uncertainties.

虚拟电厂为市场参与聚集了灵活的资源。这对可再生能源高渗透率电网的安全运行具有重要意义。提出了考虑电动汽车换电池服务的VPP两阶段鲁棒优化模型。交换服务通过中央电池充电站和多个电池配电站以分布式方式进行，所述中央电池充电站和多个电池配电站通过运输卡车连接。VPP的运作是为了参与能源和储备市场。考虑英国电网短期运行储备（STOR）的二次响应能力，对备用呼叫的技术要求进行了建模。第二阶段采用嵌套列约束生成（NCCG）算法求解二元变量模型。通过两个研究实例的仿真验证了该方法的有效性。结果表明，VPP提供了满足任意储备使用的储备容量，VPP奖励随着不确定性水平的增加而降低。

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引用次数: 0

A multi-scale spatiotemporal spiking neural model for power load forecasting considering extreme weather impact 考虑极端天气影响的多尺度时空尖峰神经网络负荷预测模型

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-01-21 DOI: 10.1016/j.ijepes.2026.111604

Yuanshuo Guo , Jun Wang , Hong Peng , Tao Wang , Hongping Hu , Antonio Ramírez-de-Arellano

The increasing frequency of extreme weather events has brought about significant mutation in the distribution characteristics of power load, while traditional models are unable to handle such sudden changes in load and adequately characterize the coupling effects across various scales. To address this problem, this study proposes a bidirectional nonlinear spiking neural P (NSNP) model with weather-aware multi-scale fusion, which represents an enhanced NSNP framework that integrates multi-scale adaptive feature extraction network (MAFEN) and multiple encoders based on bidirectional NSNP (BiNSNP) variants, termed multi-scale spatiotemporal BiNSNP attention fusion network (MSBAF-Net). Inspired by nonlinear spiking mechanisms, this architecture captures complex nonlinear load dynamics. Moreover, this multi-source data parallel fusion network effectively achieves dynamic weighting of features across both spatial and temporal dimensions, thereby capturing local patterns at critical time steps in load sequences and cross-channel feature correlations under extreme weather. Specifically, MSBAF-Net performs channel separation, isolating the abrupt components of the load into the residual channel. Based on the characteristics of different channels, MSBAF-Net incorporates a targeted bidirectional modeling strategy alongside differentiated feature extraction pathways, implemented through two lightweight NSNP-like convolutional models. Additionally, feature fusion network (FFN) maintains the interaction of multi-scale load features in time and space. Finally, comparison study using three real-world datasets and 25 baseline prediction models is performed. Experimental results demonstrate that MSBAF-Net achieves the best comprehensive performance across all extreme weather scenarios. Notably, under the low-temperature cold wave scenario, MSBAF-Net achieves average forecasting accuracies of 97.51% and 97.38% for Lines 1–10 at the power station A and Lines 1–7 at the power station B, respectively. Our codes and datasets have been released at https://github.com/hssinne/MSBAF-Net.

随着极端天气事件的频繁发生，电力负荷的分布特征发生了显著的突变，传统的模型无法处理这种负荷的突变，也无法充分表征各尺度的耦合效应。为了解决这一问题，本研究提出了一种具有天气感知多尺度融合的双向非线性峰值神经P （NSNP）模型，该模型代表了一种增强的NSNP框架，该框架集成了多尺度自适应特征提取网络（MAFEN）和基于双向NSNP （BiNSNP）变体的多个编码器，称为多尺度时空BiNSNP注意力融合网络（MSBAF-Net）。受非线性尖峰机制的启发，该架构捕捉了复杂的非线性负载动态。此外，该多源数据并行融合网络有效地实现了跨时空特征的动态加权，从而捕获负载序列中关键时间步长的局部模式和极端天气下跨通道特征相关性。具体来说，MSBAF-Net执行通道分离，将负载的突然分量隔离到剩余通道中。基于不同通道的特征，MSBAF-Net结合了有针对性的双向建模策略以及差异化的特征提取路径，通过两个轻量级的类似nsnp的卷积模型实现。此外，特征融合网络（FFN）保持了多尺度载荷特征在时间和空间上的相互作用。最后，利用3个真实数据集和25个基线预测模型进行了比较研究。实验结果表明，MSBAF-Net在所有极端天气情景下都具有最佳的综合性能。值得注意的是，在低温寒潮情景下，MSBAF-Net对A电站1-10号线和B电站1-7号线的平均预报准确率分别为97.51%和97.38%。我们的代码和数据集已在https://github.com/hssinne/MSBAF-Net上发布。

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引用次数: 0

Flexible DC transmission lines protection method based on StarNet 基于StarNet的柔性直流输电线路保护方法

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-01-21 DOI: 10.1016/j.ijepes.2026.111580

Yanfang Wei , Junjiang Wang , Zhanye Yang , Zhihui Zeng , Xiaowei Wang , Jiayin Li

To address the challenges of threshold selection in existing fault diagnosis methods for flexible DC transmission lines, this study proposes a fault identification approach integrating gramian angular field (GAF) and StarNet-based analysis. The methodology initiates by transforming sequential voltage data under diverse fault conditions into two-dimensional voltage GAF images through GAF-based encoding. These generated voltage GAF images are subsequently partitioned into training and testing datasets to facilitate the training and validation processes of the StarNet. During model development, the network architecture is optimized through partial convolutional layers, while convergence efficiency is enhanced via a cosine annealing algorithm. Comprehensive simulations evaluating transition resistance, noise interference, sampling frequency, and lightning strike disturbances demonstrate the proposed method’s adaptability and superior diagnostic performance across multiple operational scenarios.

针对现有柔性直流输电线路故障诊断方法中阈值选择困难的问题，提出了一种结合格拉曼角场（GAF）和基于starnet分析的故障识别方法。该方法首先通过基于GAF的编码将不同故障条件下的时序电压数据转换为二维电压GAF图像。这些生成的电压GAF图像随后被划分为训练和测试数据集，以促进StarNet的训练和验证过程。在模型开发过程中，通过部分卷积层优化网络结构，同时通过余弦退火算法提高收敛效率。综合模拟评估了过渡电阻、噪声干扰、采样频率和雷击干扰，证明了该方法在多种作战场景下的适应性和卓越的诊断性能。

引用次数: 0