International Journal of Electrical Power & Energy Systems最新文献

A distributed charging pricing strategy for highway microgrid considering carbon trading: A Stackelberg evolutionary joint game approach 考虑碳交易的高速公路微电网分布式充电定价策略：一个Stackelberg进化联合博弈方法

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

International Journal of Electrical Power & Energy Systems

Pub Date : 2025-12-13 DOI: 10.1016/j.ijepes.2025.111450

Chongtao Bai , Suhua Lou , Wenrui Zhang , Dan Yang , Tianmeng Yang , Zheng Lin , Xinrui Xiong

With the rapid growth in electric vehicle (EV) adoption, highway charging stations (HCSs) composed of photovoltaic generation, energy storage systems, and charging piles have become an effective solution to alleviate range anxiety for long-distance EV travel and reduce carbon emissions in the transportation sector. This study investigates how highway microgrid operator (HMGO) can design distributed pricing strategies for multiple HCSs along highways to maximize their overall profit in the coupled electricity-carbon market. A Stackelberg-evolutionary joint game model is proposed. In the upper Stackelberg game, the interaction between the leader (HMGO) and followers (EVs) is modeled through electricity price and charging demand decisions. In the lower evolutionary game, the charging behaviors among EV users are modeled considering their bounded rationality in decision-making. The proposed approach quantifies the carbon reduction contributions of both HCSs and EVs into tradable carbon emission allowances, enabling their participation in the carbon market and guiding optimal charging price strategies. Case studies demonstrate that: (1) Compared with unified pricing and electricity-market-only pricing, the proposed pricing strategy increases the total profit of HMGO by 3.1% and 26.2%, respectively, while reducing the PV curtailment rate by 2.22% and 3.52%. (2) Carbon trading further enables the HMGO to lower the average EV charging price by 12.3%, thereby attracting additional charging demand and enhancing carbon trading profit. (3) The carbon trading price and the penetration rate of home charging piles significantly affect the optimal pricing and profitability, underscoring their importance in highway microgrid pricing and operation.

随着电动汽车的快速发展，由光伏发电、储能系统和充电桩组成的高速公路充电站已经成为缓解电动汽车长途行驶里程焦虑和减少交通领域碳排放的有效解决方案。本研究探讨高速公路微电网运营商（HMGO）如何为高速公路沿线的多个微电网设计分布式定价策略，以最大化其在电力-碳耦合市场中的整体利润。提出了一个Stackelberg-evolutionary联合博弈模型。在上层Stackelberg博弈中，领导者（HMGO）和追随者（ev）之间的互动通过电价和充电需求决策来建模。在低进化博弈中，考虑用户决策的有限理性，对电动汽车用户的充电行为进行建模。该方法将hcs和电动汽车的碳减排贡献量化为可交易的碳排放配额，使其能够参与碳市场并指导最优充电价格策略。案例研究表明：(1)与统一定价和市场化定价相比，所提出的定价策略使HMGO的总利润分别提高3.1%和26.2%，同时使光伏弃风率降低2.22%和3.52%。(2)碳交易进一步使HMGO降低了12.3%的电动汽车平均充电价格，从而吸引了额外的充电需求，提高了碳交易利润。(3)碳交易价格和家庭充电桩普及率对高速公路微网最优定价和盈利能力有显著影响，在高速公路微网定价和运营中具有重要意义。

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引用次数: 0

Coordinated Source-Grid fault current limiting strategy in HVDC grids based on fault severity and quantitative limitation demand 基于故障严重程度和定量限制需求的高压直流电网协调源网故障限流策略

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

International Journal of Electrical Power & Energy Systems

Pub Date : 2025-12-13 DOI: 10.1016/j.ijepes.2025.111471

Yuhong Wang , Chunsheng Guo , Ting Du , Jianquan Liao , Nengqiao Wei , Dachuan Yu

Reliable fault current limitation is critical in high-voltage direct current (HVDC) grids due to the fast dynamics and low fault tolerance of power electronic equipment, as well as the high cost of DC circuit breakers (DCCBs). To address these challenges, this paper proposes a coordinated source-grid fault current limiting strategy based on fault severity assessment and quantitative measurement of current limitation demand. Firstly, an artificial neural network (ANN)-based model is developed to predict 1-ms fault currents in real time, enabling rapid classification of fault severity within 1 ms. Secondly, a dual-metric approach is introduced to quantify the current limiting requirements from both the current amplitude and energy perspectives, building a modulation coefficient controller for adaptive source-side suppression. Thirdly, an adaptive grid-side fault current limiter (FCL) operation logic is formulated, where the engagement timing and coordination with the source side are optimized according to the assessed fault severity. Simulation and experimental results demonstrate that the proposed strategy reduces DCCB breaking current by over 44 % and metal-oxide arrester (MOA) energy absorption by more than 75 %, compared with conventional approaches. The method enhances system protection, improves coordination between devices, and is particularly suited to large-scale renewable-integrated HVDC networks.

由于电力电子设备的快速动态和低容错性，以及直流断路器（dccb）的高成本，在高压直流（HVDC）电网中可靠的故障电流限制至关重要。针对这些挑战，本文提出了一种基于故障严重程度评估和限流需求定量测量的协同源网故障限流策略。首先，建立了基于人工神经网络（ANN）的故障电流实时预测模型，实现了1 ms内故障严重程度的快速分类；其次，引入双度量方法，从电流幅度和能量两个角度量化电流限制要求，构建自适应源侧抑制的调制系数控制器。第三，构建了自适应电网侧故障限流器运行逻辑，根据评估的故障严重程度优化与源侧的啮合时机和协调；仿真和实验结果表明，与传统方法相比，该方法可使DCCB断流降低44%以上，金属氧化物避雷器（MOA）能量吸收降低75%以上。该方法增强了系统保护，改善了设备之间的协调，特别适用于大型可再生集成高压直流电网。

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引用次数: 0

Optimum control for UPQC-embedded source-network-load-storage system using extreme learning machine scheme 应用极限学习机方案实现upqc嵌入式源-网络-负载-存储系统的最优控制

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

International Journal of Electrical Power & Energy Systems

Pub Date : 2025-12-13 DOI: 10.1016/j.ijepes.2025.111462

Bo Gao, Yuchen Zhang, Jian Han, Zewen Li, Fangming Deng

This study presents an optimum control scheme for the unified power quality conditioner (UPQC)-embedded source-network-load-storage system (SNLSS) to improve the penetration of renewable energy, power quality (PQ) and system utilization rate simultaneously. In the proposed optimum control scheme, four optimization objectives are considered and they include: 1) maximizing the output power of PV arrays to improve the related penetration rate; 2) minimizing the load voltage deviation to improve the voltage quality; 3) maximizing the power factor of the power grid the improve the current quality; and 4) maximizing the utilization rate of proposed system to make full use of the UPQC. By adopting the hierarchical optimization idea, the multi-objective optimization problem is converted into a few single-objective ones. Then, all optimum solutions are solved and trained as an ELM surrogate model, which realizes the quick and precise implementation of optimum control. Time-domain simulation results demonstrate the usefulness of the proposed optimum control scheme.

提出了一种统一电能质量调节器(UPQC)-嵌入式源网负荷存储系统（SNLSS）的优化控制方案，以同时提高可再生能源的渗透率、电能质量（PQ）和系统利用率。在该优化控制方案中，考虑了四个优化目标：1)最大化光伏阵列的输出功率以提高相关渗透率；2)减小负载电压偏差，提高电压质量；3)最大限度地提高电网的功率因数，改善电流质量；4)最大化系统利用率，充分利用UPQC。采用层次优化思想，将多目标优化问题转化为多个单目标优化问题。然后，对所有最优解进行求解并训练成ELM代理模型，实现了最优控制的快速精确实施。时域仿真结果验证了所提最优控制方案的有效性。

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引用次数: 0

Multi-Agent Deep reinforcement learning for EV aggregator bidding in Renewable-Dominated electricity markets 基于多智能体深度强化学习的可再生电力市场电动汽车聚合器竞价

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

International Journal of Electrical Power & Energy Systems

Pub Date : 2025-12-12 DOI: 10.1016/j.ijepes.2025.111444

Yuanshi Zhang , Lingchi Meng , Antonio Carlos Zambroni , Qinran Hu , Haizhou Liu , Andrey Lebedev , Amin Mohammadpour Shotorbani

The increasing penetration of renewable energy into electricity markets has introduced significant volatility and uncertainty, presenting new challenges for flexible demand-side resources such as Electric Vehicle Aggregators (EVAs). However, existing approaches to EVA bidding strategies often relied on static pricing forecasts or single-agent optimization frameworks, which failed to capture the dynamic, multi-agent nature of modern electricity markets. To address these limitations, this paper proposes an intelligent bidding strategy framework based on Multi-Agent Deep Reinforcement Learning tailored for heavy-duty EV fleets engaged in freight operations. A multi-agent game-theoretic market model is constructed, incorporating renewable generators, traditional suppliers, industrial and commercial users, and EVAs to replicate real-time interactions within spot markets. A joint optimization strategy is developed to align vehicle scheduling constraints with market participation, while the Multi-agent Deep Deterministic Policy Gradient algorithm is employed to enable adaptive, decentralized learning of optimal bidding behaviors. Simulation results demonstrate that the proposed framework effectively reduces peak loads, smooths load profiles, and enhances EVA revenues under volatile market conditions. The findings offer actionable insights into integrating flexible electric mobility into future renewable-dominated power systems.

可再生能源在电力市场的不断渗透带来了巨大的波动性和不确定性，给灵活的需求侧资源（如电动汽车聚合器（ev））带来了新的挑战。然而，现有的EVA投标策略方法往往依赖于静态定价预测或单代理优化框架，无法捕捉现代电力市场的动态、多代理特性。为了解决这些限制，本文提出了一种基于多智能体深度强化学习的智能竞标策略框架，为从事货运业务的重型电动汽车车队量身定制。构建了一个多智能体博弈论市场模型，将可再生能源发电商、传统供应商、工业和商业用户以及EVAs纳入其中，以复制现货市场中的实时交互。开发了一种联合优化策略，使车辆调度约束与市场参与保持一致，而采用多智能体深度确定性策略梯度算法来实现最优竞标行为的自适应分散学习。仿真结果表明，该框架有效地降低了峰值负荷，平滑了负荷分布，并在波动的市场条件下提高了EVA收益。研究结果为将灵活的电动汽车整合到未来以可再生能源为主导的电力系统中提供了可行的见解。

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引用次数: 0

Inverse optimization and robust aggregation based bidding strategy for distributed energy resource aggregators using multi-agent reinforcement learning 基于多智能体强化学习的分布式能源聚合器逆优化和鲁棒聚合竞价策略

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

International Journal of Electrical Power & Energy Systems

Pub Date : 2025-12-12 DOI: 10.1016/j.ijepes.2025.111468

Ke Zhang , Xu Wang , Mohammad Shahidehpour , Chuanwen Jiang , Hongkun Yang , Zhaohao Ding , Zhengmao Li

Distributed energy resource aggregators are increasingly influential in electricity markets, yet bidding approaches based solely on machine learning or robust optimization often struggle with limited data, privacy concerns, and operation feasibility. We address these challenges with two physical-driven models that quantify an aggregator’s economic and technical characteristics through economic parameter surrogation and flexibility aggregation. First, a parametric inverse optimization model jointly learns the cost and constraint coefficients of responsive loads from historical observations, yielding reliable economic surrogates without restrictive assumptions. Second, a flexibility aggregation model constructs a feasible operation region that ensures both aggregation optimality and disaggregation feasibility. These models are embedded in a multi-agent bidding method so that learned policies respect identified costs, feasible flexibility, and robustness to uncertainty while relying only on aggregate information for privacy protection. Tests on the IEEE 118-bus system show that the proposed hybrid approach achieves a better fit to observed responses, maintains feasibility under uncertainty, and secures consistently higher revenues, while reducing dependence on large training datasets. The results highlight a practical path to reliable, privacy-aware bidding for aggregators in competitive markets.

分布式能源聚合器在电力市场上的影响力越来越大，然而，仅仅基于机器学习或鲁棒优化的投标方法往往会受到有限的数据、隐私问题和操作可行性的困扰。我们通过两个物理驱动模型来解决这些挑战，这些模型通过经济参数替代和灵活性聚合来量化聚合器的经济和技术特征。首先，参数逆优化模型从历史观测中共同学习响应负荷的成本和约束系数，在没有限制性假设的情况下产生可靠的经济替代。其次，利用柔性聚合模型构建可行操作区域，保证聚合最优性和分解可行性。这些模型被嵌入到一个多智能体投标方法中，这样学习到的策略就能尊重已识别的成本、可行的灵活性和对不确定性的鲁棒性，而只依赖于汇总信息来保护隐私。在IEEE 118总线系统上的测试表明，所提出的混合方法能够更好地拟合观察到的响应，在不确定的情况下保持可行性，并确保持续较高的收益，同时减少对大型训练数据集的依赖。研究结果强调了在竞争激烈的市场中，为聚合器提供可靠、有隐私意识的竞标的可行途径。

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引用次数: 0

Data-driven emission analysis of on-board chargers and large AC charging sites of electric vehicles 电动汽车车载充电器及大型交流充电点数据驱动排放分析

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

International Journal of Electrical Power & Energy Systems

Pub Date : 2025-12-12 DOI: 10.1016/j.ijepes.2025.111457

Antti Hildén, Toni Simolin, Mehdi Attar, Pertti Pakonen

Expansion of electric vehicles (EVs) is rapid, and it has led to the introduction of numerous EV models. The harmonic current emissions of the on-board chargers of EVs are analyzed in this article based on a large collection of measurement data from public charging sites. The objectives are to discover the emissions of the variety of EV models and to estimate their aggregated emissions in future scenarios. This article utilizes the measurement data of 109 EVs at rated power and 27 below rated power. The recent development of aggregated emissions is observed with a four-year measurement data of a 40-spot charging site. Aggregated emissions in the future are simulated in scenarios of the years 2030 and 2040. In the results, many EVs can be divided into groups based on the emissions. The emissions increase the steepest when below 10 A charging current. The highest harmonics are the 5th, 7th and 11th order, yet compared to the standards, the problematic harmonics are the 11th–19th order. The future scenario simulations indicate no problems with emissions. The major harmonics are the 3rd, 5th and 7th order and the years 2030 and 2040 have similar results. The simulation studies provide charging site-specific summation coefficients for the harmonics. This article contributes to the development of emission standards and methods to control and monitor the emissions of EV charging sites and gives guidance to estimate the emissions of a charging site for stakeholders involved in charging site design and operation.

电动汽车（EV）的扩张迅速，并导致了众多电动汽车车型的推出。本文基于大量公共充电站点的测量数据，对电动汽车车载充电器的谐波电流排放进行了分析。目标是发现各种电动汽车模型的排放量，并估计它们在未来情景下的总排放量。本文利用109辆电动汽车在额定功率下和27辆低于额定功率下的测量数据。最近总排放量的发展是用一个40个充电点的四年测量数据来观察的。在2030年和2040年的情景中模拟了未来的总排放量。结果显示，许多电动汽车可以根据排放量进行分组。当充电电流低于10 A时，排放增加最快。最高的谐波是第5阶、第7阶和第11阶，但与标准相比，有问题的谐波是第11 - 19阶。未来情景模拟表明，排放没有问题。主要谐波为三阶、五阶和七阶，2030年和2040年的结果相似。仿真研究提供了充电点特定的谐波求和系数。本文有助于制定控制和监测电动汽车充电站排放的排放标准和方法，并为参与充电站设计和运营的利益相关者提供对充电站排放估算的指导。

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引用次数: 0

Method for identifying high-resistance grounding fault section in single-neutral-point low-resistance grounding active distribution networks 单中性点低阻接地有功配电网高阻接地故障段识别方法

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

International Journal of Electrical Power & Energy Systems

Pub Date : 2025-12-12 DOI: 10.1016/j.ijepes.2025.111464

Jing Zhang, Botong Li, Fahui Chen, Bin Li

The paper focuses on single-neutral-point low-resistance grounding (SNP-LRG) active distribution networks (ADNs) where the distribution system adopts the low-resistance grounding mode, and distributed generation (DG) grid-connected transformers adopt the ungrounded mode. The study analyzes the characteristic relationship between zero-sequence voltage and current at switches on the main line and branch lines during positive and negative direction single-phase ground faults. Based on this analysis, a directional discrimination principle for single-phase grounding faults using direction-indicating voltage (DIV) and a corresponding fault section identification method is proposed. The impact of transition resistance (TR) and DG capacity on the proposed method is analyzed, and a calculation method for the maximum allowable measurement error boundaries (MAMEBs) under different TRs is proposed. Simulation results demonstrate that the proposed method can achieve accurate and reliable section-level ground fault identification even with a TR of 3000 Ω, unaffected by arc discharge phenomena during ground faults or the integration of DG. Compared to the widely proposed phase-based detection methods for SPGFs, the proposed method maintains accurate judgment at a TR of 3000 Ω while significantly enhancing adaptability to grid topology changes and DG integration. Furthermore, this study quantifies the MAMEBs for measurement equipment, providing clear guidance for the selection and evaluation of measurement devices in engineering applications. Simulations confirm that the proposed method ensures accurate fault section identification at a TR of 3000 Ω when considering a measurement absolute error of 0.0244 kV.

本文研究的是单中性点低阻接地（SNP-LRG）有源配电网（ADNs），其中配电系统采用低阻接地方式，分布式发电并网变压器采用不接地方式。研究了正负向单相接地故障时主支路开关零序电压和电流的特征关系。在此基础上，提出了用指示电压（DIV）进行单相接地故障的定向判别原理和相应的故障区段识别方法。分析了过渡电阻（TR）和DG容量对方法的影响，提出了不同过渡电阻下最大允许测量误差边界（MAMEBs）的计算方法。仿真结果表明，在TR值为3000 Ω的情况下，该方法能够在不受接地故障时电弧放电现象或DG集成影响的情况下，实现准确可靠的分段接地故障识别。与目前广泛提出的基于相位的spgf检测方法相比，该方法在TR为3000 Ω的情况下保持了准确的判断，同时显著增强了对网格拓扑变化和DG集成的适应性。此外，本研究还量化了测量设备的mameb，为工程应用中测量设备的选择和评价提供了明确的指导。仿真结果表明，在测量绝对误差为0.0244 kV的情况下，该方法能够在TR为3000 Ω的情况下准确识别出故障剖面。

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引用次数: 0

High-impedance fault location in distribution networks using randomness of power-frequency voltage signal: A transfer divergence difference approach 基于工频电压信号随机性的配电网高阻抗故障定位：一种传输发散差分方法

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

International Journal of Electrical Power & Energy Systems

Pub Date : 2025-12-11 DOI: 10.1016/j.ijepes.2025.111430

Yanyong Gong , Yang Liu , Jing Li , Zhiwei Yao , Zonghong Han

When high-impedance faults (HIFs) occur in a distribution network, the weak fault voltage is easily obscured by operational and measurement noise. Aimed to HIFs location problem, this paper presents a novel location method with tow stage based on probabilistic distribution feature. At the first stage, the transfer divergence is proposed to quantify the probability distribution change of power-frequency voltage before and during the fault. At the second stage, the transfer divergence difference is proposed to catch probabilistic distribution difference between adjacent monitoring regions to identify the fault section. Numerical simulations and field tests demonstrate that the proposed method is robust to noise, even without anti-noise technology. It can accurately and promptly identify and locate faults, even in scenarios with a high proportion of distributed generation and multiple distribution network operations.

当配电网发生高阻抗故障时，微弱的故障电压容易被运行噪声和测量噪声所掩盖。针对hfs的定位问题，提出了一种基于概率分布特征的双阶段定位方法。首先，提出了传递散度来量化故障前和故障中工频电压的概率分布变化；在第二阶段，提出了传递散度差来捕捉相邻监测区域之间的概率分布差，从而识别断层段。数值模拟和现场试验结果表明，即使不采用抗噪声技术，该方法也具有较强的抗噪声能力。即使在分布式发电占比高、配电网运行多的场景下，也能准确、快速地识别和定位故障。

引用次数: 0

Relation between hosting capacity of MV and LV networks and a joint hosting capacity assessment 中、低压网络承载能力关系及联合承载能力评估

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

International Journal of Electrical Power & Energy Systems

Pub Date : 2025-12-11 DOI: 10.1016/j.ijepes.2025.111448

Taís Tavares de Oliveira, Math H.J. Bollen

Solar PV connections at medium voltage (MV) level can impact low voltage (LV) networks by reducing their margin for installing additional PV units. This paper presents an approach to estimate the hosting capacity of distribution networks, including an integrated model of MV-LV networks and a visualisation method for assessing the sharing of hosting capacity between voltage levels. The objective is to quantify the mutual influence of MV and LV connections and to support the planning and management of connection requests. The proposed visualisation method illustrates the trade-off between MV and LV production units, accounting for both voltage and loading limitations, and provides a good representation of how PV connections at one voltage level affect the other. Results show that the coupling between MV and LV networks significantly impacts the hosting capacity, how it can be shared, and that constraints at either level can limit future connections. The method facilitates the identification of the voltage level imposing the most significant limitations and thus also supports the assessment of where and which mitigation strategies would be more effective. Overall, the method highlights the importance of a joint assessment considering the MV-LV coupling to support informed planning decisions.

中压（MV）水平的太阳能光伏连接可以通过减少安装额外光伏单元的余量来影响低压（LV）网络。本文提出了一种估计配电网承载能力的方法，包括一个中低压电网的综合模型和一种评估电压水平之间承载能力共享的可视化方法。目标是量化中压和低压连接的相互影响，并支持连接请求的规划和管理。提出的可视化方法说明了中压和低压生产单元之间的权衡，考虑了电压和负载限制，并提供了一个电压水平下PV连接如何影响另一个电压水平的良好表示。结果表明，中低网络之间的耦合显著影响了托管容量及其共享方式，并且任何级别的约束都可能限制未来的连接。该方法有助于确定施加最大限制的电压等级，从而也有助于评估在哪些地方和哪种缓解战略将更为有效。总的来说，该方法强调了考虑MV-LV耦合的联合评估的重要性，以支持明智的规划决策。

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引用次数: 0

An online adaptive evolution method for traction transformer temperature estimation model based on multi-scale periodic updates 基于多尺度周期更新的牵引变压器温度估计模型在线自适应进化方法

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

International Journal of Electrical Power & Energy Systems

Pub Date : 2025-12-11 DOI: 10.1016/j.ijepes.2025.111442

Renyu Wang , Gang Zhang , Jingjian Yang , Zhaofeng Gong , Ming Gao

Accurate estimation of traction transformer winding temperature is critical for assessing operational status and ensuring the safety and reliability of urban rail system. However, conventional offline models, trained on historical data, exhibit limited adaptability to evolving system conditions, leading to accumulated estimation errors and diminished long-term usability. Therefore, we propose an online adaptive evolution framework for transformer temperature estimation, incorporating a multi-scale periodic update mechanism. Specifically, a two-stage progressive update strategy captures temperature variations across multiple temporal scales, maintaining accuracy under varying operational conditions. On one hand, a transfer learning-based feature calibration method is proposed, partially freezing model parameters. It enables rapid adaptation to short-term load fluctuations. On the other hand, to preserve long-term knowledge integrity, a self-distillation strategy is introduced. Through self-distillation of historical model, thermodynamic consistency preservation with adaptability to scheduling changes and equipment aging can be balanced. Experimental results demonstrate that the proposed method achieves a computational accuracy exceeding 98%, while improving computational efficiency by 66.89% compared to full model retraining. Overall, the approach significantly enhances the long-term usability and adaptability of transformer temperature models, providing strong support for deployment in urban rail transit systems.

牵引变压器绕组温度的准确估算对于评估城市轨道交通的运行状态和确保轨道交通的安全可靠至关重要。然而，传统的离线模型，在历史数据上训练，对不断变化的系统条件表现出有限的适应性，导致累积的估计误差和降低的长期可用性。因此，我们提出了一个包含多尺度周期更新机制的变压器温度估计在线自适应演化框架。具体来说，两阶段渐进更新策略捕获多个时间尺度的温度变化，在不同的操作条件下保持准确性。一方面，提出了一种基于迁移学习的特征标定方法，部分冻结模型参数；它能够快速适应短期负荷波动。另一方面，为了长期保持知识的完整性，引入了一种自蒸馏策略。通过历史模型的自精馏，实现了对调度变化适应性和设备老化适应性的热力学一致性保持的平衡。实验结果表明，与全模型再训练相比，该方法的计算准确率超过98%，计算效率提高66.89%。总体而言，该方法显著增强了变压器温度模型的长期可用性和适应性，为在城市轨道交通系统中的部署提供了有力支持。

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引用次数: 0