Electric Power Systems Research最新文献_第7页

Study on surface hydrophobicity of silicone rubber insulator with grating structure based on nanosecond pulse laser processing 基于纳秒脉冲激光加工的光栅结构硅橡胶绝缘子表面疏水性研究

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-01-09 DOI: 10.1016/j.epsr.2026.112710

Yuyao Hu , Yulin Wang , Fangbin Liu , Xingliang Jiang , Wentao Jia , Qiang Zhou , Hui Liu

As the insulation equipment of the power grid, the original contact angle of the silicone rubber of the main material of the composite insulator is about 110°, which does not meet the superhydrophobic standard. To improve the hydrophobicity, a biomimetic superhydrophobic structure, namely the grating micro/nano composite structure of rice leaf, was textured on silicone rubber surface using nanosecond pulsed laser, and the influence of laser technological parameters on the grating microstructure and wetting performance was investigated. Moreover, the chemical elements and functional groups on the surface of silicone rubber before and after laser treatment were tested with an energy dispersive spectrometer and Fourier transform infrared spectroscope. The results show that after laser processing, the grating microstructure and micro / nano particles on the surface of silicone rubber increase the average roughness from 0.6537 μm to 2.5191 μm, and the maximum contact angle is 154.25°. The elemental species on the surface of silicone rubber remain unchanged, but the overall content of carbon and oxygen elements increases, while that of silicon element declines. In summary, the grating microstructure prepared by nanosecond laser can effectively improve the hydrophobicity of the silicone rubber surface, thereby improving the performance of the composite insulator.

作为电网的绝缘设备，复合绝缘子的主要材料硅橡胶的原始接触角约为110°，不符合超疏水标准。为了提高硅橡胶的疏水性，利用纳秒脉冲激光在硅橡胶表面织构了一种仿生超疏水结构，即水稻叶片的光栅微纳复合结构，并研究了激光工艺参数对光栅微观结构和润湿性能的影响。利用能量色散光谱仪和傅里叶变换红外光谱仪对激光处理前后硅橡胶表面的化学元素和官能团进行了检测。结果表明：激光加工后，硅橡胶表面的光栅微结构和微纳颗粒使硅橡胶表面的平均粗糙度从0.6537 μm提高到2.5191 μm，最大接触角为154.25°；硅橡胶表面元素种类保持不变，但碳、氧元素总体含量增加，硅元素含量下降。综上所述，纳秒激光制备的光栅微结构可以有效改善硅橡胶表面的疏水性，从而提高复合绝缘子的性能。

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引用次数: 0

SHapley additive exPlanations-based neural architecture search and multi-channel temporal convolutional network with bidirectional gated recurrent unit-based photovoltaic power ultra-short-term fusion prediction with abrupt weather feature enhancement 基于SHapley加性解释的神经结构搜索和多通道时间卷积网络双向门控循环单元光伏超短期融合预测与突发天气特征增强

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-01-09 DOI: 10.1016/j.epsr.2025.112694

Wang Tian , Liu Zhaobin , Sheng Chenxing , Guo Wei

Photovoltaic power forecasting is highly influenced by meteorological conditions.To address the forecasting issue under sudden weather changes,this study proposes a SHAP-NMTCN-BiGRU model for nonlinear power prediction, and applies an error correction model for secondary adjustment of the predicted values.First,use the McClear model to calculate the clear - sky radiation and clear - sky index (k).Then, divide the dataset into sudden and non - sudden weather subsets based on k.Next, employ Random Forest to enhance the sudden weather subset, incorporating k in sub - decision tree division to capture nonlinear features.Furthermore,use SHAP values to optimize the weighted method between models and feature dimensions,improving computational efficiency.To overcome the nonlinear forecasting problem,NAS is used to automatically search for the optimal network structure of the hybrid model.Finally, the error between the predicted and actual power values is calculated,and a Transformer model is used for linear error prediction and secondary correction of the power values.Experimental results show that the RF-enhanced feature method combined with k effectively captures short-term trends.In nonlinear forecasting,the SHAP-NMTCN-BiGRU model demonstrates strong performance and stability.

光伏发电预测受气象条件影响较大。为解决突发天气条件下的电力预测问题，本文提出了一种用于非线性电力预测的SHAP-NMTCN-BiGRU模型，并采用误差修正模型对预测值进行二次调整。首先，利用McClear模型计算晴空辐射和晴空指数(k)。然后，基于k将数据集划分为突发和非突发天气子集，然后利用随机森林对突发天气子集进行增强，将k纳入子决策树划分中以捕获非线性特征。利用SHAP值优化模型与特征维度之间的加权方法，提高计算效率。为了克服非线性预测问题，采用NAS自动搜索混合模型的最优网络结构。最后，计算了预测值与实际功率值之间的误差，并利用变压器模型对功率值进行了线性误差预测和二次校正。实验结果表明，结合k的射频增强特征方法能有效捕捉短期趋势。在非线性预测中，SHAP-NMTCN-BiGRU模型表现出较强的性能和稳定性。

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引用次数: 0

A novel analytical methodology for estimating high-frequency lumped model inductances and series capacitance of transformer winding: an indirect measurement procedure 一种估算变压器绕组高频集总模型电感和串联电容的新分析方法：间接测量程序

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-01-09 DOI: 10.1016/j.epsr.2026.112722

Moustafa Sahnoune Chaouche , Faouzi Didi , Abderrazak Amara , Hamza Houassine , Mohd Fairouz Mohd Yousof , Ahmad F. Tazay , Aymen Flah , Mohamed K. Metwaly , Ramy N.R. Ghaly , Sherif S.M. Ghoneim

In this article, a new analytical method is introduced to effectively estimate the self-inductance, mutual inductances, and series capacitance of transformer windings. The approach uses FR data collected at the winding terminals with the neutral open test. It applies an analytical formula that converts the sum of the inverse squares of both short-circuit and open-circuit natural frequencies, derived from the FR curve, into a polynomial function. These formulas are based on a lumped, mutually coupled equivalent model of the winding, with relationships expressed as a polynomial function connected by a factor relating the inductances, generalized to an N-1 degree for the N-th section of the model. By solving this polynomial, all winding inductance values can be accurately estimated, enabling the determination of the series capacitance. Notably, this method relies solely on measurements of the FR curve, ground capacitance, and equivalent inductance, providing an indirect yet highly efficient way to determine all parameters of the lumped mutually coupled equivalent model. This technique has been rigorously validated through experimental frequency response measurements on two air-core insulated windings, producing remarkably precise results that demonstrate its effectiveness in the field of frequency modeling.

本文介绍了一种新的分析方法，可以有效地估计变压器绕组的自感、互感和串联电容。该方法使用在绕组端子收集的FR数据，并进行中性点开路试验。它应用了一个解析公式，将从FR曲线导出的短路和开路固有频率的反平方之和转换为多项式函数。这些公式是基于一个集总的、相互耦合的绕组等效模型，其关系表示为一个多项式函数，由一个与电感有关的因子连接，在模型的第n部分推广到N-1度。通过求解该多项式，可以准确地估计所有绕组的电感值，从而可以确定串联电容。值得注意的是，该方法仅依赖于FR曲线、接地电容和等效电感的测量，为确定集总相互耦合等效模型的所有参数提供了一种间接但高效的方法。该技术已通过对两个空芯绝缘绕组的实验频率响应测量进行了严格验证，产生了非常精确的结果，证明了其在频率建模领域的有效性。

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引用次数: 0

Series arc fault detection and line selection method based on STD-RLS feature enhancement 基于STD-RLS特征增强的串联电弧故障检测选线方法

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-01-09 DOI: 10.1016/j.epsr.2026.112723

Hao Tian, Limin Hou, Hongxin Gao

The series arc fault (SAF) is one of the main hidden dangers of electrical fires. In household multi-load circuits, the signal features of a faulty branch within the main circuit are easily drowned out by the power frequency and integer harmonic components of normal branches, making it difficult to extract fault features. Therefore, a feature enhancement method based on seasonal trend decomposition and recursive least squares adaptive filtering (STD-RLS) was proposed, and a novel method for the SAF detection and line selection was constructed by combining variational mode decomposition (VMD) and a support vector machine (SVM) recognition model. First, the SAF was simulated in household load circuits under varying numbers of branches, load types, and fault locations. Second, the STD-RLS method was employed to remove power frequency and integer harmonic components from the signal to obtain the fault features. These signals underwent VMD to extract the waveform variability coefficient, time-domain peak, frequency-domain peak, and spectral energy for each intrinsic modal function (IMF) as fault features. Finally, a recognition model based on SVM was established for fault detection and line selection testing. Results show that the proposed method effectively enhances fault features, achieving fault detection and line selection accuracy exceeding 97%.

串联电弧故障是电气火灾的主要隐患之一。在家用多负荷电路中，主电路内故障支路的信号特征容易被正常支路的工频和整数谐波分量淹没，给故障特征提取带来困难。为此，提出了一种基于季节趋势分解和递推最小二乘自适应滤波（STD-RLS）的特征增强方法，并结合变分模态分解（VMD）和支持向量机（SVM）识别模型构建了一种新的SAF检测和选线方法。首先，在不同支路数量、负载类型和故障位置的家庭负载电路中进行了SAF仿真。其次，采用STD-RLS方法去除信号中的工频和整数谐波分量，得到故障特征；对这些信号进行VMD提取波形变异性系数、时域峰值、频域峰值和各内禀模态函数（IMF）的频谱能量作为故障特征。最后，建立了基于支持向量机的故障检测和选线检测识别模型。结果表明，该方法有效增强了故障特征，实现了97%以上的故障检测和选线准确率。

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引用次数: 0

Optimal capacity allocation for wind farm grid-forming units based on Markov transition field and soft actor-critic algorithm 基于马尔可夫过渡场和软行为者评判算法的风电场并网机组最优容量分配

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-01-08 DOI: 10.1016/j.epsr.2025.112689

Xinglong Fang , Yang Hu , Ziqiu Song , Jizhen Liu

With the increasing penetration of renewable energy in power system,grid-forming (GFM) control has become an important method to improve system stability. Nevertheless, the optimal allocation of GFM units that harmonizes dynamic performance with system stability has not yet been adequately addressed. To this end, this paper focuses on the optimization of the proportion of GFM units in wind farms. First, a modeling method based on the Markov transition field-based (MTF) is proposed to represent the dynamic evolution of wind farms under multi-stationary operating conditions. Second, a composite capacity evaluation indicator that jointly accounts for system stability and economic cost is introduced, providing a quantitative basis for assessing the grid-support capability of GFM units and guiding optimal capacity allocation. Finally, the Soft Actor-Critic (SAC) algorithm in reinforcement learning (RL) for capacity allocation is introduced and verified under typical disturbance conditions such as voltage dips and sudden changes in grid strength. Experimental results demonstrate that the proposed method effectively reduces voltage and frequency fluctuations by more than 7% and accelerates system response time, enabling faster frequency recovery and superior voltage regulation.

随着可再生能源在电力系统中的渗透程度不断提高，并网控制已成为提高系统稳定性的重要手段。然而，协调动态性能和系统稳定性的GFM单元的最佳分配尚未得到充分解决。为此，本文重点研究了GFM机组在风电场中的比例优化问题。首先，提出了一种基于马尔可夫过渡场（MTF）的多平稳运行条件下风电场动态演化建模方法。其次，引入了综合考虑系统稳定性和经济成本的综合容量评价指标，为评价GFM机组的并网能力和指导容量优化分配提供了定量依据。最后，介绍了用于容量分配的强化学习（RL）中的软行为者-批评家（SAC）算法，并在电压下降和电网强度突变等典型干扰条件下进行了验证。实验结果表明，该方法有效地降低了电压和频率波动7%以上，加快了系统响应时间，实现了更快的频率恢复和优越的电压调节。

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引用次数: 0

An novel edge-cloud EEMD-OMP algorithm of missing reconstruction for renewable energy power data 一种新的边缘云EEMD-OMP算法用于可再生能源电力数据缺失重建

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-01-08 DOI: 10.1016/j.epsr.2025.112699

Yanfeng Tian, Chaoran Li, Jia Cui, Ximing Zhang, Yonghui Jin, Yuanzhong Li

Renewable energy power data is of great significance for evaluating the development and application of renewable energy power. In order to solve the problem of continuous missing of renewable energy power data (REPD), an EEMD-OMP renewable energy power data missing reconstruction method is proposed based on edge cloud. Firstly, ensemble empirical mode decomposition (EEMD) algorithm is proposed to analyze power signal through noise. The problems of mode aliasing and end effects are reduced by this method. Secondly, the orthogonal matching tracking (OMP) algorithm is introduced to obtain the global optimal solution of the power signal by iterative approximation. This algorithm has better performance and faster convergence for reconstructing missing data. Thirdly, the problem of difficult power data transmission is reduced by incorporating data missing reconstruction into edge computing. Lost data is recovered by historical information of missing data from edge nodes and real-time data from load nodes. Finally, an EEMD-OMP data missing reconstruction method based on edge cloud is proposed. The real renewable energy power data of an industrial park in Liaoning Province is collected. The results obtained from the numerical examples show that this approach outperforms the state-of-the-art in the reconstructing process.data method under different working conditions.

可再生能源电力数据对于评价可再生能源电力的发展和应用具有重要意义。为了解决可再生能源电力数据持续缺失的问题，提出了一种基于边缘云的EEMD-OMP可再生能源电力数据缺失重建方法。首先，提出了集成经验模态分解（EEMD）算法对功率信号进行噪声分析。该方法减少了模态混叠和末端效应问题。其次，引入正交匹配跟踪（OMP）算法，通过迭代逼近得到功率信号的全局最优解；该算法对缺失数据的重构具有更好的性能和更快的收敛速度。第三，在边缘计算中引入数据缺失重构，解决电力数据传输困难的问题。通过边缘节点丢失数据的历史信息和负载节点的实时数据恢复丢失数据。最后，提出了一种基于边缘云的EEMD-OMP数据缺失重建方法。对辽宁省某工业园区的可再生能源电力实际数据进行了采集。数值算例结果表明，该方法在重构过程中优于现有方法。不同工况下的数据方法。

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引用次数: 0

Network-aware coordinated multi-microgrid energy management with carbon emission considerations under uncertainty: a multi-agent double deep Q networks approach 不确定条件下考虑碳排放的网络感知协调多微电网能源管理：多智能体双深度Q网络方法

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-01-03 DOI: 10.1016/j.epsr.2025.112683

Ayodele Benjamin Esan , Hussain Shareef

This paper presents a multi-agent deep reinforcement learning-in-the-loop (DRL-ITL) framework using Double Deep Q-Networks (DDQN) for optimal energy management of multi-microgrids (MMGs) with electricity retailers (ERs) and microgrids (MGs) under renewable and load uncertainty. The framework tackles the complexity of coordinating energy exchanges and market participation under economic and technical constraints. DRL-ITL couples a multi-agent DDQN with a single-level optimization model derived from a bilevel formulation: the upper level maximizes ER profits by increasing MMG sales revenue while minimizing day-ahead purchases and associated carbon costs, and also maximizes available transfer capacity (ATC); the lower level minimizes operating costs. To enforce feasibility, the lower level is recast via Karush–Kuhn–Tucker (KKT) conditions, yielding a tractable mathematical program with equilibrium constraints (MPEC). This hybrid design keeps learning in the loop: DRL agents learn optimal actions for stochastic parameters, while the MPEC guarantees feasibility. We evaluate DRL-ITL on a modified IEEE 14-bus network and benchmark it against two-stage risk-averse stochastic optimization (RASO) with conditional value at risk (CVaR) and a robust optimization (RO) approach. Numerical results show DRL-ITL reduces aggregate MG operating costs to €128,408 (9.01% and 5.28% lower than RASO and RO) and increases total ER profits to €34,466 (10.43% and 15.44% higher), respectively. It further lowers total CO2 emissions to 305.47 kg, representing 16.7% and 11.3% reductions relative to the stochastic and robust baselines. The DRL-ITL framework enhances MG cost efficiency, ER profitability, and decarbonization metrics under uncertainty, outperforming both benchmarks while executing in 0.5 secs at test time.

本文提出了一种基于双深度q网络（DDQN）的多智能体深度强化在环学习（DRL-ITL）框架，用于可再生能源和负荷不确定性下具有电力零售商（er）和微电网（mg）的多微电网（mmg）的最优能量管理。该框架解决了在经济和技术限制下协调能源交换和市场参与的复杂性。DRL-ITL将一个多智能体DDQN与一个由双层公式推导的单级优化模型耦合在一起：上层通过增加MMG销售收入来最大化ER利润，同时最小化前一天的购买和相关的碳成本，并最大化可用转移能力（ATC）；较低的水平可以最大限度地降低运营成本。为了加强可行性，通过Karush-Kuhn-Tucker （KKT）条件对较低的层次进行了重新建模，产生了一个具有平衡约束（MPEC）的易于处理的数学程序。这种混合设计在循环中保持学习：DRL代理学习随机参数的最佳行为，而MPEC保证可行性。我们在改进的IEEE 14总线网络上评估了DRL-ITL，并对具有条件风险值（CVaR）和鲁棒优化（RO）方法的两阶段风险规避随机优化（RASO）进行了基准测试。数值结果表明，DRL-ITL将MG总运营成本降低至128,408欧元（比RASO和RO分别低9.01%和5.28%），将ER总利润提高至34,466欧元（分别高出10.43%和15.44%）。它进一步将二氧化碳排放总量降低到305.47 kg，相对于随机基线和稳健基线分别减少了16.7%和11.3%。DRL-ITL框架提高了不确定性下的MG成本效率、ER盈利能力和脱碳指标，在0.5秒的测试时间内执行，优于两个基准。

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引用次数: 0

Power quality assessment of the distribution system containing passive magnetic fault current limiter under normal and fault conditions 含无源磁故障限流器的配电系统在正常和故障状态下的电能质量评估

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-01-03 DOI: 10.1016/j.epsr.2025.112698

Subhamoy Das , Tapan Santra , Amalendu Bikash Choudhury

This paper investigates power quality impacts of Passive Magnetic Fault Current Limiter (PMFCL) in electrical systems, focusing on harmonic distortion and voltage sag during fault conditions. A laboratory prototype and simulation models are developed to evaluate device performance under both normal and fault scenarios. Fast Fourier Transform (FFT), Continuous Wavelet Transform (CWT), and Discrete Wavelet Packet Transform (DWPT) are employed for harmonic analysis. Under normal operation PMFCL introduces no significant harmonics, with total harmonic distortion (THD) of only 0.11%. During fault, higher-order harmonics (3rd, 5th, 7th) appear, raising THD to 10.11% which has been reduced to 8.1% using the shorting ring, maintaining satisfactory current suppression. 2% reduction of THD is very important in industrial applications. PMFCL also improves voltage sag performance in 11 kV distribution network, the sag depth is reduced from 0.3 pu to 0.65 pu, and transient recovery voltage is also suppressed. A comparative discussion with different types of FCLs highlights novelty and advantages of PMFCL, especially its passive operation and cost-effectiveness. The study concludes that using PMFCL, the harmonic content remains within IEEE 519–2022 and UK G5/3 limits, hence PMFCL offers an effective solution for improving power quality in modern systems. The main contributions of this work are minimization of harmonic distortion using shorting ring on the central limb of PMFCL and reduction of voltage sag by using PMFCL under fault condition.

本文研究了无源磁故障限流器（PMFCL）对电力系统电能质量的影响，重点研究了故障条件下的谐波畸变和电压凹陷。建立了实验室原型和仿真模型，以评估设备在正常和故障情况下的性能。采用快速傅立叶变换（FFT）、连续小波变换（CWT）和离散小波包变换（DWPT）进行谐波分析。在正常工作下，PMFCL不产生明显的谐波，总谐波失真（THD）仅为0.11%。故障时出现高次谐波（3、5、7次），将THD提高到10.11%，通过短路环将THD降低到8.1%，保持了满意的电流抑制。将THD降低2%在工业应用中是非常重要的。PMFCL还改善了11 kV配电网的电压凹陷性能，将电压凹陷深度从0.3 pu降低到0.65 pu，并抑制了暂态恢复电压。通过与不同类型fcl的比较讨论，突出了PMFCL的新颖性和优势，特别是其被动操作和成本效益。研究表明，使用PMFCL后，谐波含量保持在IEEE 519-2022和英国G5/3限制范围内，因此PMFCL为改善现代系统的电能质量提供了有效的解决方案。本工作的主要贡献是利用PMFCL中心分支短路环减小谐波失真，以及在故障情况下使用PMFCL减小电压凹陷。

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引用次数: 0

Open-circuit faults of IGBTs in clamp double submodule-modular multilevel converters: Characterization and diagnosis 钳形双子模块-模块化多电平变换器中igbt的开路故障：表征与诊断

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-01-03 DOI: 10.1016/j.epsr.2025.112680

Hong Wu , Yue Wang , Xuan Li , Yi Liu , Yonghui Liu , Yufei Li

The clamp double submodule (CDSM) stands out among different submodules (SMs) of modular multilevel converters (MMCs) due to its capability of handling DC faults in a relatively cost-effective manner. However, research on diagnosing insulated gate bipolar transistor (IGBT) open-circuit faults (OCFs) in CDSMs remains limited, hindering their reliable application and further development. To address this issue, this article first conducted a comprehensive analysis of IGBT OCFs in the CDSM. The analysis indicates that the characteristics of IGBT OCFs in two half-bridge units (HBUs) of a CDSM are consistent with those observed in a single half-bridge submodule (HBSM). However, the OCF of the connecting IGBT in a CDSM is distinct from those observed in a single HBSM or full-bridge submodule (FBSM). Subsequently, a diagnostic strategy is proposed, which adopts the switching function and the capacitor voltage variation as diagnostic criteria. Specifically, an HBU is deemed faulty if and only if two conditions are satisfied concurrently: first, its switching function remains at a specific value for a duration exceeding the preset time threshold; second, the variation in its capacitor voltage over the same duration also exceeds the corresponding threshold. Experimental results in a hardware-in-the-loop (HIL) platform verify the effectiveness of the proposed strategy.

钳位双子模块（CDSM）在模块化多电平转换器（mmc）的不同子模块（SMs）中脱颖而出，因为它能够以相对经济有效的方式处理直流故障。然而，对cdsm中绝缘栅双极晶体管（IGBT）开路故障诊断的研究仍然有限，阻碍了cdsm的可靠应用和进一步发展。为了解决这一问题，本文首先对CDSM中的IGBT ocf进行了全面分析。分析表明，在CDSM的两个半桥单元（HBUs）中，IGBT ocf的特性与在单个半桥子模块（HBSM）中观察到的特性一致。然而，CDSM中连接IGBT的OCF与单个HBSM或全桥子模块（FBSM）中观察到的OCF不同。随后，提出了一种以开关函数和电容电压变化为诊断标准的诊断策略。具体来说，当且仅当同时满足两个条件时，HBU被认为是故障的：第一，其开关功能保持在特定值的时间超过预设的时间阈值；其次，其电容电压在相同持续时间内的变化也超过了相应的阈值。在硬件在环（HIL）平台上的实验结果验证了该策略的有效性。

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引用次数: 0

Research on electrical parameter preprocessing and residual electrical life prediction for AC contactors 交流接触器电参数预处理及剩余电寿命预测研究

IF 4.2 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2026-01-02 DOI: 10.1016/j.epsr.2025.112691

Yankai Li, Shuxin Liu, Chaojian Xing

To address the problems of insufficient processing and utilization of AC contactor electrical parameter information, and the difficulty of achieving high-precision residual electrical life prediction in existing methods, this paper proposes an electrical parameter preprocessing method and a residual electrical life prediction method. Firstly, seven electrical parameters are extracted from voltage and current signals collected during the full-life testing of AC contactors. Then, a hybrid parameter partitioning method is developed to partition electrical parameters into core and auxiliary parameters. Information extraction from auxiliary parameters is performed using a DRSN (deep residual shrinkage network), thereby establishing optimal inputs for the prediction model. Finally, an ECA-BiTCN(efficient channel attention, bidirectional temporal convolutional network) residual electrical life prediction model for AC contactors is developed. This model has the capabilities of multi-scale feature extraction and automatic enhancement of key features, thereby effectively improving prediction accuracy. Compared to other methods, the electrical parameter preprocessing method prevents the loss of electrical parameter information. The ECA-BiTCN model reduces the root mean square error and mean absolute error by at least 280.0626 and 111.5015, and improves the coefficient of determination by at least 0.0131. This approach provides a novel and effective technical solution for preprocessing electrical parameters and predicting residual electrical life of AC contactors.

针对交流接触器电参数信息处理利用不足、现有方法难以实现高精度剩余电寿命预测的问题，提出了一种电参数预处理方法和一种剩余电寿命预测方法。首先，从交流接触器全寿命测试中采集的电压和电流信号中提取7个电气参数；然后，提出了一种混合参数划分方法，将电气参数划分为核心参数和辅助参数。利用深度残余收缩网络（deep residual shrinkage network，简称DRSN）对辅助参数进行信息提取，从而为预测模型建立最优输入。最后，建立了交流接触器剩余电寿命预测模型ECA-BiTCN（efficient channel attention，双向时间卷积网络）。该模型具有多尺度特征提取和关键特征自动增强的能力，有效提高了预测精度。与其他方法相比，电参数预处理方法防止了电参数信息的丢失。ECA-BiTCN模型将均方根误差和平均绝对误差分别降低了280.0626和111.5015，并将确定系数提高了至少0.0131。该方法为交流接触器电参数预处理和剩余电寿命预测提供了一种新颖有效的技术解决方案。

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引用次数: 0