Electric Power Systems Research最新文献_第3页

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

Flexibility optimization from distributed storage resources under stochastic uncertainties 随机不确定性下分布式存储资源的灵活性优化

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.2025.112674

Lílian Venturi Pinheiro , Thiago Ribeiro De Barros , Leonardo Willer De Oliveira , Janaína G. Oliveira , Tiago André Soares , Bruno Henriques Dias

The present work proposes a two-stage optimization approach for flexibility services provided by battery energy storage systems (BESS) in distribution networks with photovoltaic (PV) generation and electric vehicles (EV). The considered flexibility services include reserve allocation and voltage regulation to support network operation. The first stage optimizes the day-ahead (DA) scheduling of distributed BESS to minimize overall costs, including energy, BESS usage, and reserve, while accounting for stochastic variations in load, PV generation, and EV penetration. The second stage simulates the real-time (RT) operation of the electrical distribution network, evaluating system behavior under different scenarios based on DA decisions. A coordinated control strategy is applied, integrating DA scheduling with network voltage levels. Deviations between BESS outputs in DA and RT stages are fed back into a new DA run to adjust outputs and reduce costs. Results on a medium-voltage distribution system with 157 nodes (based on a reduced version of the EPRI CKT5 feeder) demonstrate that the proposed scenario-based model provides feasible solutions under uncertainty, with BESS playing a key role while strictly adhering to planned operational modes from DA to RT, as typically enforced in energy market participation.

本文提出了一种两阶段优化方法，用于光伏（PV）发电和电动汽车（EV）配电网中电池储能系统（BESS）提供的灵活性服务。考虑的灵活性服务包括储备分配和电压调节，以支持网络运行。第一阶段优化分布式BESS的日前调度，以最小化总成本，包括能源、BESS使用和储备，同时考虑负载、光伏发电和电动汽车渗透率的随机变化。第二阶段模拟配电网的实时运行，基于数据分析决策评估不同场景下的系统行为。采用了一种将数据处理调度与网络电压水平相结合的协调控制策略。在数据分析和实时数据分析阶段，BESS输出之间的偏差被反馈到新的数据分析运行中，以调整输出并降低成本。对157个节点的中压配电系统（基于EPRI CKT5馈线的简化版本）的结果表明，所提出的基于场景的模型在不确定性下提供了可行的解决方案，其中BESS在严格遵守从DA到RT的计划运行模式（通常在能源市场参与中强制执行）的同时发挥了关键作用。

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引用次数: 0

An Intelligent diagnosis and hierarchical risk assessment method for current sensors driven by multi-source feature fusion 基于多源特征融合驱动的电流传感器智能诊断与分层风险评估方法

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.2025.112704

Mingyu Zhan , Zhifeng Wang , Shiqin Wang , Yongquan Chen , Yujia Wang , Chenhao Sun

Current sensors are critical for power metering, power-quality monitoring, and anomaly detection in wind–solar–storage systems, and their stability directly affects dispatch accuracy and system security. Under long-term dynamic operating conditions and complex environments, however, they are prone to latent degradation faults such as drift, delay, and waveform distortion that are difficult to detect reliably using conventional methods. This paper proposes a multilayer, risk-oriented diagnostic framework for current sensors, termed Sensor Monitoring with Fuzzy–Trend Network (SMF-TNet). SMF-TNet integrates multi-source feature acquisition, risk-sensitivity analysis, fuzzy-level modeling, and dual-path inference to achieve hierarchical risk representation and diagnostic decision-making from raw observations to fault identification. Unlike existing hybrid schemes that adopt a single fuzzy-inference path or loosely combine multi-source data, SMF-TNet jointly exploits variable risk weights, fuzzy risk scores, and complementary rule-based and trend-based information within a unified architecture, enabling more faithful characterization of sensor risk evolution. Case studies using data from a practical wind power system show that SMF-TNet improves fault-risk identification and high-risk sample discrimination, while reducing false alarms and missed detections compared with benchmark models, demonstrating its potential for multi-source monitoring and early warning of critical sensors in renewable-rich power systems.

电流传感器是风电-太阳能储能系统中电能计量、电能质量监测和异常检测的关键，其稳定性直接影响到调度的准确性和系统的安全性。然而，在长期动态运行条件和复杂环境下，它们容易出现漂移、延迟和波形畸变等潜在退化故障，这些故障难以用常规方法可靠地检测出来。本文提出了一种多层、面向风险的电流传感器诊断框架，称为模糊趋势网络传感器监测（SMF-TNet）。SMF-TNet集成了多源特征获取、风险敏感性分析、模糊级建模和双路径推理，实现了从原始观测到故障识别的分层风险表示和诊断决策。与现有采用单一模糊推理路径或松散组合多源数据的混合方案不同，SMF-TNet在统一架构内共同利用可变风险权重、模糊风险评分以及互补的基于规则和基于趋势的信息，从而能够更忠实地表征传感器风险演变。使用实际风电系统数据的案例研究表明，与基准模型相比，SMF-TNet改进了故障风险识别和高风险样本识别，同时减少了误报和漏检，展示了其在可再生能源丰富的电力系统中多源监测和关键传感器早期预警的潜力。

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引用次数: 0

Low-cost dual-factor programmable circuit breaker for demand-response load management in industrial microgrids 用于工业微电网需求响应负荷管理的低成本双因素可编程断路器

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.2025.112705

Oluwasegun O. Oladapo, Patrick W.C. Ho, Charles R. Sarimuthu

Industrial sectors are facing increasing demands to reduce operational expenditures, enhance energy efficiency, and maintain high safety standards. Ineffective load management, prolonged fault detection, and unauthorized switching contribute to wasted energy, production disruptions, and increased maintenance expenditures. This manuscript presents a dual-factor authentication programmable circuit breaker that integrates biometric fingerprint recognition, password authentication, and real-time current monitoring utilizing a low-cost ATmega328P platform. Unlike existing designs that implement authentication or fault detection in isolation, the proposed system uniquely combines secure access, sub-50 ms protection response, and demand-responsive load-priority management for peak energy reduction in industrial microgrids.

Experimental validation confirmed complete unauthorized access rejection, fault detection within ±0.1 A, and tripping times of 45–49 ms, compliant with IEC 60947-2 and IEEE C37.112 standards. Simulations in Python validated that adaptive demand response strategies achieved a peak current reduction of up to 35% while maintaining critical loads for 30 days. The proposed design surpasses traditional and advanced smart breakers in response times, authentication strength, and fault detection accuracy, as evidenced by benchmarking. These findings indicate that the breaker serves as a secure, scalable solution for industrial microgrids, enhancing protection and facilitating energy-aware load management.

工业部门正面临着减少运营支出、提高能源效率和保持高安全标准的日益增长的需求。无效的负载管理、长时间的故障检测和未经授权的切换会导致能源浪费、生产中断和维护支出增加。本文介绍了一种双因素认证可编程断路器，该断路器利用低成本的ATmega328P平台集成了生物指纹识别，密码认证和实时电流监测。与现有的隔离实施身份验证或故障检测的设计不同，该系统独特地结合了安全访问、低于50毫秒的保护响应和需求响应负载优先管理，用于工业微电网的峰值能耗降低。实验验证完全拒绝非法接入，故障检测在±0.1 A范围内，跳闸时间45-49 ms，符合IEC 60947-2和IEEE C37.112标准。Python中的模拟验证了自适应需求响应策略在维持临界负载30天的情况下实现了高达35%的峰值电流减少。通过基准测试证明，该设计在响应时间、认证强度和故障检测精度方面超越了传统和先进的智能断路器。这些发现表明，断路器可作为工业微电网的安全，可扩展的解决方案，增强保护和促进能源意识负载管理。

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引用次数: 0

A cement plant load disaggregation method based on an improved UNet model for fine-grained energy monitoring 基于改进UNet模型的细粒度能源监测水泥厂负荷分解方法

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.2025.112690

Ao WANG , Shuangyuan WANG , Guozhen BAI , Ran LI

Cement plants are among the most energy-intensive industrial facilities, where equipment-level energy monitoring is essential for effective energy conservation and emission reduction. Non-intrusive load monitoring (NILM) provides a low-cost approach to achieve this goal; however, most existing NILM algorithms are optimized for residential loads and exhibit poor accuracy under the multi-phase, strongly coupled, and non-stationary operating conditions of cement plants. To overcome these limitations, this study proposes Cement-UNet, a high-precision multitask load disaggregation model tailored for cement plant NILM. Building upon the U-Net backbone, Cement-UNet introduces: (1) an R2CL encoder that jointly captures transient-steady-state interactions through hybrid convolutional and recurrent operations; (2) a GMSA dual-attention decoder that explicitly models intra-device and inter-device power coupling via hierarchical self- and cross-attention; and (3) multi-level skip connections with a lightweight pyramid pooling module to enhance multi-scale feature reconstruction and computational efficiency.The model’s performance is validated on both the public UK-DALE dataset and a real-world cement plant dataset. Experimental results demonstrate that Cement-UNet achieves reductions of 22.1 %, 14.3 %, and 18.9 % in MAE, SAE, and RMSE, respectively, relative to the best-performing baselines, while maintaining relatively low computational complexity. These findings confirm that Cement-UNet effectively improves disaggregation accuracy and generalization in complex cement industrial energy systems.

水泥厂是能源最密集的工业设施之一，设备级能源监测对有效节能减排至关重要。非侵入式负载监测（NILM）为实现这一目标提供了一种低成本的方法；然而，大多数现有的NILM算法都针对住宅负荷进行了优化，并且在水泥厂的多相、强耦合和非平稳运行条件下精度较差。为了克服这些限制，本研究提出了cement - unet，这是一个为水泥厂NILM量身定制的高精度多任务负载分解模型。在U-Net骨干网的基础上，Cement-UNet引入了：(1)一个R2CL编码器，通过混合卷积和循环操作共同捕获瞬态-稳态相互作用；(2) GMSA双注意解码器，通过分层自注意和交叉注意明确模拟设备内和设备间的功率耦合；(3)采用轻量级金字塔池化模块进行多级跳跃连接，提高多尺度特征重构和计算效率。该模型的性能在公开的UK-DALE数据集和真实的水泥厂数据集上进行了验证。实验结果表明，与最佳基准相比，Cement-UNet在MAE、SAE和RMSE方面分别降低了22.1%、14.3%和18.9%，同时保持了相对较低的计算复杂度。这些发现证实了cement - unet在复杂的水泥工业能源系统中有效地提高了分解精度和泛化。

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引用次数: 0

A novel cluster partition method of hydro-wind-photovoltaic stations considering electrical-hydraulic connection and power output stability 一种考虑电液连接和输出稳定性的水风光伏电站集群划分新方法

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.2025.112693

Zhuang Nie , Yongcan Chen , Yiping Miao , Bin Luo , Weilin Xu , Shijun Chen , Renshan Ding , Jianghong Tang

The rapid growth in the scale and number of stations has brought significant challenges to the operation of hydro-wind-photovoltaic (PV) multi-energy complementary system. To simplify the dispatching process and improve the efficiency, this paper proposes a novel cluster partition method for hydro-wind-PV stations (HWPS). Compared with traditional methods, this method innovatively introduces the hydraulic coupling and the stability of total power output into cluster partition. Firstly, cluster partition indicators are carefully designed, including electrical connections, hydraulic coupling, and power output stability. Secondly, a power station network composed of "node-edge" is constructed, where nodes represent power stations and edges represent the strength of relationship. Finally, based on the constructed network, a community detection algorithm is applied with the goal of maximizing modularity to obtain the optimal solution. Simulation results based on the IEEE 34-node system demonstrate significant improvements: the proposed method increases cascade hydropower generation by 410 million kWh, reduces water curtailment by 449 million m³, and decreases the total output fluctuation coefficient by 25.38 %. These results confirm its significant potential for enhancing operational benefits and grid stability.

电站规模和数量的快速增长给水风光伏多能互补系统的运行带来了重大挑战。为了简化调度流程，提高调度效率，提出了一种新的水电-风-光伏电站集群划分方法。与传统方法相比，该方法创新性地将液力耦合和总输出功率的稳定性引入到集群划分中。首先，精心设计集群分区指标，包括电气连接、液力耦合和功率输出稳定性。其次，构建由“节点-边”构成的电站网络，节点代表电站，边代表关系强度。最后，在构建的网络基础上，以模块化最大化为目标，应用社区检测算法获得最优解。基于IEEE 34节点系统的仿真结果表明：该方法可使梯级水力发电量增加4.1亿kWh，减少减水量4.49亿m³，总出力波动系数降低25.38%。这些结果证实了它在提高运营效益和电网稳定性方面的巨大潜力。

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引用次数: 0