Electric Power Systems Research最新文献_第5页

Mission-critical microgrids: Strategies for safe and reliable operations

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-03-17 DOI: 10.1016/j.epsr.2025.111625

Amiron Wolff dos Santos Serra , Hércules Araújo Oliveira , Luiz Antonio de Souza Ribeiro , José Gomes de Matos , Alexandre Cunha Oliveira , Osvaldo Ronald Saavedra

{"title":"Mission-critical microgrids: Strategies for safe and reliable operations","authors":"Amiron Wolff dos Santos Serra , Hércules Araújo Oliveira , Luiz Antonio de Souza Ribeiro , José Gomes de Matos , Alexandre Cunha Oliveira , Osvaldo Ronald Saavedra","doi":"10.1016/j.epsr.2025.111625","DOIUrl":"10.1016/j.epsr.2025.111625","url":null,"abstract":"<div><div>This article details the design and real-world implementation of a mission-critical microgrid for an aerospace rocket launch center. The project focuses on ensuring energy security and reliability during launch operations, while also prioritizing emission reductions during non-operational periods. This paper makes significant contributions by identifying and addressing key challenges in the seamless integration and implementation of critical functionalities within microgrids, ensuring their reliable operation across various scenarios such as island capability and black-start operation. Key challenges including the mitigation of transformer inrush currents, fault detection in an ungrounded system, and power balance during off-grid operation have been successfully solved. The microgrid has been operational since June 2023. Experimental results from the commissioning phase are also included, which confirm the design's effectiveness in ensuring energy security and significantly reducing CO<sub>2</sub> emissions.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"245 ","pages":"Article 111625"},"PeriodicalIF":3.3,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-driven single-end partial discharge localization in power cables based on time domain reflectometry and transfer function analyses

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-03-17 DOI: 10.1016/j.epsr.2025.111601

Morteza Shamsoddini , Tongkun Lan , Seokbum Ko , Chi Yung Chung

{"title":"AI-driven single-end partial discharge localization in power cables based on time domain reflectometry and transfer function analyses","authors":"Morteza Shamsoddini , Tongkun Lan , Seokbum Ko , Chi Yung Chung","doi":"10.1016/j.epsr.2025.111601","DOIUrl":"10.1016/j.epsr.2025.111601","url":null,"abstract":"<div><div>Accurate localization of partial discharge (PD) in power cables is critical for minimizing downtime and associated costs. Therefore, this paper presents a single-end localization method that simplifies implementation by avoiding the complexities of double-sided or distributed schemes. A fundamental challenge for online monitoring systems based on a single-end measurement scheme is the accurate and autonomous identification of incident pulses and their corresponding reflections, particularly in environments where impulse noise and PD-like interference are present and may resemble actual PD pulses, making it difficult to distinguish true events from interfering pulses. In this regard, this paper proposes a method based on the traveling wave characteristics and transfer function (TF) analysis to pinpoint the PD source accurately, even in challenging conditions such as multi-path propagation, impulse noise, and simultaneous PD events. To achieve this, a cable-specific attenuation characteristic is developed and incorporated within a two-step signal segmentation algorithm, and then the U-Net model is employed to estimate PD pulses’ arrival time precisely. Additionally, the proposed method provides a statistical analysis of its maximum localization capability based on the noise level and cable length. The performance of the method is assessed under both homogeneous and inhomogeneous cable configurations. The results demonstrate a localization error of less than 1% for a 1.5 km cable.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"245 ","pages":"Article 111601"},"PeriodicalIF":3.3,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Faulty feeder identification under high impedance faults for flexible grounding distribution system based on leakage resistance identification

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-03-17 DOI: 10.1016/j.epsr.2025.111604

Xinqian Wang , Xiaowei Wang , Wenquan Shao , Fan Zhang , Jie Gao , Zhihua Zhang , Yizhao Wang

{"title":"Faulty feeder identification under high impedance faults for flexible grounding distribution system based on leakage resistance identification","authors":"Xinqian Wang , Xiaowei Wang , Wenquan Shao , Fan Zhang , Jie Gao , Zhihua Zhang , Yizhao Wang","doi":"10.1016/j.epsr.2025.111604","DOIUrl":"10.1016/j.epsr.2025.111604","url":null,"abstract":"<div><div>To address the challenge of identifying faulty feeder in the case of high impedance faults (HIF) within a flexible grounding system that employs parallel small resistance (PSR) from the arc suppression coil, this paper proposes a method for faulty feeder identification based on leakage resistance identification. Initially, the paper analyzes the variation characteristics of the equivalent resistance to ground (ERG), while taking into consideration the leakage resistance in the distribution line. For the healthy feeder, the ERG is always equal to its leakage resistance. In contrast, for a faulty feeder, the ERG is determined by paralleling the leakage resistances of all healthy feeders before the PSR is put into use, However, it becomes approximately three times greater than the neutral resistance once the PSR is in use. Subsequently, the ERG of each feeder is fitted using the least squares method, and a scheme for identifying faulty feeders is constructed. The effectiveness of this proposed method is ultimately validated through PSCAD simulations and field recording data, demonstrating that the transition resistance can reach up to 5000Ω, which is anticipated to enhance the performance of protection for single-phase-to-ground fault (SPGF).</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"245 ","pages":"Article 111604"},"PeriodicalIF":3.3,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nondominated sorting grey wolf algorithm-based optimal scheduling for electric-hydrogen-heat multi-energy microgrid

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-03-17 DOI: 10.1016/j.epsr.2025.111628

Dongming Song , Xingdong Wu , Leilei Jiang , Shuai Zhang , Nan Feng

{"title":"Nondominated sorting grey wolf algorithm-based optimal scheduling for electric-hydrogen-heat multi-energy microgrid","authors":"Dongming Song , Xingdong Wu , Leilei Jiang , Shuai Zhang , Nan Feng","doi":"10.1016/j.epsr.2025.111628","DOIUrl":"10.1016/j.epsr.2025.111628","url":null,"abstract":"<div><div>The multi-energy microgrid (MEMG) is a system in which multiple energy sources exist together, serving as a crucial approach to Accomplishing energy conservation, reducing emissions, and optimizing energy systems. Ensuring economic benefits while coordinating the utilization of multiple energy sources to balance environmental protection, and other objectives remains a critical issue that requires urgent resolution. This paper proposes a regional MEMG capable of absorbing CO₂ emissions and desalinating seawater. The alkaline solution generated during water electrolysis for hydrogen production can absorb CO₂ emitted by the system, while the desalination device produces fresh water. The system is capable of recovering waste heat from both water electrolysis and fuel cell processes. To enhance energy efficiency within the MEMG system, an optimization model is developed with the objectives of minimizing economic operating costs, reducing CO₂ emissions, and decreasing energy losses. Addressing the problem with an enhanced grey wolf optimization algorithm. Results show that the system performs excellently in handling complex multi-objective optimization problems, demonstrating significant advantages compared to various commonly used optimization algorithms. Specifically, the proposed system reduces operating costs by 24.08 %, CO₂ emissions by 13.25 %, and energy consumption by 8.60 %, achieving energy savings and emission reductions.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"245 ","pages":"Article 111628"},"PeriodicalIF":3.3,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic inertia security analysis under uncertain power variability

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-03-17 DOI: 10.1016/j.epsr.2025.111565

Wei Hua, Dongdong Li, Yang Mi

{"title":"Dynamic inertia security analysis under uncertain power variability","authors":"Wei Hua, Dongdong Li, Yang Mi","doi":"10.1016/j.epsr.2025.111565","DOIUrl":"10.1016/j.epsr.2025.111565","url":null,"abstract":"<div><div>In modern power systems, the integration of renewable energy sources (RESs) leads to increased uncertainty and reduced system inertia. The provision of inertia from RESs also adds uncertainty to system inertia, so it is important to study the impact of inertia uncertainty on frequency stability to ensure power system stability and security. To address the problem that how inertia uncertainty affects system frequency, this paper proposes a dynamic inertia security analysis method, integrating the probabilistic analysis method and the stochastic differential equations (SDEs), for inertia uncertainty analysis under uncertain power variability (UPV). A stochastic process is used to model UPV, and SDEs are used to model frequency dynamic under UPV accordingly. A frequency stability possibility index that includes frequency deviation and rate of change of frequency (RoCoF) is proposed for frequency stability analysis. The trajectories of the frequency are generated using Monte Carlo simulation (MCS) method considering dynamic inertia and UPV. The dynamic inertia security analysis is conducted to further analyze frequency stability. The proposed method is verified by a typical system frequency response (SFR) model and IEEE 39-bus system on Simulink.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"245 ","pages":"Article 111565"},"PeriodicalIF":3.3,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sensorless sequential charging guidance and control for multiple types of electric vehicles with ordered piles based on bi-objective hierarchical optimization

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-03-16 DOI: 10.1016/j.epsr.2025.111603

Wen Wang, Ye Yang, Qingwen Han, Shuaihua Li, Peijun Li, Fan Wu, Yulu Zhong

{"title":"Sensorless sequential charging guidance and control for multiple types of electric vehicles with ordered piles based on bi-objective hierarchical optimization","authors":"Wen Wang, Ye Yang, Qingwen Han, Shuaihua Li, Peijun Li, Fan Wu, Yulu Zhong","doi":"10.1016/j.epsr.2025.111603","DOIUrl":"10.1016/j.epsr.2025.111603","url":null,"abstract":"<div><div>In response to the difficulty in comprehensively analyzing the charging demand of multiple types of electric vehicles when orderly pile charging is ineffective, and the challenge in balancing the dual objectives of grid load variance and user charging costs, this study proposes a novel guidance and control method for non-sensitive charging of ordered piles and for charging multiple types of electric vehicles based on dual-objective hierarchical optimization. The Monte Carlo method is employed to accurately calculate the charging load requirements of various types of electric vehicles, providing a robust foundation for subsequent charging guidance and control. Moreover, this study integrates the variance of power grid load and user charging costs into a unified optimization framework and develops a dual-objective hierarchical optimization model for charging guidance and control, achieving an effective balance between the two objectives. To address this complex problem, an improved genetic algorithm was implemented, which effectively determines the distribution scheme of charging stations, charging times, charging power, and other parameters, thereby enhancing the efficiency of the solution. The experimental results demonstrate that this method can effectively guide users of multiple types of electric vehicles to charge during low load periods, minimizing the variance of grid load while maintaining user satisfaction with the charging cost and method. This illustrates the effectiveness and superiority of the approach in practical applications.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"245 ","pages":"Article 111603"},"PeriodicalIF":3.3,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-agent deep reinforcement learning for mitigation of unbalanced active powers using distributed batteries in low voltage residential distribution system 利用分布式电池缓解低压住宅配电系统不平衡有功功率的多代理深度强化学习

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-03-15 DOI: 10.1016/j.epsr.2025.111599

Watcharakorn Pinthurat , Branislav Hredzak

{"title":"Multi-agent deep reinforcement learning for mitigation of unbalanced active powers using distributed batteries in low voltage residential distribution system","authors":"Watcharakorn Pinthurat , Branislav Hredzak","doi":"10.1016/j.epsr.2025.111599","DOIUrl":"10.1016/j.epsr.2025.111599","url":null,"abstract":"<div><div>High penetration and uneven distribution of single-phase rooftop PVs and load demands in power systems can cause unbalanced active powers, which in turn can adversely affect power quality and system reliability. This paper proposes a multi-agent deep reinforcement learning-based strategy to compensate for the unbalanced active powers by employing single-phase battery systems distributed in the LV residential distribution system and subsidized by the utility. First, the unbalanced active powers are formulated as a Markov game. Then, the Markov game can be solved by a multi-agent deep deterministic policy gradient algorithm. The proposed strategy uses only local measurements, and the experiences of the agents are shared in a centralized manner during training to achieve cooperative task. Information about phase connections of the battery systems is no longer required. The proposed strategy can learn from historical data and gradually become mastered. The four-wire LV residential distribution system uses real data from rooftop PVs and demands for verification. As adaptive agents, the battery systems are able to cooperatively operate by charging/discharging active powers so that neutral current at the point of common connection can be minimized.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"245 ","pages":"Article 111599"},"PeriodicalIF":3.3,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Volt/VAR optimization for photovoltaic-storage-charging station high-permeability power distribution networks: A data-knowledge hybrid driven reinforcement learning method

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-03-15 DOI: 10.1016/j.epsr.2025.111618

Minghe Wu , Lucheng Hong , Yubo Yuan , Yuan Gao , Jie Gu , Jiaqi Song

{"title":"Volt/VAR optimization for photovoltaic-storage-charging station high-permeability power distribution networks: A data-knowledge hybrid driven reinforcement learning method","authors":"Minghe Wu , Lucheng Hong , Yubo Yuan , Yuan Gao , Jie Gu , Jiaqi Song","doi":"10.1016/j.epsr.2025.111618","DOIUrl":"10.1016/j.epsr.2025.111618","url":null,"abstract":"<div><div>The integration of a high proportion of electric vehicles and photovoltaic systems has increased the uncertainty of power flow state transitions in power distribution networks (PDN). To address the intermittency of power and the complexity of operational constraints caused by the integration of photovoltaic-storage-charging station systems into the PDN, this paper proposes a Volt/VAR optimization (VVO) framework driven by a data-knowledge hybrid approach. In this framework, a novel DistFlow-based soft actor-critic (DFSAC) algorithm is first introduced, which constructs an expert knowledge safety layer based on the DistFlow equations to describe the coupling relationship between the reinforcement learning actions and PDN voltage, ensuring the safety of the VVO strategy. A novel data augmentation technique and a linearized power flow calculation method are then proposed, enhancing the diversity and completeness of the state-action pairs for the reinforcement learning agent and increasing the speed of interaction with the environment. Finally, numerical experiments using real PDN operational data and the IEEE 34-bus system are conducted. The results show that the proposed method outperforms other state-of-the-art VVO methods, demonstrating better performance and good robustness under extreme source-load power conditions. The DistFlow linearization safety layer also shows good scalability in large-scale real power systems. Additionally, the proposed data augmentation method improves the DFSAC performance by approximately 75 % to 85 %, and the linearized power flow calculation method increases the overall training speed of the DFSAC agent by about 3 times.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"245 ","pages":"Article 111618"},"PeriodicalIF":3.3,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep entropy learning for multi-energy cooperation system with non-dispatchable generation and storage unit under load shedding

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-03-14 DOI: 10.1016/j.epsr.2025.111619

Kiavash Parhizkar, Borzou Yousefi, Mohammad Rezvani, Abdolreza Noori Shirazi

{"title":"Deep entropy learning for multi-energy cooperation system with non-dispatchable generation and storage unit under load shedding","authors":"Kiavash Parhizkar, Borzou Yousefi, Mohammad Rezvani, Abdolreza Noori Shirazi","doi":"10.1016/j.epsr.2025.111619","DOIUrl":"10.1016/j.epsr.2025.111619","url":null,"abstract":"<div><div>This study explores the resilience of a multi-energy cooperation generation (MECG) system with electricity, natural gas networks, and sustainable energy units (SEUs). The wind farm generation (WFG), as the non-dispatchable unit, is adopted to supply the grid for the profit of customers. In particular, an innovative entropy learning-based soft-actor critic is introduced to assess system resilience against low-probability but high-destruction events. The suggested framework was validated through empirical analysis using the IEEE 24-bus network and the Belgian 20-node gas network equipped with wind turbine and battery unit. In deep entropy learning, the problem of the MECG system is modeled based on the Markovian decision process (MDP) to solve the optimization problem by interacting the agent with the complex network (environment). By maximizing a reward function, the deep neural network (DNN) of deep entropy learning is trained so that optimizes the complex MECG system with WFG and battery storage unit (BSU). Our findings illuminate the potential efficiency gains and the enhanced adaptive capacity achievable through strategic integration, providing actionable insights for policymakers, engineers, and researchers. By contributing to the discourse on resilient and sustainable energy systems, this study addresses the urgent need for robust energy infrastructures capable of withstanding today's dynamic environmental and operational landscapes.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"245 ","pages":"Article 111619"},"PeriodicalIF":3.3,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning-based decomposition and sub-problem acceleration for fast production cost minimization simulation

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-03-14 DOI: 10.1016/j.epsr.2025.111582

Zishan Guo , Chong Qu , Qinran Hu , Tao Qian

{"title":"Learning-based decomposition and sub-problem acceleration for fast production cost minimization simulation","authors":"Zishan Guo , Chong Qu , Qinran Hu , Tao Qian","doi":"10.1016/j.epsr.2025.111582","DOIUrl":"10.1016/j.epsr.2025.111582","url":null,"abstract":"<div><div>Production cost minimization (PCM) simulation is crucial for long-term power system assessments, yet it presents significant computational challenges due to the large number of binary variables involved. The traditional time-domain decomposition (TDD) method aims to expedite PCM solving but frequently lead to substantial temporal constraint violations, thereby compromising accuracy. While machine learning (ML) techniques have been integrated with branch and bound (B&B) algorithms to enhance solving speed and maintain optimality, they have not achieved significant acceleration. To address these challenges, this paper introduces a two-pronged framework: (1) a learning-based TDD approach that employs multiple binary classification techniques to generate a high-quality set of initial decomposition segments (IDSs), which helps in reducing constraint violations across sub-problems; and (2) a sub-problem acceleration approach that utilizes relay learning to expedite the solving of sub-problems while preserving optimality. Simulation results show that our approach can solve yearly time horizon PCMs within tens of seconds with a more than 35% reduction on the number of constraint violations compared to traditional TDD method.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"245 ","pages":"Article 111582"},"PeriodicalIF":3.3,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0