CSEE Journal of Power and Energy Systems最新文献_第8页

Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework 发电机跳闸方案的智能预测：基于知识融合的深度强化学习框架

IF 7.1 2区工程技术

CSEE Journal of Power and Energy Systems Pub Date : 2023-12-28 DOI: 10.17775/CSEEJPES.2022.08970

Lingkang Zeng;Wei Yao;Ze Hu;Hang Shuai;Zhouping Li;Jinyu Wen;Shijie Cheng

{"title":"Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework","authors":"Lingkang Zeng;Wei Yao;Ze Hu;Hang Shuai;Zhouping Li;Jinyu Wen;Shijie Cheng","doi":"10.17775/CSEEJPES.2022.08970","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2022.08970","url":null,"abstract":"Generator tripping scheme (GTS) is the most commonly used scheme to prevent power systems from losing safety and stability. Usually, GTS is composed of offline predetermination and real-time scenario match. However, it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power system. To improve efficiency of predetermination, this paper proposes a framework of knowledge fusion-based deep reinforcement learning (KF-DRL) for intelligent predetermination of GTS. First, the Markov Decision Process (MDP) for GTS problem is formulated based on transient instability events. Then, linear action space is developed to reduce dimensionality of action space for multiple controllable generators. Especially, KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making process. This can enhance the efficiency and learning process. Moreover, the graph convolutional network (GCN) is introduced to the policy network for enhanced learning ability. Numerical simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 1","pages":"66-75"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375964","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Convexification of Hybrid AC-DC Optimal Power Flow with Line-Commutated Converters 带线路换流器的交直流混合优化功率流的凸化

IF 7.1 2区工程技术

CSEE Journal of Power and Energy Systems Pub Date : 2023-12-28 DOI: 10.17775/CSEEJPES.2022.05250

Hongyuan Liang;Zhigang Li;J. H. Zheng;Q. H. Wu

引用次数: 0

System Strength Assessment Based on Multi-task Learning 基于多任务学习的系统强度评估

IF 7.1 2区工程技术

CSEE Journal of Power and Energy Systems Pub Date : 2023-12-28 DOI: 10.17775/CSEEJPES.2023.00440

Baoluo Li;Shiyun Xu;Huadong Sun;Zonghan Li;Lin Yu

{"title":"System Strength Assessment Based on Multi-task Learning","authors":"Baoluo Li;Shiyun Xu;Huadong Sun;Zonghan Li;Lin Yu","doi":"10.17775/CSEEJPES.2023.00440","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.00440","url":null,"abstract":"Increase in permeability of renewable energy sources (RESs) leads to the prominent problem of voltage stability in power system, so it is urgent to have a system strength evaluation method with both accuracy and practicability to control its access scale within a reasonable range. Therefore, a hybrid intelligence enhancement method is proposed by combining the advantages of mechanism method and data driven method. First, calculation of critical short circuit ratio (CSCR) is set as the direction of intelligent enhancement by taking the multiple renewable energy station short circuit ratio as the quantitative indicator. Then, the construction process of CSCR dataset is proposed, and a batch simulation program of samples is developed accordingly, which provides a data basis for subsequent research. Finally, a multi-task learning model based on progressive layered extraction is used to simultaneously predict CSCR of each RESs connection point, which significantly reduces evaluation error caused by weak links. Predictive performance and anti-noise performance of the proposed method are verified on the CEPRI-FS-102 bus system, which provides strong technical support for real-time monitoring of system strength.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 1","pages":"41-50"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375966","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control 基于并行系统的有功功率校正控制人工智能量化评估与自我进化

IF 7.1 2区工程技术

CSEE Journal of Power and Energy Systems Pub Date : 2023-12-28 DOI: 10.17775/CSEEJPES.2023.00190

Tianyun Zhang;Jun Zhang;Feiyue Wang;Peidong Xu;Tianlu Gao;Haoran Zhang;Ruiqi Si

{"title":"Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control","authors":"Tianyun Zhang;Jun Zhang;Feiyue Wang;Peidong Xu;Tianlu Gao;Haoran Zhang;Ruiqi Si","doi":"10.17775/CSEEJPES.2023.00190","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.00190","url":null,"abstract":"In artificial intelligence (AI) based-complex power system management and control technology, one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence evolution. However, there is, currently, nearly no standard technical framework for objective and quantitative intelligence evaluation. In this article, based on a parallel system framework, a method is established to objectively and quantitatively assess the intelligence level of an AI agent for active power corrective control of modern power systems, by resorting to human intelligence evaluation theories. On this basis, this article puts forward an AI self-evolution method based on intelligence assessment through embedding a quantitative intelligence assessment method into automated reinforcement learning (AutoRL) systems. A parallel system based quantitative assessment and self-evolution (PLASE) system for power grid corrective control AI is thereby constructed, taking Bayesian Optimization as the measure of AI evolution to fulfill autonomous evolution of AI under guidance of their intelligence assessment results. Experiment results exemplified in the power grid corrective control AI agent show the PLASE system can reliably and quantitatively assess the intelligence level of the power grid corrective control agent, and it could promote evolution of the power grid corrective control agent under guidance of intelligence assessment results, effectively, as well as intuitively improving its intelligence level through self-evolution.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 1","pages":"13-28"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375965","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

New-Type Power System Stabilizers (NPSS) for Damping Wideband Oscillations in Converter-Dominated Power Systems 用于抑制变流器主导电力系统宽带振荡的新型电力系统稳定器（NPSS）

IF 6.9 2区工程技术

CSEE Journal of Power and Energy Systems Pub Date : 2023-12-28 DOI: 10.17775/CSEEJPES.2023.02130

Jan Shair;Xiaorong Xie;Haozhi Li;Vladimir Terzija

{"title":"New-Type Power System Stabilizers (NPSS) for Damping Wideband Oscillations in Converter-Dominated Power Systems","authors":"Jan Shair;Xiaorong Xie;Haozhi Li;Vladimir Terzija","doi":"10.17775/CSEEJPES.2023.02130","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.02130","url":null,"abstract":"Traditionally, synchronous generators are equipped with power system stabilizers (PSSs) to stabilize electromechanical low-frequency oscillations (LFOs). With increasing applications of converter-based devices in modern power systems, system-wide control interactions between different converter-based devices are causing more prevalent electromagnetic wide-band oscillations (WBOs). Extensive research has been done on design, application, and standardization of classical PSSs for addressing LFOs associated with synchronous generators. However, little attention is given to same for converter-based devices as a countermeasure to electromagnetic WBOs. This paper proposes a concept of utilizing new-type power system stabilizers (NPSSs) specifically designed to address WBOs caused by converter-control interactions in modern converter-dominated power systems. The article puts forward various design strategies, tuning methods, and implementation techniques of the NPSSs to counteract WBOs. The NPSSs are not limited to inverter-based resources (type-3/4 wind turbines); they can also be applied to other converter-based devices such as FACTS controllers, HVDC links, battery storage inverters, etc. We present several application scenarios demonstrating the importance of supplementing NPSSs in converter-based devices. Furthermore, the paper highlights current research challenges and opportunities in designing and implementing NPSS for various converter-based devices, providing a comprehensive overview of state-of-the-art and future directions in this area.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"11 3","pages":"1186-1198"},"PeriodicalIF":6.9,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10376003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Design and Experimental Testing of a Moving Coil Actuator with Compensation Coils 带补偿线圈的动圈致动器的设计与实验测试

IF 7.1 2区工程技术

CSEE Journal of Power and Energy Systems Pub Date : 2023-12-28 DOI: 10.17775/CSEEJPES.2023.05240

Boyuan Yin;Xianwu Zeng;John Frederick Eastham;Emelie Nilsson;Jean-francois Rouquette;Jean Rivenc;Ludovic Ybanez;Xiaoze Pei

{"title":"Design and Experimental Testing of a Moving Coil Actuator with Compensation Coils","authors":"Boyuan Yin;Xianwu Zeng;John Frederick Eastham;Emelie Nilsson;Jean-francois Rouquette;Jean Rivenc;Ludovic Ybanez;Xiaoze Pei","doi":"10.17775/CSEEJPES.2023.05240","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.05240","url":null,"abstract":"Hydrogen-powered electric aircraft have attracted significant interests aiming to achieve decarbonization targets. Onboard DC electric networks are facing great challenges in DC fault protection requirements. Vacuum interrupters are widely used in low voltage and medium voltage power systems due to being environmentally friendly with low maintenance. In this paper a moving coil actuator with compensation coils for a vacuum interrupter, as part of a hybrid direct current circuit breaker, is designed and experimentally tested. Compensation coils are used to improve operating speed compared with original moving coil actuator. Comparisons between four possible connections of compensation coils and original moving coil actuator are carried out. Experimental results show comparisons between different connections of actuator coils in terms of opening time and coil current with a range of pre-charged capacitor voltages. Dynamic performance of each actuator connection is also compared. The actuator with compensation coils is shown to have a higher current rising rate and achieve faster opening speed, which is a critical requirement for electric aircraft network protection. The parallel connection actuator achieves the highest opening speed within 3.5 ms with capacitor voltage of 50 V.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 2","pages":"707-716"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375970","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140351466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Adaptive Emergency Control of Power Systems Based on Deep Belief Network 基于深度信念网络的电力系统自适应应急控制

IF 6.9 2区工程技术

CSEE Journal of Power and Energy Systems Pub Date : 2023-12-28 DOI: 10.17775/CSEEJPES.2022.00070

Junyong Wu;Baoqin Li;Liangliang Hao;Fashun Shi;Pengjie Zhao

{"title":"Adaptive Emergency Control of Power Systems Based on Deep Belief Network","authors":"Junyong Wu;Baoqin Li;Liangliang Hao;Fashun Shi;Pengjie Zhao","doi":"10.17775/CSEEJPES.2022.00070","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2022.00070","url":null,"abstract":"Emergency control is an essential means to help system maintain synchronism after fault clearance. Traditional “offline calculation, online matching” scheme faces significant challenges on adaptiveness and robustness problems. To address these challenges, this paper proposes a novel closed-loop framework of transient stability prediction (TSP) and emergency control based on Deep Belief Network (DBN). First, a hierarchical real-time anti-jitter TSP method using sliding time windows is adopted, which takes into account accuracy and rapidity at the same time. Next, a sensitivity regression model is established to mine the implicit relationship between power angles and sensitivity. When impending instability of the system is foreseen, optimal emergency control strategy can be determined in time. Lastly, responses after emergency control are fed back to the TSP model. If prediction result is still unstable, an additional control strategy will be implemented. Comprehensive numerical case studies are conducted on New England IEEE 39-bus system and Northeast Power Coordinated Council (NPCC) 140-bus system. Results show the proposed method can detect instability of system as soon as possible and assist in maintaining reliable system synchronism.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 4","pages":"1618-1631"},"PeriodicalIF":6.9,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375981","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control 面向大规模电网调度与控制的人机混合增强智能系统框架与关键技术

IF 7.1 2区工程技术

CSEE Journal of Power and Energy Systems Pub Date : 2023-12-28 DOI: 10.17775/CSEEJPES.2023.00940

Shixiong Fan;Jianbo Guo;Shicong Ma;Lixin Li;Guozheng Wang;Haotian Xu;Jin Yang;Zening Zhao

{"title":"Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control","authors":"Shixiong Fan;Jianbo Guo;Shicong Ma;Lixin Li;Guozheng Wang;Haotian Xu;Jin Yang;Zening Zhao","doi":"10.17775/CSEEJPES.2023.00940","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.00940","url":null,"abstract":"With integration of large-scale renewable energy, new controllable devices, and required reinforcement of power grids, modern power systems have typical characteristics such as uncertainty, vulnerability and openness, which makes operation and control of power grids face severe security challenges. Application of artificial intelligence (AI) technologies represented by machine learning in power grid regulation is limited by reliability, interpretability and generalization ability of complex modeling. Mode of hybrid-augmented intelligence (HAI) based on human-machine collaboration (HMC) is a pivotal direction for future development of AI technology in this field. Based on characteristics of applications in power grid regulation, this paper discusses system architecture and key technologies of human-machine hybrid-augmented intelligence (HHI) system for large-scale power grid dispatching and control (PGDC). First, theory and application scenarios of HHI are introduced and analyzed; then physical and functional architectures of HHI system and human-machine collaborative regulation process are proposed. Key technologies are discussed to achieve a thorough integration of human/machine intelligence. Finally, state-of-the-art and future development of HHI in power grid regulation are summarized, aiming to efficiently improve the intelligent level of power grid regulation in a human-machine interactive and collaborative way.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 1","pages":"1-12"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375976","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Fully Decoupled Branch Energy Balancing Control Method for Modular Multilevel Matrix Converter Based on Sequence Circulating Components 基于序列循环组件的模块化多电平矩阵转换器全解耦支路能量平衡控制方法

IF 7.1 2区工程技术

CSEE Journal of Power and Energy Systems Pub Date : 2023-12-28 DOI: 10.17775/CSEEJPES.2023.01280

Zexin Zhao;Weijiang Chen;Zhichang Yang;Guoliang Zhao;Bin Han;Yunfei Xu;Nianwen Xiang;Shulai Wang

{"title":"Fully Decoupled Branch Energy Balancing Control Method for Modular Multilevel Matrix Converter Based on Sequence Circulating Components","authors":"Zexin Zhao;Weijiang Chen;Zhichang Yang;Guoliang Zhao;Bin Han;Yunfei Xu;Nianwen Xiang;Shulai Wang","doi":"10.17775/CSEEJPES.2023.01280","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.01280","url":null,"abstract":"The modular multilevel matrix converter (M3C) is a potential frequency converter for low-frequency AC transmission. However, capacitor voltage control of high-voltage and large-capacity M3C is more difficult, especially for voltage balancing between branches. To solve this problem, this paper defines sequence circulating components and theoretically analyzes the influence mechanism of different sequence circulating components on branch capacitor voltage. A fully decoupled branch energy balancing control method based on four groups of sequence circulating components is proposed. This method can control capacitor voltages of nine branches in horizontal, vertical and diagonal directions. Considering influences of both circulating current and voltage, a cross decoupled control is designed to improve control precision. Simulation results are taken from a low-frequency transmission system based on PSCAD/EMTDC, and effectiveness and precision of the proposed branch energy balancing control method are verified in the case of nonuniform parameters and an unbalanced power system.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 1","pages":"235-247"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375968","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139694897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Constraint Learning-based Optimal Power Dispatch for Active Distribution Networks with Extremely Imbalanced Data 基于约束学习的极不平衡数据有源配电网优化电力调度

IF 7.1 2区工程技术

CSEE Journal of Power and Energy Systems Pub Date : 2023-12-28 DOI: 10.17775/CSEEJPES.2023.05970

Yonghua Song;Ge Chen;Hongcai Zhang

{"title":"Constraint Learning-based Optimal Power Dispatch for Active Distribution Networks with Extremely Imbalanced Data","authors":"Yonghua Song;Ge Chen;Hongcai Zhang","doi":"10.17775/CSEEJPES.2023.05970","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.05970","url":null,"abstract":"Transition towards carbon-neutral power systems has necessitated optimization of power dispatch in active distribution networks (ADNs) to facilitate integration of distributed renewable generation. Due to unavailability of network topology and line impedance in many distribution networks, physical model-based methods may not be applicable to their operations. To tackle this challenge, some studies have proposed constraint learning, which replicates physical models by training a neural network to evaluate feasibility of a decision (i.e., whether a decision satisfies all critical constraints or not). To ensure accuracy of this trained neural network, training set should contain sufficient feasible and infeasible samples. However, since ADNs are mostly operated in a normal status, only very few historical samples are infeasible. Thus, the historical dataset is highly imbalanced, which poses a significant obstacle to neural network training. To address this issue, we propose an enhanced constraint learning method. First, it leverages constraint learning to train a neural network as surrogate of ADN's model. Then, it introduces Synthetic Minority Oversampling Technique to generate infeasible samples to mitigate imbalance of historical dataset. By incorporating historical and synthetic samples into the training set, we can significantly improve accuracy of neural network. Furthermore, we establish a trust region to constrain and thereafter enhance reliability of the solution. Simulations confirm the benefits of the proposed method in achieving desirable optimality and feasibility while maintaining low computational complexity.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":"10 1","pages":"51-65"},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375977","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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