Electric Power Systems Research最新文献_第2页

A decentralized energy management scheme for a DC microgrid with correlated uncertainties and integrated demand response 具有相关不确定性和综合需求响应的直流微电网分散式能源管理方案

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2024-09-25 DOI: 10.1016/j.epsr.2024.111093

{"title":"A decentralized energy management scheme for a DC microgrid with correlated uncertainties and integrated demand response","authors":"","doi":"10.1016/j.epsr.2024.111093","DOIUrl":"10.1016/j.epsr.2024.111093","url":null,"abstract":"<div><div>Secure operation of a DC microgrid with high penetration of renewables and electric vehicle load is challenging. This paper proposes a decentralized energy management scheme for a grid-connected renewable integrated community DC microgrid considering the water-energy nexus. Uncertainties of renewable generation, plug-in electric vehicle load, electricity demand, electricity price in the day-ahead wholesale market, ambient temperature, and water demand are modelled using a probabilistic approach. Correlation between the input random variables is modelled using Copula theory. Flexibilities on the consumer side across multiple entities (temperature-dependent loads, electric vehicles, and water supply system storage) are coordinated to form an “integrated demand response entity”, which is further coordinated with the DC microgrid operator side flexibility (electrochemical storage) to support system operation. A distributed dynamic pricing scheme is used to implement the integrated demand response program. The objectives of the energy management scheme are to minimize the DC microgrid operator’s operating cost and consumers’ electricity cost. The decentralized algorithm is solved by the “alternating direction method of multipliers”. Simulation studies on a six-bus DC microgrid test system demonstrate that the proposed strategy reduces the operating cost of the DC microgrid operator by <span><math><mrow><mo>∼</mo><mn>19</mn><mo>.</mo><mn>28</mn><mtext>%</mtext></mrow></math></span> and the electricity cost of the consumers by <span><math><mrow><mo>∼</mo><mn>13</mn><mo>.</mo><mn>82</mn><mtext>%</mtext></mrow></math></span>.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142318787","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

A novel energy management of public charging stations using attention-based deep learning model 利用基于注意力的深度学习模型对公共充电站进行新型能源管理

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2024-09-24 DOI: 10.1016/j.epsr.2024.111090

{"title":"A novel energy management of public charging stations using attention-based deep learning model","authors":"","doi":"10.1016/j.epsr.2024.111090","DOIUrl":"10.1016/j.epsr.2024.111090","url":null,"abstract":"<div><div>Electricity grids are complex systems that must balance the supply and demand of electricity in real-time. However, with the increasing adoption of electric vehicles (EVs), managing the grid’s stability has become more challenging. EV charging can cause spikes in electricity demand, leading to peak demand periods that strain the power grid’s infrastructure. With the help of load forecasting, this effect on the grid can be mitigated by predicting the charging demand of electric vehicles in advance. This will help utilities adjust their energy supply in real-time, ensuring enough energy is available to meet demand, and preventing overloads or under utilization of the grid. Moreover, the EV charging demand is influenced by a wide range of factors, including charging station locations, weather, and time of day. Therefore, advanced deep learning models are required to learn these complex relationships and identify patterns in EV charging demand, enabling utilities to make more informed decisions. In this research, an attention-based deep learning approach is proposed for more accurate prediction of EV load demand. This novel approach integrates attention mechanisms with traditional deep learning models like LSTM and GRU, allowing the model to dynamically weight the importance of different features and focus on the most relevant information. The outcomes are compared to conventional deep learning and machine learning algorithms. To test the efficacy of the proposed framework, an actual ACN dataset for public EV charging stations is utilized.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378779624009751/pdfft?md5=da6e0cd18feb674eaf7cd88b3244adb7&pid=1-s2.0-S0378779624009751-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315059","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

Comments on the high-frequency response of hemispherical grounding electrodes with emphasis on magnetic induction effects 对半球形接地电极高频响应的评论，重点是磁感应效应

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2024-09-24 DOI: 10.1016/j.epsr.2024.111100

{"title":"Comments on the high-frequency response of hemispherical grounding electrodes with emphasis on magnetic induction effects","authors":"","doi":"10.1016/j.epsr.2024.111100","DOIUrl":"10.1016/j.epsr.2024.111100","url":null,"abstract":"<div><div>Accurate calculation of electrodes’ impedance is of major importance for the operation and protection of overhead power lines whose towers are periodically bonded to earth via grounding electrodes; particularly when the towers are hit by lightning discharges. A recent paper has proposed a simple RC-circuit to model the frequency response of hemispherical electrodes which predicts a continuous decrease to zero in electrode impedance as the frequency increases. The prediction agrees with the model, but the model is not sound and, therefore, the prediction is disputable. We show that the model is inadequate because it is founded on the conviction that the magnetic field is zero everywhere inside the soil <sup>___</sup>which we claim is a mistake. We employ a full-wave FEM solver to recalculate the numerical results presented in that paper and observed a notable disagreement for high frequencies. In addition, we also show how the problem of calculating the impedance of hemispherical grounding systems can be solved, encompassing displacement-current effects and magnetic-field effects.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378779624009854/pdfft?md5=e44ae86fce0890d1c0b95ce7695fae23&pid=1-s2.0-S0378779624009854-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315060","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

Enhancing the maximum or flexible power point tracking control of a photovoltaic array with a non-invasive and computationally robust model-based method for partial shading detection 利用基于模型的非侵入式、计算稳健的部分遮光检测方法，加强光伏阵列的最大或灵活功率点跟踪控制

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2024-09-24 DOI: 10.1016/j.epsr.2024.111096

{"title":"Enhancing the maximum or flexible power point tracking control of a photovoltaic array with a non-invasive and computationally robust model-based method for partial shading detection","authors":"","doi":"10.1016/j.epsr.2024.111096","DOIUrl":"10.1016/j.epsr.2024.111096","url":null,"abstract":"<div><div>The objective of this paper is to devise an efficient partial shading detection (PSD) scheme so that the PSD may not degrade the speed of power tracking of a photovoltaic array, while retaining the ability of accurately differentiating uniform irradiance from partial shading. A model-based approach is chosen to make PSD very accurate without using any expensive infrastructure. It is also ensured that the proposed PSD does not affect the power tracking dynamics when there is no partial shading, which is not true for some existing PSD schemes. The new PSD scheme is developed by deploying only one pair of irradiance and temperature sensors. In order to make PSD fault-tolerant to a sensor failure, provisions are kept for estimating the relevant irradiance or temperature quantity when the corresponding physical measurement is unavailable. A novel peak-power environmental dependence model is further derived to estimate the cell temperature without any convergence issue. Apart from the irradiance sensor failure, the issue of the irradiance sensor shading is also suitably addressed in the proposed dual-sensor non-invasive fault-tolerant (DSNIFT) PSD. Detailed simulation and experimental studies are performed to verify the improvement of PSD accuracy and power tracking speed by deploying the proposed DSNIFT methodology.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378779624009817/pdfft?md5=7bcf31514cea1d0e496036f169941602&pid=1-s2.0-S0378779624009817-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315058","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

EV load forecasting using a refined CNN-LSTM-AM 使用改进的 CNN-LSTM-AM 进行电动汽车负荷预测

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2024-09-23 DOI: 10.1016/j.epsr.2024.111091

{"title":"EV load forecasting using a refined CNN-LSTM-AM","authors":"","doi":"10.1016/j.epsr.2024.111091","DOIUrl":"10.1016/j.epsr.2024.111091","url":null,"abstract":"<div><div>Electric vehicle (EV) load forecasting is becoming increasingly important for power system operation. Accurately multi-step-ahead forecasting EV loads is challenging. The correlation between the series at different time intervals and the key points in forecasting the time series will affect the results of EV load forecasting. Therefore, in this paper, a method is presented for the combination of time series of different length intervals into a hybrid CNN-LSTM-AM model for multi-step-ahead forecasting. The input matrix consists of combining time series of different lengths. A designed CNN network with a one-dimensional convolutional structure is used to extract features. After the convolutional layer, the temporal features remain. Finally, LSTM Encoder-Decoder and Attention Mechanism (AM) are combined to solve the problem of forgetting multi-step-ahead forecasting. Through the validation of the public ACN-data, it is demonstrated that the proposed method achieve accurate prediction results. According to error metrics, MAE, RMSE and R<sup>2</sup> outperform other models with a value of 0.5268, 0.9519 and 0.9138 respectively. The maximum number of multi-step-ahead prediction reaches 96 steps. This provides a reference for longer multi-step predictions in the future. It is also confirmed in the ACN-data that the accuracy of the hybrid model is better than the single model in EV load prediction.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378779624009763/pdfft?md5=063a37546ac2cd8bdcf5797812985c6c&pid=1-s2.0-S0378779624009763-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311440","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

Optimal sizing of islanded microgrid using pelican optimization algorithm for Kutubdia Island of Bangladesh 使用鹈鹕优化算法为孟加拉国库图布迪亚岛优化孤岛微电网规模

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2024-09-23 DOI: 10.1016/j.epsr.2024.111088

{"title":"Optimal sizing of islanded microgrid using pelican optimization algorithm for Kutubdia Island of Bangladesh","authors":"","doi":"10.1016/j.epsr.2024.111088","DOIUrl":"10.1016/j.epsr.2024.111088","url":null,"abstract":"<div><div>This study proposes an optimal design approach, based on the Pelican Optimization Algorithm (POA), to configure the optimal sizing of design variables on an islanded microgrid: photovoltaic (PV) modules, wind turbines (WT), diesel generators (DG), and batteries in Kutubdia, Bangladesh based on optimal life cycle cost (LCC) and cost of energy (COE). Additionally, the economic analysis of three independent battery technologies, notably lead acid, lithium-ion, and nickel-iron are carried out to find the economically feasible technology, to ensure uninterrupted power supply. Moreover, reliability and sensitivity analyses of the optimized microgrid using POA were conducted for various reliability indices and variable interest rates. Results show that proposed POA method provides the optimal island microgrid configuration with lead acid (LA) batteries (PV/WT/LA/DG) based on a minimum LCC of $8334901, COE of 0.1080$/KWh and greenhouse gas emission amount of 19664 kgs/year. Furthermore, the outcomes generated by the POA are compared with genetic algorithm, particle swarm optimization, moth flame optimization algorithm, whale optimization algorithm and grey wolf optimization. It is found that POA method achieves more competitive results compared to other optimization techniques due to its ability to adjust parameters, fast convergence speed, and straightforward computations.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378779624009738/pdfft?md5=a021acb9d25dcb7bf52a29a6138f5ac9&pid=1-s2.0-S0378779624009738-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311442","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

Harmonic management of wind–storage cogeneration systems based on inductive filtering technology 基于感应滤波技术的风力蓄能热电联产系统谐波管理

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2024-09-23 DOI: 10.1016/j.epsr.2024.111101

{"title":"Harmonic management of wind–storage cogeneration systems based on inductive filtering technology","authors":"","doi":"10.1016/j.epsr.2024.111101","DOIUrl":"10.1016/j.epsr.2024.111101","url":null,"abstract":"<div><div>Numerous power electronic devices such as converters in wind–storage cogeneration systems introduce harmonic currents owing to their inherent nonlinear characteristics, considerably affecting the power quality of these cogeneration systems at grid connection points. To address this problem, a harmonic control method is proposed using induction filtering for wind–storage cogeneration systems. Next, a new topology is designed using an induction filter–based step-up transformer as the core equipment of these systems to overcome the shortcomings of traditional harmonic suppression methods. Subsequently, a mathematical model and the equivalent circuit of the induction filter–based step-up transformer are established, and the filtering mechanism and operating characteristics of the transformer are discussed. Furthermore, a simulation model is developed in the MATLAB system, and the filtering performance is simulated and analyzed under different operating conditions to verify the effectiveness of the proposed topology for harmonic suppression.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378779624009866/pdfft?md5=d3960b0f28e335eda2c9d9bd5911adcb&pid=1-s2.0-S0378779624009866-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311441","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

A scalable method with synchronous parallelization for computing selected eigenvalues of large-scale power system model 计算大规模电力系统模型选定特征值的同步并行化可扩展方法

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2024-09-23 DOI: 10.1016/j.epsr.2024.111085

{"title":"A scalable method with synchronous parallelization for computing selected eigenvalues of large-scale power system model","authors":"","doi":"10.1016/j.epsr.2024.111085","DOIUrl":"10.1016/j.epsr.2024.111085","url":null,"abstract":"<div><div>For small signal stability analysis of power systems, computing eigenvalues of the state space model is a widely used method, but still worthy of study due to computational issues in practical application. For large-scale power systems, high dimension of state space model, unknown distribution of eigenvalues and requirement on computing speed make computation of eigenvalues a challenging task. A computationally efficient method is proposed with utilizing synchronous parallelization for finding eigenvalues of concern in a flexibly specified area on the <em>s</em>-plane. The designed parallel framework achieves exact workload balance among computing units, which is attributed to the kernel eigenvalue solver implemented by Krylov–Schur factorization with fixing subspace dimension and discarding restart process. Satisfactory parallel speedup and numerical stability are obtained. Reliability for finding all target eigenvalues and parallel scalability of the parallelization are validated by numerical experiments on three power systems with different scales.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378779624009702/pdfft?md5=e12b3f4b95b930fa256bae5c91bff938&pid=1-s2.0-S0378779624009702-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311444","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

Deep learning-based post-disaster energy management and faster network reconfiguration method for improvement of restoration time 基于深度学习的灾后能源管理和更快的网络重构方法可缩短恢复时间

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2024-09-23 DOI: 10.1016/j.epsr.2024.111081

{"title":"Deep learning-based post-disaster energy management and faster network reconfiguration method for improvement of restoration time","authors":"","doi":"10.1016/j.epsr.2024.111081","DOIUrl":"10.1016/j.epsr.2024.111081","url":null,"abstract":"<div><div>Natural disasters in the world often cause power outages. To improve post-disaster response and restoration time, a method for energy management and network reconfiguration in emergencies has been proposed, where energy storages systems (ESSs) in distribution networks are used to support the system balance immediately and then the network reconfiguration is accelerated based on deep learning techniques. First, when power failures occur, the outputs of ESSs are calculated optimally in terms of the proposed optimization model under the constraints of minimizing line losses. After the injections of ESSs in emergencies, the network reconfiguration is still needed due to the limitation of ESS capacities. To avoid calculations of power flow repeatedly, a deep-learning based reconfiguration method for distribution networks has been proposed, where the deep-learning model is trained offline and can output the parameters of power flow immediately when a state combination of distribution generators (DGs), loads and power lines, which is an effective way to accelerate the network reconfiguration. If many reconfiguration schemes are found, a decision-making method with preferences are used to select one according to different situations. Finally, cases are designed, and simulation results show that the calculation time of a reconfiguration schemes are reduced significantly by almost one hundred multiples.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378779624009660/pdfft?md5=387754fbfe06c33cee7f788d539f2ad6&pid=1-s2.0-S0378779624009660-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311443","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

Automatic fault detection and stability management using intelligent hybrid controller 利用智能混合控制器进行故障自动检测和稳定性管理

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2024-09-23 DOI: 10.1016/j.epsr.2024.111075

{"title":"Automatic fault detection and stability management using intelligent hybrid controller","authors":"","doi":"10.1016/j.epsr.2024.111075","DOIUrl":"10.1016/j.epsr.2024.111075","url":null,"abstract":"<div><div>Microgrid allows the integration of remote sources and other flexible loads to raise security concerns. Thus, it is necessary to detect the type of fault to maintain the system's stability. Existing fault detection systems include limitations such as high detection times, inability to process noisy data and discretization issues. To address these issues, a spiking neural network with a self-organizing map is used to produce precise synaptic weights for fault detection in the microgrid. A feature exploration-based spiking neural network can accurately classify faults such as line-to-ground (LG), line-to-line (LL), double line-to-ground (DLG), and three-phase ground (TLG). To mitigate the impact of the fault, a voltage deviation estimation-based control method is used, which employs a three-degree of freedom fractional order proportional integral resonant (3DOF-FOPIR) controller. In order to stabilize the system frequency, the controller sends a control signal to the multi-level inverter based on the measured voltage deviation and fault auxiliary value. This ensures reduced distortions at the output voltage, and thus, it maintains the stability of the microgrid. As a result, when compared to graph-based convolution networks, the proposed method has a higher accuracy of 99.8 % and a lower error in system stability of 55.47 %.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S037877962400960X/pdfft?md5=f03317cd014f1101b76f8596f53de8db&pid=1-s2.0-S037877962400960X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311439","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