2021 3rd International Conference on Smart Power & Internet Energy Systems (SPIES)最新文献_第8页

Research on Power Factor Correcting Algorithm Based on Average Detection 基于平均值检测的功率因数校正算法研究

2021 3rd International Conference on Smart Power & Internet Energy Systems (SPIES) Pub Date : 2021-09-25 DOI: 10.1109/SPIES52282.2021.9633916

Ming Yan, Q. Song, Lukai Zhao

引用次数: 0

Research on Power Allocation Algorithm for Maximizing Energy Efficiency in Uplink NOMA System of 5G Smart Grid 5G智能电网上行NOMA系统能效最大化的功率分配算法研究

2021 3rd International Conference on Smart Power & Internet Energy Systems (SPIES) Pub Date : 2021-09-25 DOI: 10.1109/SPIES52282.2021.9633963

L. Chen, Li Peizhe, Xiao Zhenfeng, Wu Yefan, Liu Haotian, Di Xin, Zheng Jiayun

引用次数: 0

Research of Power Allocation Algorithm in the Distribution Side of Smart Grid Based on 5G Non Othogonal Multiple Access System 基于5G非正交多址系统的智能电网配电侧功率分配算法研究

2021 3rd International Conference on Smart Power & Internet Energy Systems (SPIES) Pub Date : 2021-09-25 DOI: 10.1109/SPIES52282.2021.9633910

Li Peizhe, Xiao Zhenfeng, Wu Yefan, Liu Haotian, Zheng Jiayun, Di Xin

{"title":"Research of Power Allocation Algorithm in the Distribution Side of Smart Grid Based on 5G Non Othogonal Multiple Access System","authors":"Li Peizhe, Xiao Zhenfeng, Wu Yefan, Liu Haotian, Zheng Jiayun, Di Xin","doi":"10.1109/SPIES52282.2021.9633910","DOIUrl":"https://doi.org/10.1109/SPIES52282.2021.9633910","url":null,"abstract":"5G communication technology has the advantages of large bandwidth, low delay, high reliability and service-oriented network architecture, which is a new wireless technology to solve the communication needs of smart grid. In order to solve the power allocation problem among smart grid services, this paper proposes a user grouping and power allocation algorithm based on non-orthogonal multiple access (NOMA) system. Firstly, the users of the base station are grouped, and we think of several users as a cluster, the channel gain between the base station and the users is input, and several users corresponding to the maximum channel gain are divided into a cluster. Then allocates the power of the users in the cluster, and power distribution is carried out among the clusters. The power distribution within the cluster follows the criterion of maximum fairness, and the power distribution between the clusters adopts the idea of iteration. By constantly adjusting the maximum power value of the cluster and the minimum power value of the cluster, the optimal power distribution of the smart grid system can be obtained. MATLAB simulation results show that: under different conditions, the algorithm proposed in this paper makes the distribution result fairer, the user’s minimum rate is also improved compared with the algorithm proposed in other literatures.","PeriodicalId":411512,"journal":{"name":"2021 3rd International Conference on Smart Power & Internet Energy Systems (SPIES)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115166583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Electromagnetic Characteristics of Oil - based Fe₃O₄ Nanometer Two - phase Magnetic Fluid Transformer 油基Fe₃O₄纳米两相磁流体变压器电磁特性研究

2021 3rd International Conference on Smart Power & Internet Energy Systems (SPIES) Pub Date : 2021-09-25 DOI: 10.1109/SPIES52282.2021.9633929

Yongteng Jing, R. Sun, Yan Li

{"title":"Research on Electromagnetic Characteristics of Oil - based Fe₃O₄ Nanometer Two - phase Magnetic Fluid Transformer","authors":"Yongteng Jing, R. Sun, Yan Li","doi":"10.1109/SPIES52282.2021.9633929","DOIUrl":"https://doi.org/10.1109/SPIES52282.2021.9633929","url":null,"abstract":"Because of the high thermal conductivity of the nanometer two-phase magnetic fluid, it will be used as a new cooling medium for transformers to solve the problem of limited capacity of the transformer due to the temperature limit. However, the electromagnetic characteristics of the nanometer two-phase magnetic fluid transformer are rarely studied. Therefore, based on the electromagnetic field theory and the magnetic fluid characteristics (oil-based Fe3O4 nanometer two-phase magnetic fluid), the electromagnetic characteristics of the new transformer are analyzed and studied. Taking a 50MVA and 110kV transformer product as an example, calculated and analyzed the changes of internal magnetic field before and after using the nanometer two-phase magnetic fluid cooling material in this paper. By compared and analyzed the metal structures such as iron core, clip and tank and the magnetic field and eddy current field of magnetic fluid, the influence of magnetic fluid on electromagnetic characteristics of transformer is further demonstrated. The calculation and analysis show that the electromagnetic properties of the transformer are obviously changed by using the nanometer two-phase magnetic fluid as the cooling medium. The research can be applied to design of this kind of transformer.","PeriodicalId":411512,"journal":{"name":"2021 3rd International Conference on Smart Power & Internet Energy Systems (SPIES)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126825405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance Analysis of Regression and Artificial Neural Network Schemes for Dynamic Model Reduction of Power Systems 电力系统动态模型约简的回归与人工神经网络方案性能分析

2021 3rd International Conference on Smart Power & Internet Energy Systems (SPIES) Pub Date : 2021-09-25 DOI: 10.1109/SPIES52282.2021.9633912

Lahiru Aththanayake, Apel Mahmud, N. Hosseinzadeh, A. Gargoom

{"title":"Performance Analysis of Regression and Artificial Neural Network Schemes for Dynamic Model Reduction of Power Systems","authors":"Lahiru Aththanayake, Apel Mahmud, N. Hosseinzadeh, A. Gargoom","doi":"10.1109/SPIES52282.2021.9633912","DOIUrl":"https://doi.org/10.1109/SPIES52282.2021.9633912","url":null,"abstract":"The performance of regression and artificial neural network schemes is evaluated for dynamic model reduction of power systems. The evaluation criterion is based on the goodness of fit in each reduced model with respect to the original model. Multiple linear regression, polynomial regression, and support vector are used as regression models while a Feedforward Artificial Neural Network with different activation functions is used for comparison with regression models. All simulations are based on a simplified Australian 14 Generator model. Datasets for training and test sets are obtained by measuring boundary bus properties and power flowing through tie lines. The simulation results show that the artificial neural network outperforms the regression models in making a reduced model of the power system, but only related to the system responses corresponding to the contingencies that were used for training. However, they perform poorly for unknown contingencies. Research work is being continued by the authors to create better models by combining classical models with machine learning techniques.","PeriodicalId":411512,"journal":{"name":"2021 3rd International Conference on Smart Power & Internet Energy Systems (SPIES)","volume":" 46","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113952197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Novel Fault Recovery Strategy for Future Distribution Network based on Multi-objective Particle Swarm Optimization Algorithm 基于多目标粒子群优化算法的未来配电网故障恢复策略

2021 3rd International Conference on Smart Power & Internet Energy Systems (SPIES) Pub Date : 2021-09-25 DOI: 10.1109/SPIES52282.2021.9633812

Shu Liu, Xutong Hou, Chenxu Zhao, Liang Ji, Shuxin Tian, Xiangjing Su

{"title":"A Novel Fault Recovery Strategy for Future Distribution Network based on Multi-objective Particle Swarm Optimization Algorithm","authors":"Shu Liu, Xutong Hou, Chenxu Zhao, Liang Ji, Shuxin Tian, Xiangjing Su","doi":"10.1109/SPIES52282.2021.9633812","DOIUrl":"https://doi.org/10.1109/SPIES52282.2021.9633812","url":null,"abstract":"Fault recovery strategy plays a vital role in increasing the power system reliability and stability. The classic multi-objective evolutionary algorithm based on Pareto dominance criteria and crowding distance sorting method does not consider the preference of decision maker in the iterative process, which leads to the decline of convergence performance. For the problem, this paper proposes a novel fault recovery strategy based on the preference multi-objective particle swarm algorithm considering the reference vector. This method uses the reference vector to determine the preference area so as to effectively integrate the decision maker’s preference knowledge into the fault recovery plan design. As the multi-objective intelligence algorithm based on Pareto dominance does not consider the problem of decision-makers’ preference knowledge, the multi-objective discrete binary particle swarm algorithm is then introduced. Secondly, the individual solutions are selected through the v-dominance relationship according to the preferences of decision makers, and external files are maintained. Finally, the feasibility of the proposed method is verified through the 69-node distribution network.","PeriodicalId":411512,"journal":{"name":"2021 3rd International Conference on Smart Power & Internet Energy Systems (SPIES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130844504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Strategy of Power Retailer Considering the Deviation Penalty 考虑偏差惩罚的电力零售商策略

2021 3rd International Conference on Smart Power & Internet Energy Systems (SPIES) Pub Date : 2021-09-25 DOI: 10.1109/SPIES52282.2021.9633956

T. Hang, B. Zou

引用次数: 1

SPIES 2021 TOC

2021 3rd International Conference on Smart Power & Internet Energy Systems (SPIES) Pub Date : 2021-09-25 DOI: 10.1109/spies52282.2021.9633809

引用次数: 0

Diagnostic Analysis and Disintegrate Verified on the Defect of 220kV Transformer Winding Deformation 220kV变压器绕组变形缺陷的诊断分析与分解验证

2021 3rd International Conference on Smart Power & Internet Energy Systems (SPIES) Pub Date : 2021-09-25 DOI: 10.1109/SPIES52282.2021.9633802

Yang Yu, Guanqun Dong, K. Huang, Yan-long Zhao, Zhaoxuan Wang, Kai Wang

引用次数: 0

An Adaptive OCV-SOC Curve Selection Classifier for Battery State-of-Charge Estimation 一种用于电池电量状态估计的自适应OCV-SOC曲线选择分类器

2021 3rd International Conference on Smart Power & Internet Energy Systems (SPIES) Pub Date : 2021-09-25 DOI: 10.1109/SPIES52282.2021.9633968

Lingling Ju, Guangchao Geng, Q. Jiang, Yuzhong Gong, C. Qin

{"title":"An Adaptive OCV-SOC Curve Selection Classifier for Battery State-of-Charge Estimation","authors":"Lingling Ju, Guangchao Geng, Q. Jiang, Yuzhong Gong, C. Qin","doi":"10.1109/SPIES52282.2021.9633968","DOIUrl":"https://doi.org/10.1109/SPIES52282.2021.9633968","url":null,"abstract":"The State-of-Charge (SOC) estimation of lithium-ion batteries is crucial in battery management systems (BMS) for energy storage power stations. The open-circuit voltage (OCV)-SOC curve and the SOC estimation algorithm are important in SOC estimation. There exist two common OCV tests, including the low current OCV test and the incremental OCV test, for OCV-SOC curve acquirement, whose performances vary with different working conditions under $25^{circ}{C}$. To make SOC estimation more effective, the least squares support vector machines (LS-SVM) method is presented as an adaptive classifier to decide which OCV test should be applied. Besides, an adaptive square-root unscented Kalman filter (ASRUKF) algorithm is proposed to improve square-root unscented Kalman filter (SRUKF) algorithm by updating the noise covariance matrixes in real-time. Based on the Center for Advanced Life Cycle Engineering (CALCE) public data set of University of Maryland of the 18650 LFP battery under $0^{circ}{C}, 25^{circ}{C}$ and $45^{circ}{C}$, the SOC estimation algorithm based on adaptive OCV-SOC curve selection classifier is demonstrated to be precise, quick, robust and adaptive.","PeriodicalId":411512,"journal":{"name":"2021 3rd International Conference on Smart Power & Internet Energy Systems (SPIES)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116509721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1