2021 IEEE Sustainable Power and Energy Conference (iSPEC)最新文献_第9页

Optimal maintenance method for insulators based on online monitoring 基于在线监测的绝缘子优化维护方法

2021 IEEE Sustainable Power and Energy Conference (iSPEC) Pub Date : 2021-12-23 DOI: 10.1109/iSPEC53008.2021.9736091

Song Gao, Jianjun Liu, Gang Qiu, Shengzhen Yang, Xiujuan Xu

引用次数: 0

Safety-critical Eco-driving Strategy for Electric Vehicle at Signalized Intersection Using Control Barrier Function 基于控制屏障功能的信号交叉口电动汽车安全生态驾驶策略

2021 IEEE Sustainable Power and Energy Conference (iSPEC) Pub Date : 2021-12-23 DOI: 10.1109/iSPEC53008.2021.9736005

Haonan Ding, Haoxuan Dong, Weichao Zhuang, Haoji Liu, Guo-dong Yin, Zhihan Li

引用次数: 0

Joint optimal scheduling for EV-UAV system serving post-disaster rescue 服务于灾后救援的电动汽车-无人机系统联合优化调度

2021 IEEE Sustainable Power and Energy Conference (iSPEC) Pub Date : 2021-12-23 DOI: 10.1109/iSPEC53008.2021.9735807

Wenchao Bai, Mingfei Ban, Qiang Liu, Qichao Chen, Zhenjie Li, Yiqi Liu

引用次数: 0

Vulnerability Assessment For Power-Transportation Coupled Network Based On Topological Methods 基于拓扑方法的电输耦合网络脆弱性评估

2021 IEEE Sustainable Power and Energy Conference (iSPEC) Pub Date : 2021-12-23 DOI: 10.1109/iSPEC53008.2021.9735585

Liang Hong, Difei Tang, Chen Wei

引用次数: 1

Optimization Method of Spinning Reserve Capacity for Day-ahead Generation Scheduling Under the Constraints of Carbon Emission Limits 碳排放限制约束下日前发电调度旋转备用容量优化方法

2021 IEEE Sustainable Power and Energy Conference (iSPEC) Pub Date : 2021-12-23 DOI: 10.1109/iSPEC53008.2021.9735873

A. Alrashidi Abdullah Obid, Haoyong Chen

{"title":"Optimization Method of Spinning Reserve Capacity for Day-ahead Generation Scheduling Under the Constraints of Carbon Emission Limits","authors":"A. Alrashidi Abdullah Obid, Haoyong Chen","doi":"10.1109/iSPEC53008.2021.9735873","DOIUrl":"https://doi.org/10.1109/iSPEC53008.2021.9735873","url":null,"abstract":"In the context of power system with high proportion of wind power, the adjustable capacity of AGC units should be reserved to ensure real-time power balance when making day-ahead generation scheduling. In the framework with multi-power regulate segment, an analytical model of quantitative causality is presented for the problem how to reasonably arrange the upward reserve capacity and downward reserve capacity of AGC units in the day-ahead stage under the constraints of carbon emission limits. That regulating capacity not only ensures that the AGC unit can suppress the fluctuation of wind power and load, but also makes power system have a partial regulating margin, and robust optimization method is used for achieving the absolute security of these two aspects. Moreover, a new modeling method considering the fluctuation of wind power and load with upward reserve capacity and downward reserve capacity of AGC units is proposed, which reduces the number of constraints with random parameters and the complexity of the model. The simulation is carried out in modified IEEE 30-bus system with high proportion of wind power, which verifies the validity of the model.","PeriodicalId":417862,"journal":{"name":"2021 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114709159","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

Transformer Component Recognition in Pictures Taken by Submersible Robots 水下机器人拍摄的图像中变压器元件识别

2021 IEEE Sustainable Power and Energy Conference (iSPEC) Pub Date : 2021-12-23 DOI: 10.1109/iSPEC53008.2021.9735767

Yingjie Yan, Yadong Liu, Zhicheng Xie, J. Deng

引用次数: 0

Decentralised Optimal Operation Strategy for AC/DC Distribution Networks Considering Electric Vehicles 考虑电动汽车的交直流配电网络分散最优运行策略

2021 IEEE Sustainable Power and Energy Conference (iSPEC) Pub Date : 2021-12-23 DOI: 10.1109/iSPEC53008.2021.9735825

Wang Wenhua, Huang Ying, Zhang Yang, Gu Tao, Yan-Hung Wei, Zhou Qinglai, Pan Bo

{"title":"Decentralised Optimal Operation Strategy for AC/DC Distribution Networks Considering Electric Vehicles","authors":"Wang Wenhua, Huang Ying, Zhang Yang, Gu Tao, Yan-Hung Wei, Zhou Qinglai, Pan Bo","doi":"10.1109/iSPEC53008.2021.9735825","DOIUrl":"https://doi.org/10.1109/iSPEC53008.2021.9735825","url":null,"abstract":"With the development of Direct Current (DC) transmission technology and power electronics technology, traditional Alternating Current (AC) distribution networks are being gradually transformed into AC/DC hybrid distribution network with a significant zoning structure. This paper proposes a distributed optimal operation strategy for AC/DC distribution networks by considering electric vehicles (EVs). First, Monte Carlo simulation is used to establish a mathematical model for EVs. Considering the differences between constituent districts and the controllability of EVs, appropriate charging start times of EVs are designated, and the objective function is to minimize the fluctuation of the total load curve. Subsequently, the chance-constrained method is applied to address the uncertainty of renewable energy and load. By implementing the coordinated dispatch of source, load, and storage, the optimal operation model of the AC/DC distribution network is established, based on the improved alternating direction multiplier method. The simulation results verify the effectiveness of the proposed distributed optimal operation strategy.","PeriodicalId":417862,"journal":{"name":"2021 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114917388","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

Short-term Load Forecasting By Multi-feature Iterative Learning Based on ISFS And XGBoost 基于ISFS和XGBoost的多特征迭代学习短期负荷预测

2021 IEEE Sustainable Power and Energy Conference (iSPEC) Pub Date : 2021-12-23 DOI: 10.1109/iSPEC53008.2021.9735434

Yajie Tang, Zhihao Li, Chouwei Ni, Diyang Gong, Wenjin Chen, Xuesong Zhang

{"title":"Short-term Load Forecasting By Multi-feature Iterative Learning Based on ISFS And XGBoost","authors":"Yajie Tang, Zhihao Li, Chouwei Ni, Diyang Gong, Wenjin Chen, Xuesong Zhang","doi":"10.1109/iSPEC53008.2021.9735434","DOIUrl":"https://doi.org/10.1109/iSPEC53008.2021.9735434","url":null,"abstract":"With the continuous development of smart grid and demand response technology, the electrical load gradually takes on an elastic, flexible, uncertain and controllable quality. In order to fully excavate the potential information behind the power load in smart grid and make use of it, the study of load feature analysis and load forecasting appear to be particularly important. Considering massive related data, machine learning algorithm based on big data analysis is the current mainstream method of establishing a forecasting model. It deeply mines the mapping relation between features and load. Feature selections on model training will directly affect the accuracy of short-term load forecasting. For making the most of massive data to improve the effect of feature selection, this paper proposes a short-term load forecasting method by multi-feature iterative learning based on ISFS (Improved Spanning-tree Forward Selection) and XGBoost (eXtreme Gradient Boosting). Under the framework of iterative learning, the proposed method uses ISFS algorithm to make better feature selection successively by iterations. And XGBoost algorithm evaluates each feature selection by cross validation results of training data set, thus precisely finding out the optimal multi-synergistic relationships among impact features and building differentiated models with distinct feature subsets. The method accumulates information gain by re-studies from the iterative load forecasting results, manages to improve the training effect and reduce the load forecasting errors step by step. The experimental results show that the proposed method has higher load forecasting accuracy compared with other typical methods.","PeriodicalId":417862,"journal":{"name":"2021 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117190630","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

Topology Optimization of Offshore Wind Farm Collection System Considering Smoothing Effect 考虑平滑效应的海上风电场集输系统拓扑优化

2021 IEEE Sustainable Power and Energy Conference (iSPEC) Pub Date : 2021-12-23 DOI: 10.1109/iSPEC53008.2021.9735509

X. Sixuan, Wang Quanquan, Zhao Feifei, Qiao Ying, Lu Zongxiang

{"title":"Topology Optimization of Offshore Wind Farm Collection System Considering Smoothing Effect","authors":"X. Sixuan, Wang Quanquan, Zhao Feifei, Qiao Ying, Lu Zongxiang","doi":"10.1109/iSPEC53008.2021.9735509","DOIUrl":"https://doi.org/10.1109/iSPEC53008.2021.9735509","url":null,"abstract":"In China, the offshore wind generation is developed in the mode of large-scale exploiting and centralized grid connection. More and more large-scale wind power bases are planning to construct in the future. Many bases are remote from load centers inversely and located at the weak end of the interconnected bulk power network, which lead to the severe wind power loss in these areas. Based on the characteristics analysis of the large-scale wind power, the systemic economic evaluation model of the offshore wind farm collection system is established, considering the smoothing effect of wind power, the construction cost of transmission projects, the compensation cost of wind power loss, as well as the benefit of transmitting wind power. The improved genetic algorithm is proposed to optimize the model. The efficiency of the algorithm is improved, and the complex constraints are deal with, such as the non-crossover of submarine cable, through optimizing the initial population, adopting the chain table code, and using an elite selection operator. The example of wind farm topology optimization indicates that the proposed algorithm has good optimization performance and convergence.","PeriodicalId":417862,"journal":{"name":"2021 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116260221","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

Intelligent diagnosis method of relay protection defects based on Bayesian theory 基于贝叶斯理论的继电保护缺陷智能诊断方法

2021 IEEE Sustainable Power and Energy Conference (iSPEC) Pub Date : 2021-12-23 DOI: 10.1109/iSPEC53008.2021.9735901

Guo Peng, Wang Limin, Yan Zhoutian, X. Ye

引用次数: 1