智能电网(汉斯)最新文献_第2页

Research and Development of New Energy Microgrid Charging Simulation Platform 新能源微电网充电仿真平台的研究与开发

智能电网(汉斯) Pub Date : 2022-10-22 DOI: 10.1109/ICSGSC56353.2022.9963028

Wang Huanning, Huang Yan, Tan Hengyu, Yao Hejun, Zhang Han, Zhao Zhihua, Liu Yuan

引用次数: 0

Sound Diagnosis Method of Power Transformer Discharge Fault Based on CEEMDAN-SVM 基于CEEMDAN-SVM的电力变压器放电故障可靠诊断方法

智能电网(汉斯) Pub Date : 2022-10-22 DOI: 10.1109/ICSGSC56353.2022.9962992

Tianwen Zheng, Zhen Xu, Tieiun Ma, S. Mei, Lei Pan

{"title":"Sound Diagnosis Method of Power Transformer Discharge Fault Based on CEEMDAN-SVM","authors":"Tianwen Zheng, Zhen Xu, Tieiun Ma, S. Mei, Lei Pan","doi":"10.1109/ICSGSC56353.2022.9962992","DOIUrl":"https://doi.org/10.1109/ICSGSC56353.2022.9962992","url":null,"abstract":"The sound signal characteristics of the power transformer discharge fault is the key to reliable identification of the power transformer discharge fault. However, the sound signal of the power transformer discharge fault is non-stationary and susceptible to environmental interference. Therefore, this paper proposed a sound diagnosis method for the power transformer discharge fault based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and support vector machine (SVM). First, the CEEMDAN was used to decompose the power transformer's sound signal, thus a series of intrinsic mode functions (IMF) that reflect the sound signal's local properties could be obtained. The kurtosis of each IMF was solved to select the suitable IMF components for signal reconstruction and denoising. Secondly, the reconstructed signal is decomposed by CEEMDAN, and the singular spectral entropy and marginal spectral entropy are extracted to form the eigenvector. Finally, the discharge fault of the power transformer is classified and identified by SVM. The simulation results demonstrate that the proposed method can obtain a recognition rate of more than 80% of discharge fault when the power transformer noise interference is taken into account and that it may be utilized to identify and diagnose the power transformer discharge defects.","PeriodicalId":66748,"journal":{"name":"智能电网(汉斯)","volume":"49 1","pages":"88-92"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72574116","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

Location-Based Crowdsensing Technologies for Planning of Public Transport and other Smart City Solutions 基于位置的大众感知技术用于公共交通规划和其他智慧城市解决方案

智能电网(汉斯) Pub Date : 2022-10-22 DOI: 10.1109/ICSGSC56353.2022.9963104

Alena Plášilová, J. Procházka

引用次数: 0

Research on Data Leakage Prevention Technology in Power Industry 电力行业数据泄漏预防技术研究

智能电网(汉斯) Pub Date : 2022-10-22 DOI: 10.1109/ICSGSC56353.2022.9963127

Jing Ma, Yonggang Xu, Jinqiang Fan, Xiaobin Yao, Yaming Cao, Chen Zheng

引用次数: 0

Stochastic Scheduling of a Wind-Photovoltaic-Hydro Complementary System Using Stochastic Dual Dynamic Programming Method 基于随机对偶动态规划方法的风-光电-水电互补系统随机调度

智能电网(汉斯) Pub Date : 2022-10-22 DOI: 10.1109/ICSGSC56353.2022.9963011

Chang Liu, Fang-Ru Liu, Ruiguang Ma, Yufan Chen, Li Pan, X. Xu

引用次数: 1

Evaluation the Development of China's Regional Logistics Industry Using Cluster Principal Component Analysis and Entropy Weight Method 基于聚类主成分分析和熵权法的中国区域物流业发展评价

智能电网(汉斯) Pub Date : 2022-10-22 DOI: 10.1109/ICSGSC56353.2022.9963078

Xiqiao Lin, Chunli Zhou, Chunhao Lu

引用次数: 0

A Microgrid Management System Based on Metaheuristics Particle Swarm Optimization 基于元启发式粒子群优化的微电网管理系统

智能电网(汉斯) Pub Date : 2022-10-22 DOI: 10.1109/ICSGSC56353.2022.9963000

Haotian Deng, Jing Jiang, Haiya Qian, Hongjian Sun

引用次数: 0

Research on Carbon Measurement Models of Substations 变电站碳计量模型研究

智能电网(汉斯) Pub Date : 2022-10-22 DOI: 10.1109/ICSGSC56353.2022.9963116

Hengjing He, Shangli Zhou, Leping Zhang, Ziang He, Bensong Zhang, Y. Gu, Chenyu Liu, Xiangui Xiao

{"title":"Research on Carbon Measurement Models of Substations","authors":"Hengjing He, Shangli Zhou, Leping Zhang, Ziang He, Bensong Zhang, Y. Gu, Chenyu Liu, Xiangui Xiao","doi":"10.1109/ICSGSC56353.2022.9963116","DOIUrl":"https://doi.org/10.1109/ICSGSC56353.2022.9963116","url":null,"abstract":"In recent years, the Chinese government has focused on doing a good job of “peak carbon dioxide emissions” and “carbon neutrality” work. In order to answer the call for national green development and transformation, an accurate carbon measurement in power grid enterprises is the basic premise to accurately grasp the trend of the carbon emission and effectively carry out various carbon emission reduction work. In the power grid, substations, whose loss can account for 50% of the overall grid loss, have a wide range of operating equipment and some equipment also has emissions or leakages of sulfur hexafluoride gas. Therefore, in order to improve the carbon measurement of substations, the main work of this paper is as follows. The carbon emission mechanism of substations based on the composition of substations is analyzed in depth. We focus on the carbon emissions generated by various energy consumption of substations and the leakage of $SF_{6}$ gas based on the carbon emission accounting method of power grid enterprises issued by the National Development and Reform Commission and the relationship between the energy consumption and carbon emissions. Then, carbon measurement models of substations based on the measurement data are established. And we also calculate the carbon emissions of the substation, taking a 500kV GIS substation of China Southern Power Grid as an example. This paper provides a more comprehensive research on carbon measurement models of substations by analyzing the carbon emission generated by the energy consumption of substations and the leakage of gases. Therefore, it provides support for clarifying the distribution of substation carbon emissions, carrying out carbon emission reduction and promoting the transformation of the green development of power systems.","PeriodicalId":66748,"journal":{"name":"智能电网(汉斯)","volume":"94 1","pages":"37-42"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79106551","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 Power Quality Data Transmission Scheme for the Distribution Network 一种配电网电能质量数据传输方案

智能电网(汉斯) Pub Date : 2022-10-22 DOI: 10.1109/ICSGSC56353.2022.9963131

Y. Lei, Li Yinliang, Xu Quan, Lin Xinhao, L. Jinyong, Y. Zhiyong, Cen Baoyi, Yang Donghai

引用次数: 0

Optimal Charging Power Tracking Control of Advanced Adiabatic Compressed Air Energy Storage over Wide Operation Ranges 先进绝热压缩空气储能系统大工作范围充电功率跟踪控制

智能电网(汉斯) Pub Date : 2022-10-22 DOI: 10.1109/ICSGSC56353.2022.9963019

Jiayu Bai, Wei Wei, S. Mei

引用次数: 0