2022 International Conference on Power Energy Systems and Applications (ICoPESA)最新文献_第10页

Thickness Optimization for Capacitance Core of High Voltage Transformer Bushing Based on Finite Element Method 基于有限元法的高压变压器套管电容铁芯厚度优化

2022 International Conference on Power Energy Systems and Applications (ICoPESA) Pub Date : 2022-02-25 DOI: 10.1109/ICoPESA54515.2022.9754462

Hai-Yue Yang, Jia Xie, Xiaoyu Hu, Chenchen Zhang, G. Ding, Xingwang Wu, R. Yin, Jielong Wu

引用次数: 0

A Simple 3-D Analytical Modeling Method and Sensitivity Analysis for the Axial-Flux Permanent Magnet Eddy Current Coupler 轴流式永磁涡流耦合器的简单三维解析建模方法及灵敏度分析

2022 International Conference on Power Energy Systems and Applications (ICoPESA) Pub Date : 2022-02-25 DOI: 10.1109/ICoPESA54515.2022.9754470

Wenhui Li, Dazhi Wang, T. Tong, Deshan Kong, Sihan Wang, Zhong Hua

引用次数: 0

Global Configuration Strategy of SAPFs Combined with Comprehensive Evaluation of Power Quality in Distribution Network 结合配电网电能质量综合评价的sapf全局配置策略

2022 International Conference on Power Energy Systems and Applications (ICoPESA) Pub Date : 2022-02-25 DOI: 10.1109/ICoPESA54515.2022.9754415

Xiaoqing Yin, Hao Yi, Zebin Yang, F. Zhuo

引用次数: 2

Research on Application Setting Scheme of Distribution Line Non-communication Protection 配电线路非通信保护应用整定方案研究

2022 International Conference on Power Energy Systems and Applications (ICoPESA) Pub Date : 2022-02-25 DOI: 10.1109/ICoPESA54515.2022.9754398

Yiwen Liang, Shenxing Shi, Xinzhou Dong, Jinyu Chen

{"title":"Research on Application Setting Scheme of Distribution Line Non-communication Protection","authors":"Yiwen Liang, Shenxing Shi, Xinzhou Dong, Jinyu Chen","doi":"10.1109/ICoPESA54515.2022.9754398","DOIUrl":"https://doi.org/10.1109/ICoPESA54515.2022.9754398","url":null,"abstract":"Aiming at the actual situation of multiple feeders on the same bus and multiple branches on the lines in the distribution network, this paper proposes a setting scheme for the distribution line non-communication protection considering the influence of branch line faults and adjacent line faults. In the scheme, the mainline protection and branch line protection cooperate. Branch lines are equipped with quick break protection to ensure the unity of protection selectivity and rapidity. The action delay of non-communication undervoltage protection co-ordinates with overcurrent protection section I of adjacent line. The voltage setting value of non-communication undervoltage protection coordinates with the fault phase voltage variation on the bus in case of fault at the end of the protection range of overcurrent protection section I of adjacent line to solve the misoperation of undervoltage protection in case of adjacent line faults. Simulation results show that the proposed protection setting scheme is not affected by adjacent line faults, and the mainline faults and branch line faults are selective.","PeriodicalId":142509,"journal":{"name":"2022 International Conference on Power Energy Systems and Applications (ICoPESA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128932278","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 Power Grid Disturbance Signal Identification Method Based on Energy Difference 基于能量差的电网扰动信号识别方法研究

2022 International Conference on Power Energy Systems and Applications (ICoPESA) Pub Date : 2022-02-25 DOI: 10.1109/ICoPESA54515.2022.9754482

P. Ji, Quan Zhu

引用次数: 0

Research on Algorithm of Load Licker in Power System 电力系统负荷筛选器算法研究

2022 International Conference on Power Energy Systems and Applications (ICoPESA) Pub Date : 2022-02-25 DOI: 10.1109/ICoPESA54515.2022.9754444

H. Lei, Li Jiang, Ya Huang, G. Gao, J. Zhang

引用次数: 0

A Deep Reinforcement Learning Based Energy Management Strategy for Fuel-Cell Electric UAV 基于深度强化学习的燃料电池无人机能量管理策略

2022 International Conference on Power Energy Systems and Applications (ICoPESA) Pub Date : 2022-02-25 DOI: 10.1109/ICoPESA54515.2022.9754414

Q. Gao, T. Lei, Fei Deng, Zhihao Min, W. Yao, Xiaobin Zhang

{"title":"A Deep Reinforcement Learning Based Energy Management Strategy for Fuel-Cell Electric UAV","authors":"Q. Gao, T. Lei, Fei Deng, Zhihao Min, W. Yao, Xiaobin Zhang","doi":"10.1109/ICoPESA54515.2022.9754414","DOIUrl":"https://doi.org/10.1109/ICoPESA54515.2022.9754414","url":null,"abstract":"Electric propulsion UAV powered by hybrid power system consisting of fuel cells and lithium batteries have attracted significant attention for long endurance and zero emission. Different dynamic characteristics for variable power load demanding which can be stochastically affected by the UAV’s flight air dynamic disturbance are difficult to be modeled with energy management system (EMS). In this paper, a Deep Reinforcement Learning (DRL) algorithm, namely twin-delayed Deep Deterministic policy gradient (TD3), is adopted to derivate EMS for hybrid electric UAV which can avoid performance degradation from uncertainty of power system model and curse of dimensionality of traditional algorithm. The simulation results indicate that the TD3-based DRL strategy is able to coordinate multiple electric power sources based on their natural power characteristics, satisfy different flight profiles of UAV. Furthermore, the performances of TD3, Deep Q-Networks (DQN), Deep Deterministic policy gradient (DDPG) and Dynamic Programming (DP) algorithms with different parameters in EMS of hybrid electric UAV were compared and the effectiveness of the algorithm was verified by digital simulation. Comparative results also illustrate that the proposed TD3 method outperforms other two methods in solving multi-objective optimization energy management problem, in terms of hydrogen consumptions, system efficiency and battery’s state of charge (SOC) sustainability.","PeriodicalId":142509,"journal":{"name":"2022 International Conference on Power Energy Systems and Applications (ICoPESA)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125710579","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: 2

Application of Energy Storage System in Rail Transit: A Review 储能系统在轨道交通中的应用综述

2022 International Conference on Power Energy Systems and Applications (ICoPESA) Pub Date : 2022-02-25 DOI: 10.1109/ICoPESA54515.2022.9754481

Jianlin Li, Dixi Xin, Han Wang, Chang Liu

引用次数: 3

Leakage Detection and Identification of Power Plant Pipelines Based on Improved Faster RCNN 基于改进更快RCNN的电厂管道泄漏检测与识别

2022 International Conference on Power Energy Systems and Applications (ICoPESA) Pub Date : 2022-02-25 DOI: 10.1109/ICoPESA54515.2022.9754403

Danhao Wang, D. Peng, Dongmei Huang, Weiwei Liu, Erjiang Qi, Hongxun Lv

{"title":"Leakage Detection and Identification of Power Plant Pipelines Based on Improved Faster RCNN","authors":"Danhao Wang, D. Peng, Dongmei Huang, Weiwei Liu, Erjiang Qi, Hongxun Lv","doi":"10.1109/ICoPESA54515.2022.9754403","DOIUrl":"https://doi.org/10.1109/ICoPESA54515.2022.9754403","url":null,"abstract":"For steam and oil leak detection problems in power plant pipelines, the technology and system implementation based on CBAM-Faster RCNN are proposed. First, VGG16 is replaced by the ResNet101 structure in order to obtain rich semantic information of feature graph, which is the feature extraction network of CBAM-Faster RCNN. A lightweight attention mechanism module CBAM is added to the feature extraction network and the full connection layer is replaced by one-dimensional convolution. These are able to solve the problem that the feature information of small leakage objects is tend to lose. Next, the new anchor scale in RPN network is obtained based on Kernel K-means clustering method, which is suitable for corresponding to the scale of the leakage object. Then, the ROI Pooling structure in CBAM-Faster RCNN is replaced by ROI Align to improve the accuracy of leakage detection algorithm. The mAP value of CBAM-Faster RCNN network reaches 96.5 percent, which proves accurate detection of steam and oil leakage is realized. Finally, the pipeline leakage detection system based on CBAM-Faster RCNN is embedded into the platform of intelligent inspection robot in key areas of power plant and the practicability of this method is proved.","PeriodicalId":142509,"journal":{"name":"2022 International Conference on Power Energy Systems and Applications (ICoPESA)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129341817","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