2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2)最新文献_第8页

Detection of Inverter Leakage Current Based on Harmonic Extraction 基于谐波提取的逆变器漏电流检测

2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2) Pub Date : 2022-11-11 DOI: 10.1109/EI256261.2022.10117455

Zihu Wang, Yuanhong Liu, Hongqing Qu, Rui Li, Xu Zhou

引用次数: 0

Optimal Planning of Distributed Generation Based on African Vultures Optimization Algorithm 基于非洲秃鹫优化算法的分布式发电优化规划

2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2) Pub Date : 2022-11-11 DOI: 10.1109/EI256261.2022.10116298

Mintong Zhao, Jia-jia Huan, Xin Huang, Tao Yu, Qiaoyi Ding

{"title":"Optimal Planning of Distributed Generation Based on African Vultures Optimization Algorithm","authors":"Mintong Zhao, Jia-jia Huan, Xin Huang, Tao Yu, Qiaoyi Ding","doi":"10.1109/EI256261.2022.10116298","DOIUrl":"https://doi.org/10.1109/EI256261.2022.10116298","url":null,"abstract":"With the continuous impact of environmental pollution, the traditional generator represented by fossil fuels is gradually being replaced by renewable energy generators. Different from the conventional optimal planning, which only includes photovoltaic (PV) and wind turbines. This paper adds the use of fuel cells (FC) and miniature steam turbines with PV systems and wind turbines, which can make up for the defect of fluctuation of its output power. In addition, a multi-objective model considering power loss, voltage profile, and investment cost is established and solved by the African vultures optimization algorithm (AVOA). Finally, the simulation is based on IEEE 33 and 69 node distribution network (DN) systems. The results show that AVOA has a fast convergence speed and strong optimization characteristics, and can improve voltage distribution after distributed generation (DG) access. The simulation results show that the power loss and voltage profile decreased by 57% and 58% at the IEEE 33 and 64% and 61% at the IEEE 69 node after being connected to DG.","PeriodicalId":413409,"journal":{"name":"2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128539594","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

Improving HVRT of Offshore Wind Farms by Bunding with Thermal Power Plants 通过与火电厂结合改善海上风电场HVRT

2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2) Pub Date : 2022-11-11 DOI: 10.1109/EI256261.2022.10116107

Yong Lin, Shuxin Tian, Hao Yu, Panpan Wang, Honglin Chen, Runbo Chen

引用次数: 1

Impacts of DGs in Active Distribution Network Considering Bi-Directional Power Flow and Carbon Emission Flow Theory 考虑双向潮流和碳排放流理论的有功配电网dg的影响

2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2) Pub Date : 2022-11-11 DOI: 10.1109/EI256261.2022.10117424

Zhaoyuan Chai, Xinwei Shen, Hongbin Sun

引用次数: 0

A Novel EMI Attack Exploiting the Control Vulnerability of Photovoltaic Inverters

2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2) Pub Date : 2022-11-11 DOI: 10.1109/EI256261.2022.10116254

Zihao Dan, Fengchen Yang, Kaikai Pan, Chen Yan, Xiaoyu Ji, Wenyuan Xu

{"title":"A Novel EMI Attack Exploiting the Control Vulnerability of Photovoltaic Inverters","authors":"Zihao Dan, Fengchen Yang, Kaikai Pan, Chen Yan, Xiaoyu Ji, Wenyuan Xu","doi":"10.1109/EI256261.2022.10116254","DOIUrl":"https://doi.org/10.1109/EI256261.2022.10116254","url":null,"abstract":"With the rapid development of distributed generation(DG), the penetration rate of photovoltaics(PVs) in the power grid continues to increase. Therefore, various security issues faced by PV inverters have also become a major challenge to ensure the stability of the power grid.Due to the complex and open environment of PVs, attackers are more likely to access key devices such as grid-tied inverters, thereby causing damage to the entire power grid. Taking advantage of the above characteristics, this paper proposes a novel electromagnetic interference(EMI) attack against inverters, instead of cyber attacks that have been explored well in many other research works. It injects a bias signal into the PV inverter sensor through EMI, so as to change the sensor value finally transmitted to the controller. An attacker can tamper with the sensor value in this way. This paper also analyzes the vulnerability in the inverter control algorithm, and exploits the vulnerability to cause damage to the inverter through EMI attacks.The attack was verified on a commercial inverter from Texas Instruments. We present the induced DC voltage offset corresponding to different EMI frequencies of different sensors to demonstrate that EMI attacks can independently control different sensors on the inverter. In addition, we also analyze the inverter control algorithm and the possible consequences of attacks, further evaluate the consequences of the attack through simulations (we do simulations instead of real device tests to ensure safety). And the results show that this novel attack can shut down/burn the PV inverter.","PeriodicalId":413409,"journal":{"name":"2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129143739","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

Secondary Battery Energy Storage System Based on Real-Time Synchronous Data 基于实时同步数据的二次电池储能系统

2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2) Pub Date : 2022-11-11 DOI: 10.1109/EI256261.2022.10116810

Yi Li, Tianyu Fang, Kun Wang, Xiaofeng Zhou, Li Xin, Runyu Cai

引用次数: 0

Analysis of an Overhead Line Coincidence Failure Event in HVDC Network and Strategy Optimization 高压直流电网架空线路重合故障分析及策略优化

2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2) Pub Date : 2022-11-11 DOI: 10.1109/EI256261.2022.10117380

Guangming Xin, Rui Chen, Tianxiao Huang, Xiaofei Wang, Xiang Ren

引用次数: 0

Migration and Reproduction Algorithm and Its Application in Hierarchical Coordinating Economic Dispatching of Multi-microgrid System 迁移再生算法及其在多微网系统分层协调经济调度中的应用

2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2) Pub Date : 2022-11-11 DOI: 10.1109/EI256261.2022.10116817

Renwu Yan, Yunzhang Liu

引用次数: 0

Research on the Technology Framework of Integrated Energy System and Recent Progress 综合能源系统技术框架研究与进展

2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2) Pub Date : 2022-11-11 DOI: 10.1109/EI256261.2022.10116729

Yajun Zou, Wei Wang, Dawei Wang, Yanbo Wang, Xiaojun Wang, Weitao Chen

引用次数: 0

Research on Prediction of Dynamic Current Carrying Capacity of 110kV oil Immersed Transformer 110kV油浸变压器动态载流能力预测研究

2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2) Pub Date : 2022-11-11 DOI: 10.1109/EI256261.2022.10117217

Aogang Hou, Xuzhu Dong, J. Ruan, Yongqing Deng, Chen Zhang, Qiaofeng Chen

{"title":"Research on Prediction of Dynamic Current Carrying Capacity of 110kV oil Immersed Transformer","authors":"Aogang Hou, Xuzhu Dong, J. Ruan, Yongqing Deng, Chen Zhang, Qiaofeng Chen","doi":"10.1109/EI256261.2022.10117217","DOIUrl":"https://doi.org/10.1109/EI256261.2022.10117217","url":null,"abstract":"In order to alleviate the problem of high short-term load rate in summer, improve the power transmission and transformation capacity of urban power system and improve the utilization rate of transformer equipment, the dynamic current carrying capacity prediction of 110kV oil immersed transformer is studied in this paper. This paper first calculates the hot spot temperature, top oil temperature and 24-hour life loss percentage of the transformer by using GB 1094.7. Secondly, the long-term and short-term memory neural network model (LSTM) is trained and verified. Compared with ARIMA model, it highlights the high accuracy and rapid convergence of LSTM, and predicts the power load data in the next 24 hours according to the historical data of power load in one month. Finally, set the three constraints of hot spot temperature limit, top oil temperature limit and life loss limit, calculate the normal, long-term and short-term current carrying capacity of oil immersed transformer according to the predicted power load data and real-time ambient temperature, and draw a conclusion: hot spot temperature is the limiting factor affecting the long-term and short-term load capacity of transformer, and the long-term current carrying capacity of this transformer is 1.61 times that of the original, The short-term current carrying capacity is 1.64 times of the original, which provides a certain reference for the power dispatching department.","PeriodicalId":413409,"journal":{"name":"2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130845952","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