2022 12th International Conference on Power, Energy and Electrical Engineering (CPEEE)最新文献_第7页

Design of High Voltage and Large Current Trigger Source for Trigger Vacuum Switch 触发真空开关高压大电流触发源的设计

2022 12th International Conference on Power, Energy and Electrical Engineering (CPEEE) Pub Date : 2022-02-25 DOI: 10.1109/CPEEE54404.2022.9738686

Xue Liu, H. Ding

{"title":"Design of High Voltage and Large Current Trigger Source for Trigger Vacuum Switch","authors":"Xue Liu, H. Ding","doi":"10.1109/CPEEE54404.2022.9738686","DOIUrl":"https://doi.org/10.1109/CPEEE54404.2022.9738686","url":null,"abstract":"The field breakdown type triggered vacuum switch has been widely used in the field of pulse power technology because of its advantages of high rated voltage and high rated current. However, compared with the surface breakdown type triggered vacuum switch, the field breakdown type triggered vacuum switch has higher requirements on the trigger pulse. The widely used trigger source has low trigger voltage steepness and low trigger current, leading to large trigger delay and discreteness. Therefore, the design of a high voltage and high current trigger source based on full wave voltage doubling rectifier circuit and low variable ratio pulse transformer is crucial for the field breakdown type TVS. The trigger source first charges high voltage to the energy storage capacitor through the voltage doubling rectifier circuit, and then discharges to the trigger gap through the low-variable ratio pulse transformer. Thus high steep trigger voltage and large trigger current pulse can be obtained. This paper introduces the working principle of the trigger source, analyzes and designs the voltage doubling rectifier circuit and pulse transformer in detail, and verifies the feasibility and effectiveness of the design scheme through experiments.","PeriodicalId":438265,"journal":{"name":"2022 12th International Conference on Power, Energy and Electrical Engineering (CPEEE)","volume":"25 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":"125627672","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

Optimal Operation for Smart City with Model Predictive Control 基于模型预测控制的智慧城市最优运行

2022 12th International Conference on Power, Energy and Electrical Engineering (CPEEE) Pub Date : 2022-02-25 DOI: 10.1109/CPEEE54404.2022.9738664

Tetsuya Yabiku, Kanato Tamashiro, Hiroshi Takahashi, Senjyu Tomonobu

引用次数: 0

HVDC Fault detection and Identification in monopolar topology using deep learning 基于深度学习的HVDC单极拓扑故障检测与识别

2022 12th International Conference on Power, Energy and Electrical Engineering (CPEEE) Pub Date : 2022-02-25 DOI: 10.1109/CPEEE54404.2022.9738716

Sarala Mohan Naidu

{"title":"HVDC Fault detection and Identification in monopolar topology using deep learning","authors":"Sarala Mohan Naidu","doi":"10.1109/CPEEE54404.2022.9738716","DOIUrl":"https://doi.org/10.1109/CPEEE54404.2022.9738716","url":null,"abstract":"Fault detection, identification(classification), and isolation are very important to ensure continuous power transmission in a power grid system. Fast and accurate methods are thus very critical for fault diagnosis in power systems. Many research papers attempted different learning methods in this area, and the focus was either on a subset of the aspects or only on the critical asset faults. The key results from some of the relevant ones are taken up for comparison. This paper describes the novel technical results in detecting and identifying all types of AC and DC faults in the HVDC station by using a fully convolutional neural network (FCNN) deep learning algorithm. The performance is evaluated with an experiment on symmetrical monopolar HVDC station simulated in Power Systems Computer-Aided Design (PSCAD). The novel significance of the results includes applying the learned knowledge from one station to validate on the other station data, the quick time to detect and identify faults, the confusion matrix, classification reports with probability of 99.24% for detection and 97.73% for identification, False alarm rate of 1.35%, and zero percent missed faults. The adaptability of the trained model from the learned knowledge to schematically related HVDC stations is discussed.","PeriodicalId":438265,"journal":{"name":"2022 12th International Conference on Power, Energy and Electrical Engineering (CPEEE)","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":"125536897","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

Optimal Control Strategy of Temperature Control Equipment Based on CPS System 基于CPS系统的温控设备最优控制策略

2022 12th International Conference on Power, Energy and Electrical Engineering (CPEEE) Pub Date : 2022-02-25 DOI: 10.1109/CPEEE54404.2022.9738689

Chaoliang Wang, Wei Liu, Yabin Du, Bin Li

引用次数: 0