Pub Date : 2020-09-12DOI: 10.1109/ICPRE51194.2020.9233129
Cheng Qiming, Zhao Miaozhen, S. Yuying, P. Peng, Zhai Denghui, Peng Daogang
To solve defects of the traditional Z-source topology, an enhanced three-level neutral-point-clamped quasi-Z-source inverter (3L NPC QZSI) topology is proposed in the paper. New topology combines the advantages of three-level neutral-point-clamped inverter with the advantages of quasi-z-source network. Compared with traditional topology, new topology can effectively enhance inverter boost capability, reduce the capacitor voltage stress and suppress start-up shock current. All the conclusion mentioned above have been confirmed by simulations and experiment.
{"title":"Three-Level Neutral-Point-Clamped Enhanced Quasi-Z-Source Inverter","authors":"Cheng Qiming, Zhao Miaozhen, S. Yuying, P. Peng, Zhai Denghui, Peng Daogang","doi":"10.1109/ICPRE51194.2020.9233129","DOIUrl":"https://doi.org/10.1109/ICPRE51194.2020.9233129","url":null,"abstract":"To solve defects of the traditional Z-source topology, an enhanced three-level neutral-point-clamped quasi-Z-source inverter (3L NPC QZSI) topology is proposed in the paper. New topology combines the advantages of three-level neutral-point-clamped inverter with the advantages of quasi-z-source network. Compared with traditional topology, new topology can effectively enhance inverter boost capability, reduce the capacitor voltage stress and suppress start-up shock current. All the conclusion mentioned above have been confirmed by simulations and experiment.","PeriodicalId":394287,"journal":{"name":"2020 5th International Conference on Power and Renewable Energy (ICPRE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125463220","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}
Pub Date : 2020-09-12DOI: 10.1109/ICPRE51194.2020.9233164
Huahui Chen, Hongke Tang, Yang Zhao
Based on a special signal acquisition card of industrial control system, the electrical performance and interface environment of the transmission signal in the card are analyzed. Combined with the transmission line theory and the induced overvoltage theory of transmission line, the protection circuit of surge protective device (SPD) for the signal line and power line of the acquisition card is designed. The simulation impact test of the designed SPD verifies the rationality of the design and the SPD's Reliability. The design idea of SPD for special industrial control signal acquisition card is proposed.
{"title":"Design and Test Analysis of Surge Protective Device Based on Special Industrial Control Signal Acquisition Card","authors":"Huahui Chen, Hongke Tang, Yang Zhao","doi":"10.1109/ICPRE51194.2020.9233164","DOIUrl":"https://doi.org/10.1109/ICPRE51194.2020.9233164","url":null,"abstract":"Based on a special signal acquisition card of industrial control system, the electrical performance and interface environment of the transmission signal in the card are analyzed. Combined with the transmission line theory and the induced overvoltage theory of transmission line, the protection circuit of surge protective device (SPD) for the signal line and power line of the acquisition card is designed. The simulation impact test of the designed SPD verifies the rationality of the design and the SPD's Reliability. The design idea of SPD for special industrial control signal acquisition card is proposed.","PeriodicalId":394287,"journal":{"name":"2020 5th International Conference on Power and Renewable Energy (ICPRE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115062049","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}
Pub Date : 2020-09-12DOI: 10.1109/ICPRE51194.2020.9233287
Li Wei, Huang Wei
This article mainly studies combustion instability. It analyzed the basic composition of the combustion chamber and gas turbine and the principle of combustion instability. In order to detect the abnormality of the combustion chamber pressure, the paper proposed a model based on the combination of Principal Component Analysis (PCA), Mind Evolutionary Algorithm (MEA) and Back Propagation Neural Network (BPNN). The model uses Pearson's rule and PCA to preprocess the data. Then it optimizes the back propagation neural network through the global optimization capability of MEA to obtain the optimal prediction curve. At last, the paper introduced similarity and set an early warning threshold to detect the limit alarm. After verification on the simulation platform of combined-cycle gas and steam turbine power plants and import real data for simulation, the results showed that the PCA-MEA-BPNN algorithm can handle non-linear problems well and detect abnormal combustion and issue an alarm.
{"title":"Detection Method of Combustion Instability in Combustion Chamber of Gas Turbine","authors":"Li Wei, Huang Wei","doi":"10.1109/ICPRE51194.2020.9233287","DOIUrl":"https://doi.org/10.1109/ICPRE51194.2020.9233287","url":null,"abstract":"This article mainly studies combustion instability. It analyzed the basic composition of the combustion chamber and gas turbine and the principle of combustion instability. In order to detect the abnormality of the combustion chamber pressure, the paper proposed a model based on the combination of Principal Component Analysis (PCA), Mind Evolutionary Algorithm (MEA) and Back Propagation Neural Network (BPNN). The model uses Pearson's rule and PCA to preprocess the data. Then it optimizes the back propagation neural network through the global optimization capability of MEA to obtain the optimal prediction curve. At last, the paper introduced similarity and set an early warning threshold to detect the limit alarm. After verification on the simulation platform of combined-cycle gas and steam turbine power plants and import real data for simulation, the results showed that the PCA-MEA-BPNN algorithm can handle non-linear problems well and detect abnormal combustion and issue an alarm.","PeriodicalId":394287,"journal":{"name":"2020 5th International Conference on Power and Renewable Energy (ICPRE)","volume":"18 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114032683","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}
Pub Date : 2020-09-12DOI: 10.1109/ICPRE51194.2020.9233147
Peng Zhang, Q. Feng, Ran Chen, Dalong Wang, Lijia Ren
There are a large number of power quality problems with large-capacity dedicated line users of the power grid. The monitoring data of power quality presents a large number of irregular characteristics. The signal samples obtained directly by measuring the signals contain a lot of complicated and useless information, and cannot be used for the type identification of special load power quality. Regarding this problem, this paper proposes a method for identifying the type of power quality disturbances with special loads based on S transform and support vector machine (SVM). Simulation and actual data experiments show that the method can accurately identify, and it is of great significance for grasping the features of power quality, as well as useful for the supervision, analysis and management of power quality.
{"title":"Classification and Identification of Power Quality in Distribution Network","authors":"Peng Zhang, Q. Feng, Ran Chen, Dalong Wang, Lijia Ren","doi":"10.1109/ICPRE51194.2020.9233147","DOIUrl":"https://doi.org/10.1109/ICPRE51194.2020.9233147","url":null,"abstract":"There are a large number of power quality problems with large-capacity dedicated line users of the power grid. The monitoring data of power quality presents a large number of irregular characteristics. The signal samples obtained directly by measuring the signals contain a lot of complicated and useless information, and cannot be used for the type identification of special load power quality. Regarding this problem, this paper proposes a method for identifying the type of power quality disturbances with special loads based on S transform and support vector machine (SVM). Simulation and actual data experiments show that the method can accurately identify, and it is of great significance for grasping the features of power quality, as well as useful for the supervision, analysis and management of power quality.","PeriodicalId":394287,"journal":{"name":"2020 5th International Conference on Power and Renewable Energy (ICPRE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124648631","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}
Pub Date : 2020-09-12DOI: 10.1109/ICPRE51194.2020.9233110
Behnaz Behi, A. Arefi, P. Jennings, A. Pivrikas, Arian Gorjy, J. Catalão
Virtual power plants (VPPs) are defined as an aggregator of different types of energy resources and flexibility, coordinated by VPP owner through a smart control system. A correct establishment of a VPP will result in reduced electricity costs for the consumers within the VPP. One of the key aspect of VPP’s success is the consumer engagement in order to manage their flexibilities effectively. Gamification is an efficient way of learning and engagement, which can efficiently change the behavior of consumers towards participating in programs provided by VPPs for energy cost reduction. In this paper, a gamification-based approach for consumer engagement is proposed and a methodology based on Fogg’s behavior model and Kim’s model on player types is developed to examine the suitability of available gamification applications for energy saving/efficiency in the context of a VPP. Seven gamification applications are analyzed and evaluated based on the developed methodology and the results are provided.
{"title":"Consumer Engagement in Virtual Power Plants through Gamification","authors":"Behnaz Behi, A. Arefi, P. Jennings, A. Pivrikas, Arian Gorjy, J. Catalão","doi":"10.1109/ICPRE51194.2020.9233110","DOIUrl":"https://doi.org/10.1109/ICPRE51194.2020.9233110","url":null,"abstract":"Virtual power plants (VPPs) are defined as an aggregator of different types of energy resources and flexibility, coordinated by VPP owner through a smart control system. A correct establishment of a VPP will result in reduced electricity costs for the consumers within the VPP. One of the key aspect of VPP’s success is the consumer engagement in order to manage their flexibilities effectively. Gamification is an efficient way of learning and engagement, which can efficiently change the behavior of consumers towards participating in programs provided by VPPs for energy cost reduction. In this paper, a gamification-based approach for consumer engagement is proposed and a methodology based on Fogg’s behavior model and Kim’s model on player types is developed to examine the suitability of available gamification applications for energy saving/efficiency in the context of a VPP. Seven gamification applications are analyzed and evaluated based on the developed methodology and the results are provided.","PeriodicalId":394287,"journal":{"name":"2020 5th International Conference on Power and Renewable Energy (ICPRE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126669443","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}
Pub Date : 2020-09-12DOI: 10.1109/ICPRE51194.2020.9233285
Jinchuan Cai, L. Jia
In the electricity market environment, an efficient and accurate load forecasting model plays a vital role in the operation and dispatch of the power grid system. However, the deep learning method represented by the recurrent neural network is difficult to capture the effective features of mixed time pattern sequences, and there is a problem of important information loss when the sequence is too long. To solve this problem, we propose a short-term load forecasting model of TCN (temporal convolutional network)-GRU (gated recurrent unit) based on the sparse attention mechanism. This model utilizes the sparse attention GRU network to capture the node features of different time patterns and highlight their importance and then uses TCN's causal dilated convolution operations to extract long-term temporal features of the sequence. Finally, the proposed model integrates two captured feature information to complete the load forecast. Taking the public data set of a certain area in Australia and the load data of a certain area in Shanghai, China as examples, we compare this model with the typical model respectively. The results show that the proposed model has higher prediction accuracy and model robustness.
{"title":"A Hybrid Short-Term Load Forecasting Model Based on Sparse Attention Mechanism","authors":"Jinchuan Cai, L. Jia","doi":"10.1109/ICPRE51194.2020.9233285","DOIUrl":"https://doi.org/10.1109/ICPRE51194.2020.9233285","url":null,"abstract":"In the electricity market environment, an efficient and accurate load forecasting model plays a vital role in the operation and dispatch of the power grid system. However, the deep learning method represented by the recurrent neural network is difficult to capture the effective features of mixed time pattern sequences, and there is a problem of important information loss when the sequence is too long. To solve this problem, we propose a short-term load forecasting model of TCN (temporal convolutional network)-GRU (gated recurrent unit) based on the sparse attention mechanism. This model utilizes the sparse attention GRU network to capture the node features of different time patterns and highlight their importance and then uses TCN's causal dilated convolution operations to extract long-term temporal features of the sequence. Finally, the proposed model integrates two captured feature information to complete the load forecast. Taking the public data set of a certain area in Australia and the load data of a certain area in Shanghai, China as examples, we compare this model with the typical model respectively. The results show that the proposed model has higher prediction accuracy and model robustness.","PeriodicalId":394287,"journal":{"name":"2020 5th International Conference on Power and Renewable Energy (ICPRE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129227580","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}
Pub Date : 2020-09-12DOI: 10.1109/ICPRE51194.2020.9233237
Huahui Chen, S. Lin, Hongke Tang, Yang Zhao
In order to solve the problem that it is difficult to predict the transient electromagnetic field of lightning, this paper attempts to use the circuit method to estimate the current of each branch of metal structure when lightning strikes, and realizes the simulation of space transient electromagnetic field by finite element method (FEM). In this paper, the lightning protection methods of a metal frame are comprehensively analyzed, and the lightning stroke simulation of various lightning protection modes of a metal frame is carried out by using the above methods, and the indoor transient electromagnetic field is evaluated, and its spatial distribution characteristics are analyzed.
{"title":"Study on Evaluating the Transient Electromagnetic Field Caused by Different Lightning Stroke Modes Using FEM Method and Circuit Method","authors":"Huahui Chen, S. Lin, Hongke Tang, Yang Zhao","doi":"10.1109/ICPRE51194.2020.9233237","DOIUrl":"https://doi.org/10.1109/ICPRE51194.2020.9233237","url":null,"abstract":"In order to solve the problem that it is difficult to predict the transient electromagnetic field of lightning, this paper attempts to use the circuit method to estimate the current of each branch of metal structure when lightning strikes, and realizes the simulation of space transient electromagnetic field by finite element method (FEM). In this paper, the lightning protection methods of a metal frame are comprehensively analyzed, and the lightning stroke simulation of various lightning protection modes of a metal frame is carried out by using the above methods, and the indoor transient electromagnetic field is evaluated, and its spatial distribution characteristics are analyzed.","PeriodicalId":394287,"journal":{"name":"2020 5th International Conference on Power and Renewable Energy (ICPRE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125607721","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}
Pub Date : 2020-09-12DOI: 10.1109/ICPRE51194.2020.9233228
Kaiji Liao, Yuefang Du, Keyu Dong, Haoyang Fan, Xihao Zhao
The fault characteristics of the small current grounding system are not obvious when the fault occurs. Currently, it is hard to accurately select and locate the ground fault in the small current grounding system. Therefore, in this paper, a scheme of fault grounding line selection and location is proposed based on the characteristic of the zero-sequence current matrix. As the zero-sequence current phase always lags behind the zero-sequence voltage when the amplitude of zero-sequence current increases. Based on this, we developed a State Matrix S. It can effectively reflect the degree of each region that is affected by the fault. Based on the State Matrix S, we can draw out the regional fault state value through a formula we created to quantify the influence of fault. Fault line selection and ground fault location can be accurately measured by calculating and comparing the regional fault state value of each region. The scheme proposed in this paper applies to a variety of the small current grounding system. The effectiveness and reliability of this scheme have been amply demonstrated through PSCAD/EMTDC simulations and experiments.
{"title":"Identification of Fault Line Selection and Section for Single-Phase Ground Fault in Small Current Grounding System","authors":"Kaiji Liao, Yuefang Du, Keyu Dong, Haoyang Fan, Xihao Zhao","doi":"10.1109/ICPRE51194.2020.9233228","DOIUrl":"https://doi.org/10.1109/ICPRE51194.2020.9233228","url":null,"abstract":"The fault characteristics of the small current grounding system are not obvious when the fault occurs. Currently, it is hard to accurately select and locate the ground fault in the small current grounding system. Therefore, in this paper, a scheme of fault grounding line selection and location is proposed based on the characteristic of the zero-sequence current matrix. As the zero-sequence current phase always lags behind the zero-sequence voltage when the amplitude of zero-sequence current increases. Based on this, we developed a State Matrix S. It can effectively reflect the degree of each region that is affected by the fault. Based on the State Matrix S, we can draw out the regional fault state value through a formula we created to quantify the influence of fault. Fault line selection and ground fault location can be accurately measured by calculating and comparing the regional fault state value of each region. The scheme proposed in this paper applies to a variety of the small current grounding system. The effectiveness and reliability of this scheme have been amply demonstrated through PSCAD/EMTDC simulations and experiments.","PeriodicalId":394287,"journal":{"name":"2020 5th International Conference on Power and Renewable Energy (ICPRE)","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132409857","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}
Pub Date : 2020-09-12DOI: 10.1109/ICPRE51194.2020.9233128
D. Peng, Lijie Jia, Hao Zhang, K. Qi, S. Shi, Chen Fang, Haojing Wang, Xiuchen Jiang, Guojie Li
The establishment of an integrated charging station with PV, energy storage and battery swapping not only meets the different charging and replacement needs of electric vehicle users, but also takes into account the economic benefits of the charging station operating company and the safety of the power grid. In this paper, by analyzing the electrical structure and operation mode of the integrated charging station, and establishing the operation model of each system, this paper proposes a collaborative optimization Dispatching strategy that aims at the minimum operating cost of the integrated charging station, using particle swarm optimization (PSO) algorithm to solve the problem, and optimize the grid and energy storage output plan according to the relevant constraints and combined with the grid power price and electric vehicle charging and replacement demand and other relevant predicted values, thereby reducing the operating cost of the integrated charging station and reducing the load of the charging station peak-valley difference.
{"title":"Research on Cooperative Optimal Dispatching Strategy of PV-Storage-Charging-Swapping Integrated Station Based on Particle Swarm Optimization","authors":"D. Peng, Lijie Jia, Hao Zhang, K. Qi, S. Shi, Chen Fang, Haojing Wang, Xiuchen Jiang, Guojie Li","doi":"10.1109/ICPRE51194.2020.9233128","DOIUrl":"https://doi.org/10.1109/ICPRE51194.2020.9233128","url":null,"abstract":"The establishment of an integrated charging station with PV, energy storage and battery swapping not only meets the different charging and replacement needs of electric vehicle users, but also takes into account the economic benefits of the charging station operating company and the safety of the power grid. In this paper, by analyzing the electrical structure and operation mode of the integrated charging station, and establishing the operation model of each system, this paper proposes a collaborative optimization Dispatching strategy that aims at the minimum operating cost of the integrated charging station, using particle swarm optimization (PSO) algorithm to solve the problem, and optimize the grid and energy storage output plan according to the relevant constraints and combined with the grid power price and electric vehicle charging and replacement demand and other relevant predicted values, thereby reducing the operating cost of the integrated charging station and reducing the load of the charging station peak-valley difference.","PeriodicalId":394287,"journal":{"name":"2020 5th International Conference on Power and Renewable Energy (ICPRE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131524988","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}
Pub Date : 2020-09-12DOI: 10.1109/ICPRE51194.2020.9233223
Jin Yuanyuan, Wang Feiming, Cheng Hui, Liang Xingyu, Li Bing, G. Baohong
The 12kV drop-out fuse pre-arc time-current characteristic test is an important method to verify the quality of the fuse and the reliability of the fuse breaking. This paper summarizes the technical requirements of domestic and foreign test standards, combines test experience, analyzes the impact of test methods on product performance parameter test results, and gives test technical solutions and test standards. The results show that the reading value of the test current is the effective value of the full waveform of the test current, the expected current value is controlled within the range of -5% to + 5% of the target current value, and the test error meets the pre-arc time-current characteristic detection requirements; during the test process The current should be reasonably compensated according to the current attenuation to ensure that the current attenuation rate is less than 10%. The research results are a summary of the pre-arc time-current characteristic test schemes and standards for 12kV drop-out fuses, and have important practical guiding significance for improving the quality level of distribution fuses.
{"title":"Characteristic Analysis and Test Method of Pre-arc Seconds Characteristic of 12kV Drop-out Fuse","authors":"Jin Yuanyuan, Wang Feiming, Cheng Hui, Liang Xingyu, Li Bing, G. Baohong","doi":"10.1109/ICPRE51194.2020.9233223","DOIUrl":"https://doi.org/10.1109/ICPRE51194.2020.9233223","url":null,"abstract":"The 12kV drop-out fuse pre-arc time-current characteristic test is an important method to verify the quality of the fuse and the reliability of the fuse breaking. This paper summarizes the technical requirements of domestic and foreign test standards, combines test experience, analyzes the impact of test methods on product performance parameter test results, and gives test technical solutions and test standards. The results show that the reading value of the test current is the effective value of the full waveform of the test current, the expected current value is controlled within the range of -5% to + 5% of the target current value, and the test error meets the pre-arc time-current characteristic detection requirements; during the test process The current should be reasonably compensated according to the current attenuation to ensure that the current attenuation rate is less than 10%. The research results are a summary of the pre-arc time-current characteristic test schemes and standards for 12kV drop-out fuses, and have important practical guiding significance for improving the quality level of distribution fuses.","PeriodicalId":394287,"journal":{"name":"2020 5th International Conference on Power and Renewable Energy (ICPRE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132704899","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}
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