In sensorless control system, High-frequency (HF) voltage injection methods are widely used for permanent magnet synchronous motor (PMSM). However, subject to the response of rotor estimate position, either the speed loop or current loop bandwidth can not be set too large. But the current loop bandwidth is a key factor to accuracy control. With low bandwidth, it can only handle DC and low-frequency disturbance. Because the dead-time of voltage source inverters (VSIs) which cause the 6th harmonic in the d-q coordinate system, a revised repetitive controller (RRC) is proposed to reappear specific frequency harmonics. This paper described a new injection method, which combines the HF voltage and RRC output. In limited bandwidth, the 6th harmonic in current is sharply decreased. Finally, simulation results have proved the feasibility of the proposed scheme.
{"title":"Harmonic reduction for permanent magnet synchronous motor sensorless drives","authors":"Xu Zhijie, Yang Kai, Zheng Yifei, Huang Yuhao","doi":"10.1117/12.2640358","DOIUrl":"https://doi.org/10.1117/12.2640358","url":null,"abstract":"In sensorless control system, High-frequency (HF) voltage injection methods are widely used for permanent magnet synchronous motor (PMSM). However, subject to the response of rotor estimate position, either the speed loop or current loop bandwidth can not be set too large. But the current loop bandwidth is a key factor to accuracy control. With low bandwidth, it can only handle DC and low-frequency disturbance. Because the dead-time of voltage source inverters (VSIs) which cause the 6th harmonic in the d-q coordinate system, a revised repetitive controller (RRC) is proposed to reappear specific frequency harmonics. This paper described a new injection method, which combines the HF voltage and RRC output. In limited bandwidth, the 6th harmonic in current is sharply decreased. Finally, simulation results have proved the feasibility of the proposed scheme.","PeriodicalId":240234,"journal":{"name":"4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116702584","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}
Under the environment of energy crisis and green development, the development of electric vehicles has become inevitable. However, the charging load of electric vehicles has great uncertainty, which will have a certain impact on the security and stability of the power grid. Accurately predicting the charging load of electric vehicles is an effective method to avoid this problem. Considering the spatiotemporal characteristics of electric vehicle charging load, an electric vehicle load prediction method based on differential evolution algorithm improved BP neural network is proposed to complete the search of the weight space and network structure space of the neural network at the same time. The optimal network structure. The algorithm adopts the (1+1)-ES binary evolution strategy, uses a new network structure crossover and mutation method, and speeds up the search of the neural network model and the algorithm through the co-evolution of dual population structure and adaptive mutation rate strategies. Convergence improves the learning ability of the network and reduces the prediction error of the BP neural network model. The model and the BP neural network model are respectively used to predict the load of electric vehicles, and the comparison results prove the superiority of the proposed model.
{"title":"Electric vehicle load forecasting based on improved neural network based on differential evolution algorithm","authors":"Zhu Shiwei, Wu Wenzhen, Zhang Jiahao, Li Na","doi":"10.1117/12.2640366","DOIUrl":"https://doi.org/10.1117/12.2640366","url":null,"abstract":"Under the environment of energy crisis and green development, the development of electric vehicles has become inevitable. However, the charging load of electric vehicles has great uncertainty, which will have a certain impact on the security and stability of the power grid. Accurately predicting the charging load of electric vehicles is an effective method to avoid this problem. Considering the spatiotemporal characteristics of electric vehicle charging load, an electric vehicle load prediction method based on differential evolution algorithm improved BP neural network is proposed to complete the search of the weight space and network structure space of the neural network at the same time. The optimal network structure. The algorithm adopts the (1+1)-ES binary evolution strategy, uses a new network structure crossover and mutation method, and speeds up the search of the neural network model and the algorithm through the co-evolution of dual population structure and adaptive mutation rate strategies. Convergence improves the learning ability of the network and reduces the prediction error of the BP neural network model. The model and the BP neural network model are respectively used to predict the load of electric vehicles, and the comparison results prove the superiority of the proposed model.","PeriodicalId":240234,"journal":{"name":"4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124385047","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}
In recent years, the research of data visualization has attracted more and more attention, especially the visualization of traffic flow data. This paper proposes a grid clustering method based on k-means. First, the geographic data is divided into grid matrix, and then K-means is used to perform cluster analysis on the data. The traffic flow data is then displayed. In this paper, the inner feeling of public transport flow analysis is very important for public transport operation decision making, and provides a very important basis for the operation safety supervision and risk assessment decision of urban public transport.
{"title":"Visual analysis of bus flow based on grid clustering","authors":"Pan Xiuqin, Hou Fei","doi":"10.1117/12.2640174","DOIUrl":"https://doi.org/10.1117/12.2640174","url":null,"abstract":"In recent years, the research of data visualization has attracted more and more attention, especially the visualization of traffic flow data. This paper proposes a grid clustering method based on k-means. First, the geographic data is divided into grid matrix, and then K-means is used to perform cluster analysis on the data. The traffic flow data is then displayed. In this paper, the inner feeling of public transport flow analysis is very important for public transport operation decision making, and provides a very important basis for the operation safety supervision and risk assessment decision of urban public transport.","PeriodicalId":240234,"journal":{"name":"4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123561501","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：