Xia Li, Xiang Xue, Ran Liu, Qingxin Liu, Chang Yuan
The power imbalance between the source and the load is aggravated by the large number of new energy connections. Electric Spring (ES) can adjust the load side. Due to the limited capacity of a single power spring, it is necessary to install multiple power spring support system voltages in the power grid. However, multiple power springs need coordinated control methods to ensure their stable operation. In this paper, a coordinated control method for multiple power springs is proposed to optimize the voltage of the grid-connected points. First, the phenomenon of voltage deviation at each gridconnected point is analyzed. Then, according to the two-port impedance model of the power spring, an optimization model for the voltage deviation at the grid-connected point of the power spring is proposed, and solved by the particle swarm optimization algorithm. Finally, the coordinated control method for multiple power springs is given. The proposed method effectively reduces the voltage deviation of the grid connection point and enables the power spring to remain stable when the system voltage fluctuation is large. Finally, the validity of the proposed method is verified by PSACD simulation.
{"title":"Multi-electric springs coordinated control method for grid voltage optimization","authors":"Xia Li, Xiang Xue, Ran Liu, Qingxin Liu, Chang Yuan","doi":"10.1117/12.2689813","DOIUrl":"https://doi.org/10.1117/12.2689813","url":null,"abstract":"The power imbalance between the source and the load is aggravated by the large number of new energy connections. Electric Spring (ES) can adjust the load side. Due to the limited capacity of a single power spring, it is necessary to install multiple power spring support system voltages in the power grid. However, multiple power springs need coordinated control methods to ensure their stable operation. In this paper, a coordinated control method for multiple power springs is proposed to optimize the voltage of the grid-connected points. First, the phenomenon of voltage deviation at each gridconnected point is analyzed. Then, according to the two-port impedance model of the power spring, an optimization model for the voltage deviation at the grid-connected point of the power spring is proposed, and solved by the particle swarm optimization algorithm. Finally, the coordinated control method for multiple power springs is given. The proposed method effectively reduces the voltage deviation of the grid connection point and enables the power spring to remain stable when the system voltage fluctuation is large. Finally, the validity of the proposed method is verified by PSACD simulation.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129505534","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}
With the increase of the share of wind power in energy distribution, accurate ultra-short term wind power prediction results play key role in the optimal real-time scheduling of the power grid. A stacking integration method is proposed based on error correction in this paper. First, the support vector machine for regression (SVR), gradient boosting decision tree (GBDT), multilayer perceptron (MLP) and random forest (RF) are selected as the base models. Then, the linear regression is utilized as the meta-model. The error generated by the base model in the verification set and the spliced verification set are introduced into the training set of the meta-model. Finally, the prediction results and prediction errors in the prediction set are applied to the meta-model to predict the ultra-short term wind power. The experiment results show that the effectiveness of the proposed method by using the real wind power data.
{"title":"Ultra-short term wind power prediction based on an error correction stacking method","authors":"Ziqi Zhang, Yunfei Ding, Jin Yang","doi":"10.1117/12.2689397","DOIUrl":"https://doi.org/10.1117/12.2689397","url":null,"abstract":"With the increase of the share of wind power in energy distribution, accurate ultra-short term wind power prediction results play key role in the optimal real-time scheduling of the power grid. A stacking integration method is proposed based on error correction in this paper. First, the support vector machine for regression (SVR), gradient boosting decision tree (GBDT), multilayer perceptron (MLP) and random forest (RF) are selected as the base models. Then, the linear regression is utilized as the meta-model. The error generated by the base model in the verification set and the spliced verification set are introduced into the training set of the meta-model. Finally, the prediction results and prediction errors in the prediction set are applied to the meta-model to predict the ultra-short term wind power. The experiment results show that the effectiveness of the proposed method by using the real wind power data.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130295446","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}
With the increasing reliability of power supply in distribution networks, loop closing operation is often required to achieve non-stop load transfer. Firstly, the loop closing device based on improved phase shifting transformer (IPST) can be effectively used in complex loop closing scenarios such as three-phase asymmetry and voltage magnitude difference between the buses on both sides of the loop closing point of the distribution network, considering that the internal impedance of the loop closing device has a certain influence on the quality of the loop transfer voltage. Secondly, the mathematical model of IPST equivalent impedance is established based on the basic principle of transformer, and the expressions of IPST equivalent impedance calculation and the factors affecting the size are derived and analyzed. Finally, the correctness of the proposed IPST-based impedance calculation expression for the loop closing device is verified by MATLAB and PSCAD/EMTDC simulation platform.
{"title":"Impedance characteristics of loop closing device based on improved phase shifting transformer","authors":"Zhi Xu, Jianxiong Tang, Hongsheng Ma, Yupeng Jiang, Junpeng Li, Yongchun Yang","doi":"10.1117/12.2689880","DOIUrl":"https://doi.org/10.1117/12.2689880","url":null,"abstract":"With the increasing reliability of power supply in distribution networks, loop closing operation is often required to achieve non-stop load transfer. Firstly, the loop closing device based on improved phase shifting transformer (IPST) can be effectively used in complex loop closing scenarios such as three-phase asymmetry and voltage magnitude difference between the buses on both sides of the loop closing point of the distribution network, considering that the internal impedance of the loop closing device has a certain influence on the quality of the loop transfer voltage. Secondly, the mathematical model of IPST equivalent impedance is established based on the basic principle of transformer, and the expressions of IPST equivalent impedance calculation and the factors affecting the size are derived and analyzed. Finally, the correctness of the proposed IPST-based impedance calculation expression for the loop closing device is verified by MATLAB and PSCAD/EMTDC simulation platform.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"38 3-4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130312077","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}
Wenshan Xiao, Jun Wu, Zihui Guo, Wenxin Huang, Zichen Liu
An improved K-means clustering algorithm based on the initial clustering center is used to cluster the charging data of electric vehicles. The multi-classification method is studied, and the clustering effect of different number of clusters is analyzed with the contour coefficient as the evaluation standard. The simulation results show that this method can properly cluster the group characteristics of electric vehicles
{"title":"EV aggregation modeling based on improved K-means","authors":"Wenshan Xiao, Jun Wu, Zihui Guo, Wenxin Huang, Zichen Liu","doi":"10.1117/12.2689418","DOIUrl":"https://doi.org/10.1117/12.2689418","url":null,"abstract":"An improved K-means clustering algorithm based on the initial clustering center is used to cluster the charging data of electric vehicles. The multi-classification method is studied, and the clustering effect of different number of clusters is analyzed with the contour coefficient as the evaluation standard. The simulation results show that this method can properly cluster the group characteristics of electric vehicles","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122580316","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}
As 5G communication networks are now putting into commercialization, technologies for 6G communications assisted by intelligent reflecting surface (IRS) are also being explored in order to obtain faster and more reliable data transmissions. This paper studies the weighted sum-rate (WSR) maximization problem of users in an IRS-aided multiuser multiple-input single-output (MISO) downlink communication system. Aiming at the above problem, we propose a low-complexity linear alternating direction multiplier method (LADMM) that can be operated in parallel to solve this problem. The numerical results show that only adjusting the parameters in the proposed algorithm can make the user's WSR have better performance.
{"title":"A low-complexity algorithm for maximizing the weighted sum-rate of intelligent reflecting surface aided wireless communication","authors":"Shanjie Cai, Yajun Wang","doi":"10.1117/12.2689903","DOIUrl":"https://doi.org/10.1117/12.2689903","url":null,"abstract":"As 5G communication networks are now putting into commercialization, technologies for 6G communications assisted by intelligent reflecting surface (IRS) are also being explored in order to obtain faster and more reliable data transmissions. This paper studies the weighted sum-rate (WSR) maximization problem of users in an IRS-aided multiuser multiple-input single-output (MISO) downlink communication system. Aiming at the above problem, we propose a low-complexity linear alternating direction multiplier method (LADMM) that can be operated in parallel to solve this problem. The numerical results show that only adjusting the parameters in the proposed algorithm can make the user's WSR have better performance.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"GE-25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126568944","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}
Huijuan Fu, Xiaoqi Xi, Yu Han, Linlin Zhu, Mengnan Liu, Siyu Tan, Chang Liu, Lei Li, Bin Yan
X-ray tomographic imaging has become an important analytical tool with a wide range of applications. It is inevitable that noise is introduced in CT images, and noise reduction is necessary. To solve this problem, we considered to use the nonlocal property of similar block search and proposed a deep learning network based on similar block learning for noise reduction of micro CT short exposure time scanned images to improve the scanning efficiency while ensuring high quality imaging. The method uses the output of the nonlocal method as a data preprocessing algorithm by combining a nonlocal block matching algorithm with a convolutional neural network, and uses a residual channel attention mechanism to learn the features after feature extraction, which reduces noise while preserving image details. Experimental results show that the method can remove noise from CT images quickly and effectively, and compared with the classical CPCE noise reduction method, the method improves the PSNR index by 1.52 dB, which is consistent with the theoretical assumption.
{"title":"A study on low-dose CT image denoising method based on similar block learning","authors":"Huijuan Fu, Xiaoqi Xi, Yu Han, Linlin Zhu, Mengnan Liu, Siyu Tan, Chang Liu, Lei Li, Bin Yan","doi":"10.1117/12.2689480","DOIUrl":"https://doi.org/10.1117/12.2689480","url":null,"abstract":"X-ray tomographic imaging has become an important analytical tool with a wide range of applications. It is inevitable that noise is introduced in CT images, and noise reduction is necessary. To solve this problem, we considered to use the nonlocal property of similar block search and proposed a deep learning network based on similar block learning for noise reduction of micro CT short exposure time scanned images to improve the scanning efficiency while ensuring high quality imaging. The method uses the output of the nonlocal method as a data preprocessing algorithm by combining a nonlocal block matching algorithm with a convolutional neural network, and uses a residual channel attention mechanism to learn the features after feature extraction, which reduces noise while preserving image details. Experimental results show that the method can remove noise from CT images quickly and effectively, and compared with the classical CPCE noise reduction method, the method improves the PSNR index by 1.52 dB, which is consistent with the theoretical assumption.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132676319","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}
Tao Lu, Yongwang Zhang, Jie Zhang, Wen Zhao, Qizhang Xu
To improve the performance of high-frequency information acquisition data processing and meet the differentiated power service requirements in low-voltage distribution grid, this paper first investigates an edge processing architecture for high-frequency information acquisition in low-voltage distribution grid. Then, an edge processing mechanism for high-frequency information acquisition is proposed to optimize the high-frequency information acquisition data distribution strategy and communication and computation resource allocation strategies. Simulation results demonstrate that the proposed mechanism achieves better data processing performances compared with traditional data processing mechanism
{"title":"Edge processing mechanism for high-frequency information acquisition in low-voltage distribution grid","authors":"Tao Lu, Yongwang Zhang, Jie Zhang, Wen Zhao, Qizhang Xu","doi":"10.1117/12.2689389","DOIUrl":"https://doi.org/10.1117/12.2689389","url":null,"abstract":"To improve the performance of high-frequency information acquisition data processing and meet the differentiated power service requirements in low-voltage distribution grid, this paper first investigates an edge processing architecture for high-frequency information acquisition in low-voltage distribution grid. Then, an edge processing mechanism for high-frequency information acquisition is proposed to optimize the high-frequency information acquisition data distribution strategy and communication and computation resource allocation strategies. Simulation results demonstrate that the proposed mechanism achieves better data processing performances compared with traditional data processing mechanism","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131465120","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}
The probabilistic amplitude shaping(PAS) scheme based on amplitude shift keying(ASK) has attracted much attention as a signal-shaping solution. However, due to the limitation of nonlinear effects in practical applications, the robustness of the ASK modulation scheme is far worse than amplitude and phase shift keying(APSK) modulation. Therefore, we design a distributed mapping scheme based on APSK and propose the corresponding PAS scheme. To find the balance between the rate loss and the shaping gain brought by the shaping scheme, this paper utilizes Monte Carlo to simulate and successfully verify that the proposed scheme performs well at different transmission rates, with a maximum of 0.67 dB.
{"title":"A probabilistic amplitude shaping scheme on amplitude and phase shift keying modulation","authors":"Xiangrui Meng, Chao Dong","doi":"10.1117/12.2689810","DOIUrl":"https://doi.org/10.1117/12.2689810","url":null,"abstract":"The probabilistic amplitude shaping(PAS) scheme based on amplitude shift keying(ASK) has attracted much attention as a signal-shaping solution. However, due to the limitation of nonlinear effects in practical applications, the robustness of the ASK modulation scheme is far worse than amplitude and phase shift keying(APSK) modulation. Therefore, we design a distributed mapping scheme based on APSK and propose the corresponding PAS scheme. To find the balance between the rate loss and the shaping gain brought by the shaping scheme, this paper utilizes Monte Carlo to simulate and successfully verify that the proposed scheme performs well at different transmission rates, with a maximum of 0.67 dB.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127679520","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}
For multipath effects and frequency fading of channels in indoor visible light communication systems. In this paper, an improved LMS algorithm is proposed to compensate for the channel. First, the channel model of indoor visible light communication and the simulation model of orthogonal frequency division multiplexing (OFDM) are built.The simulation verifies the effect of eigenvalue distribution, filter order and step size on the LMS algorithm. Secondly, the NLMS algorithm proposed in this paper solves, to a certain extent, the inherent contradiction that the convergence speed and steady-state error of the LMS algorithm cannot be reasonably coordinated. The simulation results show that: The algorithm improves the convergence speed and reduces the computational complexity compared with the LMS algorithm, which effectively improves the performance of visible light communication systems.
{"title":"Research on the equalization algorithm of indoor visible light communication","authors":"Guolu Huang, P. Li, Yuru Wang, Z. Lan","doi":"10.1117/12.2689451","DOIUrl":"https://doi.org/10.1117/12.2689451","url":null,"abstract":"For multipath effects and frequency fading of channels in indoor visible light communication systems. In this paper, an improved LMS algorithm is proposed to compensate for the channel. First, the channel model of indoor visible light communication and the simulation model of orthogonal frequency division multiplexing (OFDM) are built.The simulation verifies the effect of eigenvalue distribution, filter order and step size on the LMS algorithm. Secondly, the NLMS algorithm proposed in this paper solves, to a certain extent, the inherent contradiction that the convergence speed and steady-state error of the LMS algorithm cannot be reasonably coordinated. The simulation results show that: The algorithm improves the convergence speed and reduces the computational complexity compared with the LMS algorithm, which effectively improves the performance of visible light communication systems.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128499541","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}
Accurate prediction of fire environment changes is helpful to accurately grasp the development trend of fire and ensure the safety of personnel. It is difficult to establish an accurate prediction model because of the coexistence of multiple parameters, complex coupling relationship, time series and nonlinearity of fire environment. In this paper, long shortterm memory network model (LSTM) based on improved Harris Hawk algorithm (CHHO) is proposed to achieve accurate prediction of fire environment data. Then, CHHO is used to optimize the hyperparameters in LSTM, and the fire temperature is predicted based on the optimized parameters. The experimental results show that the method of CHHO automatic parameter selection solves the problem of manual selection of LSTM model parameters and gives full play to the best performance of the model. The five environmental parameters of indoor fire temperature was predicted. The average fitting effect of CHHO-LSTM reached 94 %. The results show that the model has high prediction accuracy.
{"title":"Research on fire prediction model based on improved Harris hawk optimization algorithm","authors":"Yong-dong Wang, Kai-Xin Yuan, Xiangrui Cao","doi":"10.1117/12.2689496","DOIUrl":"https://doi.org/10.1117/12.2689496","url":null,"abstract":"Accurate prediction of fire environment changes is helpful to accurately grasp the development trend of fire and ensure the safety of personnel. It is difficult to establish an accurate prediction model because of the coexistence of multiple parameters, complex coupling relationship, time series and nonlinearity of fire environment. In this paper, long shortterm memory network model (LSTM) based on improved Harris Hawk algorithm (CHHO) is proposed to achieve accurate prediction of fire environment data. Then, CHHO is used to optimize the hyperparameters in LSTM, and the fire temperature is predicted based on the optimized parameters. The experimental results show that the method of CHHO automatic parameter selection solves the problem of manual selection of LSTM model parameters and gives full play to the best performance of the model. The five environmental parameters of indoor fire temperature was predicted. The average fitting effect of CHHO-LSTM reached 94 %. The results show that the model has high prediction accuracy.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128685000","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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