INTRODUCTION: With the continuous advancement of urbanization, urban landscape sculpture plays an increasingly important role in modern urban planning. Traditional planning and design methods make it challenging to demonstrate the three-dimensional sense and artistry of sculpture fully; therefore, this study explores a new method of planning and designing modern urban landscape sculpture based on three-dimensional virtual reconstruction.OBJECTIVES: This study aims to enhance the three-dimensional sense and artistry of urban landscape sculpture planning and design through three-dimensional virtual reconstruction technology to meet the needs of modern urban development better. By using advanced technical means, the planning and design can be made more intuitive and specific and provide urban residents with a more artistic public space.METHODS: The study adopts advanced three-dimensional virtual reconstruction technology, combined with urban planning and design theory, to plan and design modern urban landscape sculpture. Firstly, relevant literature on urban planning and sculpture design is collected to understand the existing design concepts and technical means. Secondly, a detailed virtual reconstruction of the sculpture is carried out by using three-dimensional modeling software to show the three-dimensional effect of the sculpture. Finally, the design scheme is optimized and improved through fieldwork and expert review.RESULTS: Through three-dimensional virtual reconstruction technology, this study successfully shows the whole picture of modern urban landscape sculpture. The design scheme not only has a three-dimensional sense but it has also been improved in artistry. The results of fieldwork and expert evaluation show that the new design scheme is more in line with the needs of urban development and adds a unique artistic atmosphere to the urban space.CONCLUSION: This study has achieved positive results in the field of modern urban landscape sculpture planning and design through 3D virtual reconstruction technology. The new design method not only provides a more specific tool for urban planners but also creates a more creative and artistic public space for urban residents. In the future, the application of this method in different urban contexts can be further explored and expanded to inject more innovation and vitality into urban planning and sculpture design.
{"title":"Based on 3D Virtual Reconstruction of Modern City Landscape Sculpture Planning Design","authors":"Xin Xu","doi":"10.4108/ew.5248","DOIUrl":"https://doi.org/10.4108/ew.5248","url":null,"abstract":"INTRODUCTION: With the continuous advancement of urbanization, urban landscape sculpture plays an increasingly important role in modern urban planning. Traditional planning and design methods make it challenging to demonstrate the three-dimensional sense and artistry of sculpture fully; therefore, this study explores a new method of planning and designing modern urban landscape sculpture based on three-dimensional virtual reconstruction.OBJECTIVES: This study aims to enhance the three-dimensional sense and artistry of urban landscape sculpture planning and design through three-dimensional virtual reconstruction technology to meet the needs of modern urban development better. By using advanced technical means, the planning and design can be made more intuitive and specific and provide urban residents with a more artistic public space.METHODS: The study adopts advanced three-dimensional virtual reconstruction technology, combined with urban planning and design theory, to plan and design modern urban landscape sculpture. Firstly, relevant literature on urban planning and sculpture design is collected to understand the existing design concepts and technical means. Secondly, a detailed virtual reconstruction of the sculpture is carried out by using three-dimensional modeling software to show the three-dimensional effect of the sculpture. Finally, the design scheme is optimized and improved through fieldwork and expert review.RESULTS: Through three-dimensional virtual reconstruction technology, this study successfully shows the whole picture of modern urban landscape sculpture. The design scheme not only has a three-dimensional sense but it has also been improved in artistry. The results of fieldwork and expert evaluation show that the new design scheme is more in line with the needs of urban development and adds a unique artistic atmosphere to the urban space.CONCLUSION: This study has achieved positive results in the field of modern urban landscape sculpture planning and design through 3D virtual reconstruction technology. The new design method not only provides a more specific tool for urban planners but also creates a more creative and artistic public space for urban residents. In the future, the application of this method in different urban contexts can be further explored and expanded to inject more innovation and vitality into urban planning and sculpture design.","PeriodicalId":502230,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"89 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140238251","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}
Rainfall prediction is a critical field of study with several practical uses, including agriculture, water management, and disaster preparedness. In this work, we examine the performance of several machine learning models in forecasting rainfall using a dataset of Australian rainfall observations from Kaggle. Six models are compared: random forest (RF), logistic regression (LogReg), Gaussian Naive Bayes (GNB), k-nearest neighbours (kNN), support vector classifier (SVC), and XGBoost (XGB). Missing value imputation and feature selection were used to preprocess the dataset. To analyse the models, we employed cross-validation and performance indicators such as accuracy, precision, recall, and F1-score. According to our findings, the RF and XGB models fared the best, with accuracy ratings of 87% and 85%, respectively. With accuracy ratings below 70%, the GNB and SVC models performed the poorest. Our findings imply that machine learning algorithms can be useful tools for predicting rainfall, but careful model selection and evaluation are required for reliable results.
{"title":"Rainfall Prediction using XGB Model with the Australian Dataset","authors":"Surendra Reddy Vinta, Rashika Peeriga","doi":"10.4108/ew.5386","DOIUrl":"https://doi.org/10.4108/ew.5386","url":null,"abstract":"Rainfall prediction is a critical field of study with several practical uses, including agriculture, water management, and disaster preparedness. In this work, we examine the performance of several machine learning models in forecasting rainfall using a dataset of Australian rainfall observations from Kaggle. Six models are compared: random forest (RF), logistic regression (LogReg), Gaussian Naive Bayes (GNB), k-nearest neighbours (kNN), support vector classifier (SVC), and XGBoost (XGB). Missing value imputation and feature selection were used to preprocess the dataset. To analyse the models, we employed cross-validation and performance indicators such as accuracy, precision, recall, and F1-score. According to our findings, the RF and XGB models fared the best, with accuracy ratings of 87% and 85%, respectively. \u0000With accuracy ratings below 70%, the GNB and SVC models performed the poorest. Our findings imply that machine learning algorithms can be useful tools for predicting rainfall, but careful model selection and evaluation are required for reliable results.","PeriodicalId":502230,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140250039","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 the growing demand for energy as well as the strengthening of environmental awareness, photovoltaic power generation, as a clean and renewable energy source, has gradually attracted people's attention and attention. To facilitate the dispatching and planning of power system, this study uses historical data and meteorological data to build a photovoltaic power generation prediction and configuration optimization model on the ground of Gaussian process regression and improved particle swarm optimization algorithm. The simulation results show that the regression prediction curve of the Gaussian process regression prediction model is the closest to the real curve, and the prediction curve is stable and not easily disturbed by noise data. The Root-mean-square deviation and the average absolute proportional error of the model are small, and the disparity in the predicted value and the true value of the model is small; The integration of multi factor data has improved the accuracy of prediction data, and the regression prediction effect is good. The improved Particle swarm optimization algorithm could continuously enhance in the search for the optimal solution, and the Rate of convergence is fast. The Pareto solution can provide different solutions suitable for photovoltaic power generation optimization. Reasonable optimization configuration can effectively reduce active power line loss and voltage deviation, with the maximum reduction values reaching 132kW and 0.028, respectively. The research and design of predictive models and optimized configuration models can promote the formation of smart grids.
{"title":"Photovoltaic power generation prediction and optimization configuration model based on GPR and improved PSO algorithm","authors":"Zhennan Zhang, Zhenliang Duan, Lingwei Zhang","doi":"10.4108/ew.3809","DOIUrl":"https://doi.org/10.4108/ew.3809","url":null,"abstract":"As the growing demand for energy as well as the strengthening of environmental awareness, photovoltaic power generation, as a clean and renewable energy source, has gradually attracted people's attention and attention. To facilitate the dispatching and planning of power system, this study uses historical data and meteorological data to build a photovoltaic power generation prediction and configuration optimization model on the ground of Gaussian process regression and improved particle swarm optimization algorithm. The simulation results show that the regression prediction curve of the Gaussian process regression prediction model is the closest to the real curve, and the prediction curve is stable and not easily disturbed by noise data. The Root-mean-square deviation and the average absolute proportional error of the model are small, and the disparity in the predicted value and the true value of the model is small; The integration of multi factor data has improved the accuracy of prediction data, and the regression prediction effect is good. The improved Particle swarm optimization algorithm could continuously enhance in the search for the optimal solution, and the Rate of convergence is fast. The Pareto solution can provide different solutions suitable for photovoltaic power generation optimization. Reasonable optimization configuration can effectively reduce active power line loss and voltage deviation, with the maximum reduction values reaching 132kW and 0.028, respectively. The research and design of predictive models and optimized configuration models can promote the formation of smart grids.","PeriodicalId":502230,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"8 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139958288","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}
Pavithra C, Dhayalan R, Anandha Kumar S, Dharshan Y, Haridharan R, Vijayadharshini M
The depletion of fossil fuels and rising energy demand have increased the use of renewable energy. Among all Solar PVs, system-based electricity production is increased due to multiple advantages. In this paper a Solar PV system with an Artificial Neural Network (ANN)-based Maximum Power Point Tracking (MPPT) controller is developed. ANN has multiple advantages like stability, improved dynamic response, and fast and precise output. The System is modelled with a DC-DC boost converter with Perturb and Observe (P&O)-based MPPT controller which is operated in MATLAB-based Simulink model. Both the controller output is analyzed and compared, among these two controllers ANN has very fast and more precise output under dynamic conditions.
{"title":"A Novel Comparative Analysis of Solar P&O, ANN-based MPPT Controller under Different Irradiance Condition","authors":"Pavithra C, Dhayalan R, Anandha Kumar S, Dharshan Y, Haridharan R, Vijayadharshini M","doi":"10.4108/ew.4942","DOIUrl":"https://doi.org/10.4108/ew.4942","url":null,"abstract":"The depletion of fossil fuels and rising energy demand have increased the use of renewable energy. Among all Solar PVs, system-based electricity production is increased due to multiple advantages. In this paper a Solar PV system with an Artificial Neural Network (ANN)-based Maximum Power Point Tracking (MPPT) controller is developed. ANN has multiple advantages like stability, improved dynamic response, and fast and precise output. The System is modelled with a DC-DC boost converter with Perturb and Observe (P&O)-based MPPT controller which is operated in MATLAB-based Simulink model. Both the controller output is analyzed and compared, among these two controllers ANN has very fast and more precise output under dynamic conditions.","PeriodicalId":502230,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"43 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139594945","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}
U. S, Geetha P, Geetha A, Balamurugan K S, Selciya Selvan
Large values from external causes, such as partial shade, can greatly influence output power of PV. The applications of partial shading are frequently utilized in simulation software. However, in this research work, partial shading and the integration of the photovoltaic Thermal (PV/T) Hybrid Solar Panel is implemented, and analysis is done to see how it affects the output power of solar panels under genuine climatic circumstances. Many research investigations have been conducted and researchers continue to look at PV/T systems to enhance their performance. The application is designed to provide information on solar panel output power under normal and partial shading situations. The maximum amount of power that solar panels can generate is 298.50 W. Under typical circumstances, partial shading in a solar panel can result in a maximum power value of 141.13 W, and this partial shading leads the power to increase.
{"title":"A novel concept of solar photovoltaic partial shading and thermal hybrid system for performance improvement","authors":"U. S, Geetha P, Geetha A, Balamurugan K S, Selciya Selvan","doi":"10.4108/ew.4943","DOIUrl":"https://doi.org/10.4108/ew.4943","url":null,"abstract":"Large values from external causes, such as partial shade, can greatly influence output power of PV. The applications of partial shading are frequently utilized in simulation software. However, in this research work, partial shading and the integration of the photovoltaic Thermal (PV/T) Hybrid Solar Panel is implemented, and analysis is done to see how it affects the output power of solar panels under genuine climatic circumstances. Many research investigations have been conducted and researchers continue to look at PV/T systems to enhance their performance. The application is designed to provide information on solar panel output power under normal and partial shading situations. The maximum amount of power that solar panels can generate is 298.50 W. Under typical circumstances, partial shading in a solar panel can result in a maximum power value of 141.13 W, and this partial shading leads the power to increase.","PeriodicalId":502230,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"42 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139595090","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}
INTRODUCTION: STAR-CCM+ is a CFD software that uses continuum mechanics numerical techniques and is a tool for thermodynamic and fluid dynamics analysis. STAR-CCM+has expanded the functions of surface treatment, such as surface wrapper, surface remesh, and volume mesh generation.With the increase of train speed, the aerodynamic phenomena become more prominent, and the aerodynamic phenomena of high-speed trains in crosswind environment become more complicated. OBJECTIVES: Bogie is an important part of high-speed train. It is of great significance to study the aerodynamic performance Three groups of trains operating in a strong wind environment are modeled, and the surface pressure distribution characteristics of the car body and bogie, as well as the aerodynamic and aerodynamic torque distribution characteristics of each car and bogie, are analyzed when the train operates at 350km/h under a Class 12 crosswind condition. RESULTS: The results of the study show the variation rules of surface pressure, aerodynamic force and aerodynamic moment of the car body and bogie with wind speed. CONCLUSION: The windward surface pressure of the vehicle body increases linearly with the increase of wind speed, and the surface pressure of the roof and leeward side decreases linearly with the increase of wind speed.
{"title":"Using STAR-CCM+ Software in Aerodynamic Performance of Bogies under Crosswind Conditions","authors":"Yong-Su Jin, Xiaoli Chen","doi":"10.4108/ew.4891","DOIUrl":"https://doi.org/10.4108/ew.4891","url":null,"abstract":"INTRODUCTION: STAR-CCM+ is a CFD software that uses continuum mechanics numerical techniques and is a tool for thermodynamic and fluid dynamics analysis. STAR-CCM+has expanded the functions of surface treatment, such as surface wrapper, surface remesh, and volume mesh generation.With the increase of train speed, the aerodynamic phenomena become more prominent, and the aerodynamic phenomena of high-speed trains in crosswind environment become more complicated. \u0000OBJECTIVES: Bogie is an important part of high-speed train. It is of great significance to study the aerodynamic performance Three groups of trains operating in a strong wind environment are modeled, and the surface pressure distribution characteristics of the car body and bogie, as well as the aerodynamic and aerodynamic torque distribution characteristics of each car and bogie, are analyzed when the train operates at 350km/h under a Class 12 crosswind condition. \u0000RESULTS: The results of the study show the variation rules of surface pressure, aerodynamic force and aerodynamic moment of the car body and bogie with wind speed. \u0000CONCLUSION: The windward surface pressure of the vehicle body increases linearly with the increase of wind speed, and the surface pressure of the roof and leeward side decreases linearly with the increase of wind speed.","PeriodicalId":502230,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"114 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139614243","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}
INTRODUCTION: Efficient and accurate optimization of green and low-carbon logistics paths, as one of the key technologies of green and low-carbon logistics, can not only promote the high-quality development of the economy, but also reduce the negative impacts of logistics on the environment and increase the cost of logistics delivery. OBJECTIVES: To address the problems of slow convergence and easy to fall into local optimization in the current performance prediction research on talent team building. METHODS: This paper proposes a snowmelt heuristic optimization algorithm to solve the green low-carbon logistics path optimization problem. Firstly, the objective function of green low-carbon logistics path optimization is designed by analyzing the optimization cost and conditional constraints of the green low-carbon logistics path optimization problem; then, a method based on intelligent optimization algorithm is proposed by designing the position-order array coding and fitness function, combined with the snow-melting optimization algorithm; finally, the validity and superiority of the proposed method are verified by simulation experiments. RESULTS: The results show that the proposed method not only improves the convergence speed but also increases the optimization fitness value. Conclusion: The problem of slow convergence and easy to fall into local optimum in the solution of green low-carbon logistics path optimization problem is solved.
{"title":"A Snowmelt Optimization Algorithm Applied to Green Low Carbon Logistics Pathways Optimization Problems","authors":"Chunxia Zhai","doi":"10.4108/ew.4889","DOIUrl":"https://doi.org/10.4108/ew.4889","url":null,"abstract":"INTRODUCTION: Efficient and accurate optimization of green and low-carbon logistics paths, as one of the key technologies of green and low-carbon logistics, can not only promote the high-quality development of the economy, but also reduce the negative impacts of logistics on the environment and increase the cost of logistics delivery. \u0000OBJECTIVES: To address the problems of slow convergence and easy to fall into local optimization in the current performance prediction research on talent team building. \u0000METHODS: This paper proposes a snowmelt heuristic optimization algorithm to solve the green low-carbon logistics path optimization problem. Firstly, the objective function of green low-carbon logistics path optimization is designed by analyzing the optimization cost and conditional constraints of the green low-carbon logistics path optimization problem; then, a method based on intelligent optimization algorithm is proposed by designing the position-order array coding and fitness function, combined with the snow-melting optimization algorithm; finally, the validity and superiority of the proposed method are verified by simulation experiments. \u0000RESULTS: The results show that the proposed method not only improves the convergence speed but also increases the optimization fitness value. \u0000Conclusion: The problem of slow convergence and easy to fall into local optimum in the solution of green low-carbon logistics path optimization problem is solved.","PeriodicalId":502230,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"106 32","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139615528","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}
Paulo Monteiro, José Lino, Rui Esteves Araújo, Louelson Costa
In this paper, the performance analysis of Machine Learning (ML) algorithms for fault analysis in photovoltaic (PV) plants, is given for different algorithms. To make the comparison more relevant, this study is made based on a real dataset. The goal was to use electric and environmental data from a PV system to provide a framework for analysing, comparing, and discussing five ML algorithms, such as: Multilayer Perceptron (MLP), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Light Gradient Boosting Machine (LightGBM). The research findings suggest that an algorithm from the Gradient Boosting family called LightGBM can offer comparable or better performance in fault diagnosis for PV system.
{"title":"Comparison between LightGBM and other ML algorithms in PV fault classification","authors":"Paulo Monteiro, José Lino, Rui Esteves Araújo, Louelson Costa","doi":"10.4108/ew.4865","DOIUrl":"https://doi.org/10.4108/ew.4865","url":null,"abstract":"In this paper, the performance analysis of Machine Learning (ML) algorithms for fault analysis in photovoltaic (PV) plants, is given for different algorithms. To make the comparison more relevant, this study is made based on a real dataset. The goal was to use electric and environmental data from a PV system to provide a framework for analysing, comparing, and discussing five ML algorithms, such as: Multilayer Perceptron (MLP), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Light Gradient Boosting Machine (LightGBM). The research findings suggest that an algorithm from the Gradient Boosting family called LightGBM can offer comparable or better performance in fault diagnosis for PV system.","PeriodicalId":502230,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":" 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139618995","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}
Trinh Truong Cong, Thanh Nguyen Vu, Gabriel Pinto, V. D. Quoc
The permanent magnet synchronous motor (PMSM) has gained widespread popularity in various industrial applications due to its simple structure, reliable performance, compact size, high efficiency, and adaptability to different shapes and sizes. Its exceptional characteristics have made it a focal point in industrial settings. The PMSM can be categorized into two primary types based on the arrangement of the permanent magnets (PM): interior permanent magnet (IPM) and surface-mounted permanent magnet (SPM). In the IPM, the magnets are embedded into the rotor, while in SPM, they are mounted on the rotor's surface. The utilization of PMs eliminates the need for excitation currents due to their high flux density and significant coercive force. This absence of excitation losses contributes to a notable increase in efficiency. In this study, a multi-objective optimal design approach is introduced for a surface mounted PMSM, aiming to achieve maximum efficiency while minimizing material costs. The optimization task is accomplished using a genetic algorithm. Furthermore, the motor designs are simulated using the finite element method (FEM) to assess and compare designs before and after the optimization process.
{"title":"Optimization Design of Surface-mounted Permanent Magnet Synchronous Motors Using Genetic Algorithms","authors":"Trinh Truong Cong, Thanh Nguyen Vu, Gabriel Pinto, V. D. Quoc","doi":"10.4108/ew.4864","DOIUrl":"https://doi.org/10.4108/ew.4864","url":null,"abstract":"The permanent magnet synchronous motor (PMSM) has gained widespread popularity in various industrial applications due to its simple structure, reliable performance, compact size, high efficiency, and adaptability to different shapes and sizes. Its exceptional characteristics have made it a focal point in industrial settings. The PMSM can be categorized into two primary types based on the arrangement of the permanent magnets (PM): interior permanent magnet (IPM) and surface-mounted permanent magnet (SPM). In the IPM, the magnets are embedded into the rotor, while in SPM, they are mounted on the rotor's surface. The utilization of PMs eliminates the need for excitation currents due to their high flux density and significant coercive force. This absence of excitation losses contributes to a notable increase in efficiency. In this study, a multi-objective optimal design approach is introduced for a surface mounted PMSM, aiming to achieve maximum efficiency while minimizing material costs. The optimization task is accomplished using a genetic algorithm. Furthermore, the motor designs are simulated using the finite element method (FEM) to assess and compare designs before and after the optimization process.","PeriodicalId":502230,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":" 34","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139618535","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}
Chojaa Hamid, Derouich Aziz, O. Zamzoum, Abderrahman El Idrissi
This research work focuses on addressing the challenges of controlling a wind energy conversion system (WECS) connected to the grid, particularly when faced with variable wind speed profiles. The system consists of a Doubly-Fed Induction Generator (DFIG) connected to the grid through an AC/DC/AC converter, along with a Li-ion battery storage system connected to the Back-to-Back converter DC link via a DC/DC converter. The non-linearity and internal parametric variation of the wind turbine can negatively impact energy production, battery charging performance, and battery lifespan. To overcome these issues, the study proposes a robust control approach called Integral action Sliding Mode Control (ISMC) to enhance the dynamic performance of the WECS based on DFIG. Additionally, the battery charging and discharging controllers play a crucial role in efficiently distributing power to the grid and storage unit based on the battery's state of charge, extracted energy, and power injected into the grid. Two current regulation modes, buck charging and boost discharging, are employed to ensure proper energy distribution. Furthermore, a storage system energy management algorithm is implemented to ensure battery safety during one of the charging modes. The effectiveness and robustness of the proposed control method were validated through simulations of a 1.5 MW wind power conversion system using Matlab/Simulink. The results confirmed the method's efficiency and efficacy.
{"title":"Robust Control System for DFIG-Based WECS and Energy Storage in reel Wind Conditions","authors":"Chojaa Hamid, Derouich Aziz, O. Zamzoum, Abderrahman El Idrissi","doi":"10.4108/ew.4856","DOIUrl":"https://doi.org/10.4108/ew.4856","url":null,"abstract":"This research work focuses on addressing the challenges of controlling a wind energy conversion system (WECS) connected to the grid, particularly when faced with variable wind speed profiles. The system consists of a Doubly-Fed Induction Generator (DFIG) connected to the grid through an AC/DC/AC converter, along with a Li-ion battery storage system connected to the Back-to-Back converter DC link via a DC/DC converter. The non-linearity and internal parametric variation of the wind turbine can negatively impact energy production, battery charging performance, and battery lifespan. To overcome these issues, the study proposes a robust control approach called Integral action Sliding Mode Control (ISMC) to enhance the dynamic performance of the WECS based on DFIG. Additionally, the battery charging and discharging controllers play a crucial role in efficiently distributing power to the grid and storage unit based on the battery's state of charge, extracted energy, and power injected into the grid. Two current regulation modes, buck charging and boost discharging, are employed to ensure proper energy distribution. Furthermore, a storage system energy management algorithm is implemented to ensure battery safety during one of the charging modes. The effectiveness and robustness of the proposed control method were validated through simulations of a 1.5 MW wind power conversion system using Matlab/Simulink. The results confirmed the method's efficiency and efficacy.","PeriodicalId":502230,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139620686","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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