首页 > 最新文献

EAI Endorsed Transactions on Energy Web最新文献

英文 中文
Image Recognition of Photovoltaic Cell Occlusion Based on Subpixel Matching 基于子像素匹配的光伏电池遮挡图像识别
Q3 Engineering Pub Date : 2024-04-12 DOI: 10.4108/ew.5751
Yuexin Jin, Jinchi Yu, Xiaoju Yin, Yuxin Wang
INTRODUCTION: During the operation of large photovoltaic power stations, they are often shielded by dust and bird droppings, which greatly reduce the power generation and even cause fires. Analysis of PV cell occlusion image recognition accuracy based on sub-pixel matching. OBJECTIVES: In order to find the location of the pv cells, we use the method of subpixel image matching. Improve recognition accuracy. METHODS: When the power plant is running normally, taken the original image for photovoltaic power station as the original sample, and then using the subpixel gradient matching algorithm, to match the original image and find out that the minimum matching values. RESULTS: If the calculation results is greater than a specified threshold, When the calculated result is greater than the specified threshold, the power station is considered abnormal. CONCLUSION: The experimental process shows that this method can better judge the operating status of photovoltaic power station, and can find out the location of mismatched photovoltaic cells more accurately, and the calculation accuracy reaches sub-pixel level.
引言:大型光伏电站在运行过程中,经常会被灰尘和鸟粪遮挡,从而大大降低发电量,甚至引发火灾。基于子像素匹配的光伏电池遮挡图像识别精度分析。目的:为了找到光伏电池的位置,我们使用了子像素图像匹配的方法。提高识别精度。方法:在电站正常运行时,以光伏电站原始图像为原始样本,然后使用子像素梯度匹配算法,对原始图像进行匹配,并找出最小匹配值。结果:如果计算结果大于指定阈值,则认为该电站异常。结论:实验过程表明,该方法能更好地判断光伏电站的运行状态,能更准确地找出不匹配光伏电池的位置,计算精度达到亚像素级。
{"title":"Image Recognition of Photovoltaic Cell Occlusion Based on Subpixel Matching","authors":"Yuexin Jin, Jinchi Yu, Xiaoju Yin, Yuxin Wang","doi":"10.4108/ew.5751","DOIUrl":"https://doi.org/10.4108/ew.5751","url":null,"abstract":"INTRODUCTION: During the operation of large photovoltaic power stations, they are often shielded by dust and bird droppings, which greatly reduce the power generation and even cause fires. Analysis of PV cell occlusion image recognition accuracy based on sub-pixel matching. \u0000OBJECTIVES: In order to find the location of the pv cells, we use the method of subpixel image matching. Improve recognition accuracy. \u0000METHODS: When the power plant is running normally, taken the original image for photovoltaic power station as the original sample, and then using the subpixel gradient matching algorithm, to match the original image and find out that the minimum matching values. \u0000RESULTS: If the calculation results is greater than a specified threshold, When the calculated result is greater than the specified threshold, the power station is considered abnormal. \u0000CONCLUSION: The experimental process shows that this method can better judge the operating status of photovoltaic power station, and can find out the location of mismatched photovoltaic cells more accurately, and the calculation accuracy reaches sub-pixel level.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"83 S8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140709302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Maximum Power Point Tracking Control Method of Photovoltaic Cell under Shadow Influence 阴影影响下的光伏电池最大功率点跟踪控制方法
Q3 Engineering Pub Date : 2024-04-12 DOI: 10.4108/ew.5755
Yifeng Meng
In view of the poor effect of battery power tracking control in the current solar power generation system, the maximum power point tracking (MPPT) control method of photovoltaic cell under the influence of shadow is proposed. The MPPT control method of photovoltaic cell is optimized by using the influence of shadow, the structural characteristics of photovoltaic cell are optimized, and the voltage rise and fall DC / DC conversion circuit is adopted, The maximum power identification algorithm of photovoltaic cells is set, and the voltage disturbance method is used to realize the MPPT, so that the solar photovoltaic cells always maintain the maximum power output, so as to ensure the control effect. Finally, the experiment shows that the MPPT control method of photovoltaic cells has high practicability and fully meets the research requirements.
针对目前太阳能发电系统中电池功率跟踪控制效果不佳的问题,提出了阴影影响下的光伏电池最大功率点跟踪(MPPT)控制方法。利用阴影的影响优化光伏电池的MPPT控制方法,优化光伏电池的结构特性,采用电压升降直流/直流转换电路,设置光伏电池最大功率识别算法,利用电压扰动法实现MPPT,使太阳能光伏电池始终保持最大功率输出,从而保证控制效果。最后,实验表明光伏电池的MPPT控制方法具有较高的实用性,完全满足研究要求。
{"title":"Maximum Power Point Tracking Control Method of Photovoltaic Cell under Shadow Influence","authors":"Yifeng Meng","doi":"10.4108/ew.5755","DOIUrl":"https://doi.org/10.4108/ew.5755","url":null,"abstract":"In view of the poor effect of battery power tracking control in the current solar power generation system, the maximum power point tracking (MPPT) control method of photovoltaic cell under the influence of shadow is proposed. The MPPT control method of photovoltaic cell is optimized by using the influence of shadow, the structural characteristics of photovoltaic cell are optimized, and the voltage rise and fall DC / DC conversion circuit is adopted, The maximum power identification algorithm of photovoltaic cells is set, and the voltage disturbance method is used to realize the MPPT, so that the solar photovoltaic cells always maintain the maximum power output, so as to ensure the control effect. Finally, the experiment shows that the MPPT control method of photovoltaic cells has high practicability and fully meets the research requirements.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"74 S9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140709486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Research on Wind Power Prediction Model Based on Random Forest and SVR 基于随机森林和 SVR 的风能预测模型研究
Q3 Engineering Pub Date : 2024-04-12 DOI: 10.4108/ew.5758
Zehui Wang, Dianwei Chi
Wind power generation is random and easily affected by external factors. In order to construct an effective prediction model based on wind power generation, a wind power prediction model based on principal component analysis (PCA) noise reduction, feature selection based on random forest model and support vector regression (SVR) algorithm is proposed. First, in the data preprocessing stage, PCA is used for sample data denoising; then the random forest model is used to calculate the importance evaluation value of each feature to optimize the selection of feature parameters; finally, The SVR algorithm is applied for training and prediction. Experiments show that the prediction effect of the model based on random forest and SVR is excellent, the root mean square error(RMSE) is 0.086, the average absolute percentage error(MAPE) is 23.47%, and the coefficient of determination(R2) is 0.991. Compared with the traditional SVR model, the root mean square error of the method proposed in this paper is reduced by 95.9%, and the prediction accuracy and the fit of the prediction curve are significantly improved.
风力发电具有随机性,容易受到外部因素的影响。为了构建有效的风力发电量预测模型,本文提出了一种基于主成分分析(PCA)降噪、基于随机森林模型的特征选择和支持向量回归(SVR)算法的风力发电量预测模型。首先,在数据预处理阶段,利用 PCA 对样本数据进行去噪;然后,利用随机森林模型计算每个特征的重要性评估值,优化特征参数的选择;最后,应用 SVR 算法进行训练和预测。实验表明,基于随机森林和 SVR 模型的预测效果非常好,均方根误差(RMSE)为 0.086,平均绝对百分比误差(MAPE)为 23.47%,判定系数(R2)为 0.991。与传统的 SVR 模型相比,本文所提方法的均方根误差降低了 95.9%,预测精度和预测曲线拟合度均有显著提高。
{"title":"Research on Wind Power Prediction Model Based on Random Forest and SVR","authors":"Zehui Wang, Dianwei Chi","doi":"10.4108/ew.5758","DOIUrl":"https://doi.org/10.4108/ew.5758","url":null,"abstract":"Wind power generation is random and easily affected by external factors. In order to construct an effective prediction model based on wind power generation, a wind power prediction model based on principal component analysis (PCA) noise reduction, feature selection based on random forest model and support vector regression (SVR) algorithm is proposed. First, in the data preprocessing stage, PCA is used for sample data denoising; then the random forest model is used to calculate the importance evaluation value of each feature to optimize the selection of feature parameters; finally, The SVR algorithm is applied for training and prediction. Experiments show that the prediction effect of the model based on random forest and SVR is excellent, the root mean square error(RMSE) is 0.086, the average absolute percentage error(MAPE) is 23.47%, and the coefficient of determination(R2) is 0.991. Compared with the traditional SVR model, the root mean square error of the method proposed in this paper is reduced by 95.9%, and the prediction accuracy and the fit of the prediction curve are significantly improved.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"70 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140709646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Research on Surface Defect Detection Method of Photovoltaic Power Generation Panels——Comparative Analysis of Detecting Model Accuracy 光伏发电板表面缺陷检测方法研究--检测模型精度对比分析
Q3 Engineering Pub Date : 2024-04-11 DOI: 10.4108/ew.5741
Yunxin Wang, Zhi Zhang, Jialiang Zhang, Jiangning Han, Jianguo Lian, Yifeng Qi, Xiaowei Liu, Jiangyang Guo, Xiaoju Yin
INTRODUCTION: Research on intelligent defect detection technology using machine vision was conducted to address the challenging problem of detecting and localizing PV defects in photovoltaic power generation system operation and maintenance. OBJECTIVES: The aim is to improve the accuracy of PV defect detection and enhance the operation and maintenance efficiency of PV power plants. METHODS: In this paper, three detection methods such as image processing based detection, traditional machine learning based detection, and deep learning algorithm based detection are discussed and compared, and analyzed respectively. It is finally concluded that the deep learning based detection is more efficient in comparison. Then further analysis and simulation experiments are done through several detection algorithms based on deep learning. RESULTS: The experiment yields a high accuracy of the detection model based on the Faster-RCNN algorithm. Its mAP value reaches 92.6%. The detection model based on the YOLOv5 algorithm reaches a mAP value of 91.4%. But its speed is as much as 7 times faster than the model based on the Faster-RCNN algorithm. CONCLUSION: Comprehensive speed and accuracy index. Combining the needs of PV defect detection in the operation and maintenance of PV power generation systems with the results of simulation experiments. It is concluded that the detection model based on the YOLOv5 algorithm can provide better detection capability. Modeling with this algorithm is more suitable for PV defect detection.
简介:为解决光伏发电系统运行和维护中检测和定位光伏缺陷这一具有挑战性的问题,开展了利用机器视觉的智能缺陷检测技术研究。目标:目的是提高光伏缺陷检测的准确性,提高光伏电站的运行和维护效率。方法:本文对基于图像处理的检测、基于传统机器学习的检测和基于深度学习算法的检测等三种检测方法进行了讨论和比较,并分别进行了分析。最后得出结论,基于深度学习的检测方法相比之下更有效。然后通过几种基于深度学习的检测算法做了进一步的分析和模拟实验。结果:实验结果表明,基于 Faster-RCNN 算法的检测模型准确率很高。其 mAP 值达到 92.6%。基于 YOLOv5 算法的检测模型的 mAP 值达到 91.4%。但其速度比基于 Faster-RCNN 算法的模型快 7 倍之多。结论:综合速度和准确性指标。结合光伏发电系统运维中的光伏缺陷检测需求与仿真实验结果。得出结论:基于 YOLOv5 算法的检测模型能够提供更好的检测能力。使用该算法建模更适合光伏缺陷检测。
{"title":"Research on Surface Defect Detection Method of Photovoltaic Power Generation Panels——Comparative Analysis of Detecting Model Accuracy","authors":"Yunxin Wang, Zhi Zhang, Jialiang Zhang, Jiangning Han, Jianguo Lian, Yifeng Qi, Xiaowei Liu, Jiangyang Guo, Xiaoju Yin","doi":"10.4108/ew.5741","DOIUrl":"https://doi.org/10.4108/ew.5741","url":null,"abstract":"INTRODUCTION: Research on intelligent defect detection technology using machine vision was conducted to address the challenging problem of detecting and localizing PV defects in photovoltaic power generation system operation and maintenance. \u0000OBJECTIVES: The aim is to improve the accuracy of PV defect detection and enhance the operation and maintenance efficiency of PV power plants. \u0000METHODS: In this paper, three detection methods such as image processing based detection, traditional machine learning based detection, and deep learning algorithm based detection are discussed and compared, and analyzed respectively. It is finally concluded that the deep learning based detection is more efficient in comparison. Then further analysis and simulation experiments are done through several detection algorithms based on deep learning. \u0000RESULTS: The experiment yields a high accuracy of the detection model based on the Faster-RCNN algorithm. Its mAP value reaches 92.6%. The detection model based on the YOLOv5 algorithm reaches a mAP value of 91.4%. But its speed is as much as 7 times faster than the model based on the Faster-RCNN algorithm. \u0000CONCLUSION: Comprehensive speed and accuracy index. Combining the needs of PV defect detection in the operation and maintenance of PV power generation systems with the results of simulation experiments. It is concluded that the detection model based on the YOLOv5 algorithm can provide better detection capability. Modeling with this algorithm is more suitable for PV defect detection.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"72 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140713913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Design of Capacitive Power Transfer System with Small Coupling Capacitance for Wireless Power Transfer 设计具有小耦合电容的电容式无线电力传输系统
Q3 Engineering Pub Date : 2024-04-11 DOI: 10.4108/ew.5735
Xin Wang, Xin Wan, Yaodong Hua, Yunkai Zhao, Yuxin Wang
Wireless power transfer systems play an important role in the application of modern power supply technology. Wireless charging has been widely used in portable devices such as smartphones, laptops, and even some medical devices. Higher system efficiency can be achieved while reducing costs. This article describes the design of a capacitive power transfer (CPT) system using the Class-E amplifier method. When the capacitance of the coupling plate is small, the operation of Class-E amplifiers under Zero-Voltage-Switching (ZVS) conditions is very sensitive to their circuit parameters. By adding an additional capacitor to the Class-E amplifier, the coupling capacitance can be increased, resulting in better circuit performance. The high efficiency of the Class-E amplifier is verified by simulation and experimental results.
无线电力传输系统在现代供电技术的应用中发挥着重要作用。无线充电已广泛应用于智能手机、笔记本电脑等便携设备,甚至一些医疗设备。在降低成本的同时,还能实现更高的系统效率。本文介绍了使用 E 类放大器方法设计电容式功率传输(CPT)系统。当耦合板的电容较小时,E 类放大器在零电压开关(ZVS)条件下的工作对其电路参数非常敏感。通过在 E 类放大器中增加一个额外的电容器,可以增大耦合电容,从而提高电路性能。模拟和实验结果验证了 E 类放大器的高效率。
{"title":"Design of Capacitive Power Transfer System with Small Coupling Capacitance for Wireless Power Transfer","authors":"Xin Wang, Xin Wan, Yaodong Hua, Yunkai Zhao, Yuxin Wang","doi":"10.4108/ew.5735","DOIUrl":"https://doi.org/10.4108/ew.5735","url":null,"abstract":"Wireless power transfer systems play an important role in the application of modern power supply technology. Wireless charging has been widely used in portable devices such as smartphones, laptops, and even some medical devices. Higher system efficiency can be achieved while reducing costs. This article describes the design of a capacitive power transfer (CPT) system using the Class-E amplifier method. When the capacitance of the coupling plate is small, the operation of Class-E amplifiers under Zero-Voltage-Switching (ZVS) conditions is very sensitive to their circuit parameters. By adding an additional capacitor to the Class-E amplifier, the coupling capacitance can be increased, resulting in better circuit performance. The high efficiency of the Class-E amplifier is verified by simulation and experimental results.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"10 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140715451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Research Progress on Deep Learning Based Defect Detection Technology for Solar Panels 基于深度学习的太阳能电池板缺陷检测技术研究进展
Q3 Engineering Pub Date : 2024-04-11 DOI: 10.4108/ew.5740
Yuxin Wang, Jiangyang Guo, Yifeng Qi, Xiaowei Liu, Jiangning Han, Jialiang Zhang, Zhi Zhang, Jianguo Lian, Xiaoju Yin
INTRODUCTION: Based on machine vision technology to carry out photovoltaic panel defect detection technology research to solve the photovoltaic panel production line automation online defect detection and localization problems. OBJECTIVES: The goal is to improve the accuracy of defect detection on PV cell production lines, increase the speed of defect detection to meet real-time monitoring needs, and improve production efficiency. METHODS: In this paper, three detection methods such as image processing based detection, traditional machine learning based detection and deep learning algorithm based detection are discussed and compared and analyzed respectively. Finally, it is concluded that deep learning based detection methods are more effective in comparison. Then, further analysis and simulation experiments are done by several deep learning based detection algorithms. RESULTS: The experimental results show that the YOLOv8 algorithm has the highest precision rate and maintains good results in terms of recall and mAP values. The detection speed is all less than other algorithms, 10.6ms. CONCLUSION: The inspection model based on yolov8 algorithm has the highest comprehensive performance and is the most suitable algorithmic model for detecting defects in solar panels in production lines.
简介:基于机器视觉技术开展光伏面板缺陷检测技术研究,解决光伏面板生产线自动化在线缺陷检测与定位难题。目标:目标是提高光伏电池生产线缺陷检测的准确性,提高缺陷检测速度以满足实时监控需求,提高生产效率。方法:本文讨论了基于图像处理的检测、基于传统机器学习的检测和基于深度学习算法的检测等三种检测方法,并分别进行了比较和分析。最后得出结论,基于深度学习的检测方法相比之下更有效。然后,通过几种基于深度学习的检测算法做了进一步的分析和模拟实验。结果:实验结果表明,YOLOv8 算法的精确率最高,在召回率和 mAP 值方面也保持了良好的结果。检测速度均低于其他算法,为 10.6ms。结论:基于 yolov8 算法的检测模型具有最高的综合性能,是最适合检测生产线上太阳能电池板缺陷的算法模型。
{"title":"Research Progress on Deep Learning Based Defect Detection Technology for Solar Panels","authors":"Yuxin Wang, Jiangyang Guo, Yifeng Qi, Xiaowei Liu, Jiangning Han, Jialiang Zhang, Zhi Zhang, Jianguo Lian, Xiaoju Yin","doi":"10.4108/ew.5740","DOIUrl":"https://doi.org/10.4108/ew.5740","url":null,"abstract":"INTRODUCTION: Based on machine vision technology to carry out photovoltaic panel defect detection technology research to solve the photovoltaic panel production line automation online defect detection and localization problems. \u0000OBJECTIVES: The goal is to improve the accuracy of defect detection on PV cell production lines, increase the speed of defect detection to meet real-time monitoring needs, and improve production efficiency. \u0000METHODS: In this paper, three detection methods such as image processing based detection, traditional machine learning based detection and deep learning algorithm based detection are discussed and compared and analyzed respectively. Finally, it is concluded that deep learning based detection methods are more effective in comparison. Then, further analysis and simulation experiments are done by several deep learning based detection algorithms. \u0000RESULTS: The experimental results show that the YOLOv8 algorithm has the highest precision rate and maintains good results in terms of recall and mAP values. The detection speed is all less than other algorithms, 10.6ms. \u0000CONCLUSION: The inspection model based on yolov8 algorithm has the highest comprehensive performance and is the most suitable algorithmic model for detecting defects in solar panels in production lines.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"25 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140714160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Analysis and Design of Wind Turbine Monitoring System Based on Edge Computing 基于边缘计算的风力涡轮机监控系统分析与设计
Q3 Engineering Pub Date : 2024-04-11 DOI: 10.4108/ew.5742
Xiaoju Yin, Yuhan Mu, Bo Li, Yuxin Wang
INTRODUCTION: A wind turbine data analysis method based on the combination of Hadoop and edge computing is proposed. OBJECTIVES: Solve the wind turbine health status monitoring system large data, time extension, energy consumption and other problems. METHODS: By analysing the technical requirements and business processes of the system, the overall framework of the system was designed and a deep reinforcement learning algorithm based on big data was proposed. RESULTS: It solves the problem of insufficient computing resources as well as energy consumption and latency problems occurring in the data analysis layer, solves the problems in WTG task offloading, and improves the computational offloading efficiency of the edge nodes to complete the collection, storage, and analysis of WTG data. CONCLUSION: The data analysis and experimental simulation platform is built through Python, and the results show that the application of Hadoop and the edge computing offloading strategy based on the DDPG algorithm to the system improves the system's quality of service and computational performance, and the method is applicable to the distributed storage and analysis of the device in the massive monitoring data.
简介:本文提出了一种基于 Hadoop 和边缘计算相结合的风力涡轮机数据分析方法。目标: 解决风机健康状态监测系统数据量大、时间延长、能耗大等问题:解决风机健康状态监测系统数据量大、时间延长、能耗大等问题。方法:通过分析系统的技术要求和业务流程,设计了系统的整体框架,并提出了基于大数据的深度强化学习算法。结果:解决了计算资源不足的问题以及数据分析层出现的能耗和延迟问题,解决了风电机组任务卸载的问题,提高了边缘节点的计算卸载效率,完成了风电机组数据的采集、存储和分析。结论:通过Python搭建了数据分析和实验仿真平台,结果表明,将Hadoop和基于DDPG算法的边缘计算卸载策略应用到系统中,提高了系统的服务质量和计算性能,该方法适用于海量监测数据中设备的分布式存储和分析。
{"title":"Analysis and Design of Wind Turbine Monitoring System Based on Edge Computing","authors":"Xiaoju Yin, Yuhan Mu, Bo Li, Yuxin Wang","doi":"10.4108/ew.5742","DOIUrl":"https://doi.org/10.4108/ew.5742","url":null,"abstract":"INTRODUCTION: A wind turbine data analysis method based on the combination of Hadoop and edge computing is proposed. \u0000OBJECTIVES: Solve the wind turbine health status monitoring system large data, time extension, energy consumption and other problems. \u0000METHODS: By analysing the technical requirements and business processes of the system, the overall framework of the system was designed and a deep reinforcement learning algorithm based on big data was proposed. \u0000RESULTS: It solves the problem of insufficient computing resources as well as energy consumption and latency problems occurring in the data analysis layer, solves the problems in WTG task offloading, and improves the computational offloading efficiency of the edge nodes to complete the collection, storage, and analysis of WTG data. \u0000CONCLUSION: The data analysis and experimental simulation platform is built through Python, and the results show that the application of Hadoop and the edge computing offloading strategy based on the DDPG algorithm to the system improves the system's quality of service and computational performance, and the method is applicable to the distributed storage and analysis of the device in the massive monitoring data.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"11 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140713031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Investigation of Quantitative Assessment Techniques for Supply-Regulation Capability in Multi-Scenario New-Type Power Systems 多情景新型电力系统供应调节能力定量评估技术研究
Q3 Engineering Pub Date : 2024-04-10 DOI: 10.4108/ew.5720
Miao Liu, Zesen Wang, Guangming Xin, Qi Li, Shuaihao Kong
This paper offers an in-depth investigation into various quantitative assessment methods used to quantify the supply regulation capacity in new types of power systems under different conditions. As new forms of energy, including renewables, are increasingly becoming the predominant sources of power systems, the traditional systems are undergoing transformative modifications to efficiently address the issue of power generation and consumption fluctuations. In this regard, this paper proposes an original framework that combines advanced statistical methods and machine learning. The primary purpose of the framework is to identify the level of resilience and flexible adaptability of new power systems. The paper presents the results of the simulations and real-world applications of the proposed measurement methods in enhancing power supply reliability and efficiency in all conditions. The implications based on the results will be beneficial to policymakers and other specialists who are making decisions involving designing and optimizing modern power systems. Furthermore, the paper aims to contribute to the existing discussion by providing further insights into the effectiveness of the proposed methods of measurement.
本文深入探讨了用于量化不同条件下新型电力系统供电调节能力的各种定量评估方法。随着包括可再生能源在内的新型能源日益成为电力系统的主要能源,传统系统正在经历转型,以有效解决发电和用电波动问题。为此,本文提出了一个结合先进统计方法和机器学习的原创框架。该框架的主要目的是确定新电力系统的弹性和灵活适应性水平。本文介绍了所提出的测量方法在提高各种条件下的供电可靠性和效率方面的模拟和实际应用结果。基于这些结果的影响将有益于政策制定者和其他专家在设计和优化现代电力系统时做出决策。此外,本文还旨在进一步深入探讨拟议测量方法的有效性,从而为现有讨论做出贡献。
{"title":"Investigation of Quantitative Assessment Techniques for Supply-Regulation Capability in Multi-Scenario New-Type Power Systems","authors":"Miao Liu, Zesen Wang, Guangming Xin, Qi Li, Shuaihao Kong","doi":"10.4108/ew.5720","DOIUrl":"https://doi.org/10.4108/ew.5720","url":null,"abstract":"This paper offers an in-depth investigation into various quantitative assessment methods used to quantify the supply regulation capacity in new types of power systems under different conditions. As new forms of energy, including renewables, are increasingly becoming the predominant sources of power systems, the traditional systems are undergoing transformative modifications to efficiently address the issue of power generation and consumption fluctuations. In this regard, this paper proposes an original framework that combines advanced statistical methods and machine learning. The primary purpose of the framework is to identify the level of resilience and flexible adaptability of new power systems. The paper presents the results of the simulations and real-world applications of the proposed measurement methods in enhancing power supply reliability and efficiency in all conditions. The implications based on the results will be beneficial to policymakers and other specialists who are making decisions involving designing and optimizing modern power systems. Furthermore, the paper aims to contribute to the existing discussion by providing further insights into the effectiveness of the proposed methods of measurement.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"9 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140720259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Development of an Energy Planning Model Using Temporal Production Simulation and Enhanced NSGA-III 利用时态生产模拟和增强型 NSGA-III 开发能源规划模型
Q3 Engineering Pub Date : 2024-04-10 DOI: 10.4108/ew.5721
Xiaojun Li, Y. Ni, Shuo Yang, Zhuocheng Feng, Qiang Liu, Jian Qiu, Chao Zhang
This paper presents an innovative model of Energy Planning Model which allows navigating the complexities of modern energy systems. Our model utilizes a combination of Temporal Production Simulation and an Enhanced Non-Dominated Sorting Genetic Algorithm III to address the challenge associated with fluctuating energy demands and renewable sources integration. The model represents a significant advancement in energy planning due to its capacity to simulate energy production and consumption dynamics over time. The unique feature of the model is based on Temporal Production Simulation, meaning that the model is capable of accounting for hourly, daily, and seasonal fluctuations in energy supply and demand. Such temporal sensitivity is crucial for optimization in systems with high percentages of intermittent renewable sources, as existing planning solutions largely ignore such fluctuations. Another component of the model is the Enhanced NSGA-III algorithm that is uniquely tailored for the nature of multi-objective energy planning where one must balance their cost, environmental performance, and reliability. We have developed improvements to NSGAIII to enhance its efficiency when navigating the complex decision space associated with energy planning to reach faster convergence and to explore more optimal solutions. Methodologically, we use a combination of in-depth problem definition approach, advanced simulation, and algorithmic adjustments. We have validated our model against existing models and testing it in various scenarios to illustrate its superior ability to reach optimal energy plans based on efficiency, sustainability, and reliability under various conditions. Overall, through its unique incorporation of the Temporal Production Simulation and an improved optimization algorithm, the Energy Planning Model provides novel insights and practical decision support for policymakers and energy planners developed to reach the optimal sustainable solutions required for the high penetration of renewables.
本文介绍了一种创新的能源规划模型,该模型可以应对现代能源系统的复杂性。我们的模型结合使用了时序生产模拟和增强型非支配排序遗传算法 III,以应对与能源需求波动和可再生能源整合相关的挑战。由于该模型能够模拟随时间变化的能源生产和消费动态,因此是能源规划领域的一大进步。该模型的独特之处在于基于时间生产模拟,这意味着该模型能够考虑能源供应和需求的小时、日和季节性波动。这种时间敏感性对于间歇性可再生能源比例较高的系统优化至关重要,因为现有的规划解决方案在很大程度上忽略了这种波动。该模型的另一个组成部分是增强型 NSGA-III 算法,该算法是针对多目标能源规划的性质量身定制的,在多目标能源规划中,必须平衡成本、环境性能和可靠性。我们对 NSGAIII 进行了改进,以提高其在浏览与能源规划相关的复杂决策空间时的效率,从而实现更快的收敛并探索更多的最优解决方案。在方法上,我们结合使用了深入的问题定义方法、高级模拟和算法调整。我们根据现有模型对我们的模型进行了验证,并在各种情况下对其进行了测试,以说明其在各种条件下实现基于效率、可持续性和可靠性的最优能源规划的卓越能力。总之,能源规划模型通过其独特的时序生产模拟和改进的优化算法,为政策制定者和能源规划者提供了新颖的见解和实用的决策支持,以实现可再生能源高度渗透所需的最佳可持续解决方案。
{"title":"Development of an Energy Planning Model Using Temporal Production Simulation and Enhanced NSGA-III","authors":"Xiaojun Li, Y. Ni, Shuo Yang, Zhuocheng Feng, Qiang Liu, Jian Qiu, Chao Zhang","doi":"10.4108/ew.5721","DOIUrl":"https://doi.org/10.4108/ew.5721","url":null,"abstract":"This paper presents an innovative model of Energy Planning Model which allows navigating the complexities of modern energy systems. Our model utilizes a combination of Temporal Production Simulation and an Enhanced Non-Dominated Sorting Genetic Algorithm III to address the challenge associated with fluctuating energy demands and renewable sources integration. The model represents a significant advancement in energy planning due to its capacity to simulate energy production and consumption dynamics over time. The unique feature of the model is based on Temporal Production Simulation, meaning that the model is capable of accounting for hourly, daily, and seasonal fluctuations in energy supply and demand. Such temporal sensitivity is crucial for optimization in systems with high percentages of intermittent renewable sources, as existing planning solutions largely ignore such fluctuations. Another component of the model is the Enhanced NSGA-III algorithm that is uniquely tailored for the nature of multi-objective energy planning where one must balance their cost, environmental performance, and reliability. We have developed improvements to NSGAIII to enhance its efficiency when navigating the complex decision space associated with energy planning to reach faster convergence and to explore more optimal solutions. Methodologically, we use a combination of in-depth problem definition approach, advanced simulation, and algorithmic adjustments. We have validated our model against existing models and testing it in various scenarios to illustrate its superior ability to reach optimal energy plans based on efficiency, sustainability, and reliability under various conditions. Overall, through its unique incorporation of the Temporal Production Simulation and an improved optimization algorithm, the Energy Planning Model provides novel insights and practical decision support for policymakers and energy planners developed to reach the optimal sustainable solutions required for the high penetration of renewables.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140717399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Study on the Influence of Land Use Change on Carbon Emissions Using System Modeling under the Framework of Dual Carbon Goals 双碳目标框架下利用系统建模研究土地利用变化对碳排放的影响
Q3 Engineering Pub Date : 2024-04-10 DOI: 10.4108/ew.5717
Pingli Zhang, Zhengyu Yang, Qianqian Ma, Jingjing Huang, Jia Jia, Hongchao Li, Hongfei Liu
At the crucial period of addressing climate change, especially to the carbonization of land use change, it is vital that relevant actions are taken to enable two ambitious dual-carbon goals, namely, ensuring that carbon emissions peak before 2030 and achieving carbon neutrality before 2060. This research investigates the impacts of land use changes on carbon emissions using a novel approach that integrates Light Detection and Ranging (LiDAR) with Geographic Information System (GIS). This approach is innovative due to its high quality three-dimensional representation to quantified exact carbon stock and forest emissions occurring due to specific land-use change. Therefore, through actual LiDAR, this research helps demarcate the pattern emitting different land-use measures, including deforestation, urban programs, agricultural differences, and forest and land changes, over historical change records and verified carbonization formulas. Similar qualitative levels between LiDAR and GIS analysis help determine the varying degrees of carbonization occurring due to enhanced deforestation, urban additions, and agricultural contributions while reporting the possible procedural carbons acquired during reforestation and other measurements. The results helped clarify that the most distinct level of land utilization shows the least level of carbon sent into the air. Therefore, the implication is that strategic land use measures and better working conditions can curb carbon indications. These signals support land-use policy and preparedness goals in a low carbon level. This study creates valuable records for the land utilization and cartograph, created through the power of LiDAR and GIS analysis.
在应对气候变化,特别是土地利用变化碳化的关键时期,必须采取相关行动,以实现两个宏伟的双碳目标,即确保在 2030 年之前碳排放达到峰值,以及在 2060 年之前实现碳中和。本研究采用光探测与测距(LiDAR)与地理信息系统(GIS)相结合的新方法,调查土地利用变化对碳排放的影响。这种方法的创新之处在于其高质量的三维表现形式,可精确量化特定土地利用变化导致的碳储量和森林排放量。因此,通过实际的激光雷达,该研究有助于根据历史变化记录和经过验证的碳化公式,划定不同土地利用措施的排放模式,包括森林砍伐、城市项目、农业差异以及森林和土地变化。激光雷达和地理信息系统分析之间相似的定性水平有助于确定由于加强森林砍伐、城市增加和农业贡献而出现的不同程度的碳化,同时报告在重新造林和其他测量过程中可能获得的程序性碳。研究结果表明,最独特的土地利用方式所排放到空气中的碳量最少。因此,这意味着战略性的土地利用措施和更好的工作条件可以抑制碳排放。这些信号支持低碳水平下的土地利用政策和准备目标。这项研究通过激光雷达和地理信息系统的分析,为土地利用和制图创造了宝贵的记录。
{"title":"Study on the Influence of Land Use Change on Carbon Emissions Using System Modeling under the Framework of Dual Carbon Goals","authors":"Pingli Zhang, Zhengyu Yang, Qianqian Ma, Jingjing Huang, Jia Jia, Hongchao Li, Hongfei Liu","doi":"10.4108/ew.5717","DOIUrl":"https://doi.org/10.4108/ew.5717","url":null,"abstract":"At the crucial period of addressing climate change, especially to the carbonization of land use change, it is vital that relevant actions are taken to enable two ambitious dual-carbon goals, namely, ensuring that carbon emissions peak before 2030 and achieving carbon neutrality before 2060. This research investigates the impacts of land use changes on carbon emissions using a novel approach that integrates Light Detection and Ranging (LiDAR) with Geographic Information System (GIS). This approach is innovative due to its high quality three-dimensional representation to quantified exact carbon stock and forest emissions occurring due to specific land-use change. Therefore, through actual LiDAR, this research helps demarcate the pattern emitting different land-use measures, including deforestation, urban programs, agricultural differences, and forest and land changes, over historical change records and verified carbonization formulas. Similar qualitative levels between LiDAR and GIS analysis help determine the varying degrees of carbonization occurring due to enhanced deforestation, urban additions, and agricultural contributions while reporting the possible procedural carbons acquired during reforestation and other measurements. The results helped clarify that the most distinct level of land utilization shows the least level of carbon sent into the air. Therefore, the implication is that strategic land use measures and better working conditions can curb carbon indications. These signals support land-use policy and preparedness goals in a low carbon level. This study creates valuable records for the land utilization and cartograph, created through the power of LiDAR and GIS analysis.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"313 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140719488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
期刊
EAI Endorsed Transactions on Energy Web
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1