EAI Endorsed Transactions on Energy Web最新文献_第3页

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

EAI Endorsed Transactions on Energy Web Pub Date : 2024-04-12 DOI: 10.4108/ew.5751

Yuexin Jin, Jinchi Yu, Xiaoju Yin, Yuxin Wang

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

EAI Endorsed Transactions on Energy Web Pub Date : 2024-04-12 DOI: 10.4108/ew.5755

Yifeng Meng

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

EAI Endorsed Transactions on Energy Web Pub Date : 2024-04-12 DOI: 10.4108/ew.5758

Zehui Wang, Dianwei Chi

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

EAI Endorsed Transactions on Energy Web 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

{"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

EAI Endorsed Transactions on Energy Web Pub Date : 2024-04-11 DOI: 10.4108/ew.5735

Xin Wang, Xin Wan, Yaodong Hua, Yunkai Zhao, Yuxin Wang

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

EAI Endorsed Transactions on Energy Web 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

{"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

EAI Endorsed Transactions on Energy Web Pub Date : 2024-04-11 DOI: 10.4108/ew.5742

Xiaoju Yin, Yuhan Mu, Bo Li, Yuxin Wang

{"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

EAI Endorsed Transactions on Energy Web Pub Date : 2024-04-10 DOI: 10.4108/ew.5720

Miao Liu, Zesen Wang, Guangming Xin, Qi Li, Shuaihao Kong

{"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

EAI Endorsed Transactions on Energy Web Pub Date : 2024-04-10 DOI: 10.4108/ew.5721

Xiaojun Li, Y. Ni, Shuo Yang, Zhuocheng Feng, Qiang Liu, Jian Qiu, Chao Zhang

{"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

EAI Endorsed Transactions on Energy Web Pub Date : 2024-04-10 DOI: 10.4108/ew.5717

Pingli Zhang, Zhengyu Yang, Qianqian Ma, Jingjing Huang, Jia Jia, Hongchao Li, Hongfei Liu

{"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