[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126644-en":3,"doc-seo-126644-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126644,549768064778,"Finn","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Design of Edge Computing System for Photovoltaic Panel Hot Spot Detection Based on Machine Learning - Edge Computing System Design","Photovoltaic panel hot spot effect is a local overheating caused by partial coverage, reducing power generation efficiency and threatening panel service life. A machine learning–based edge computing system is designed to detect hot spot severity and locate hot spots in real time using sensing data such as current, voltage, and illuminance. The architecture includes data acquisition, edge data processing, and cloud-backed data display for remote operation and maintenance.","Design of Edge Computing System for Photovoltaic Panel Hot Spot Detection Based on Machine Learning  \nJiecong Cai 1*, Xutao Guo 1 and Siyi Chen 1  \n1 State Grid Zhejiang Electric Power Co., Ltd. Research Institute  \nAbstract. The hot spot effect of photovoltaic panel refers to the local heating phenomenon caused by the  \nphotovoltaic panel being covered, which not only seriously affects the power generation efficiency of  \nphotovoltaic panel, but also is one of the most important factors threatening the service life of photovoltaic  \npanel. In this paper, an edge computing system was designed to detect hot spot effect based on real-time  \nsensing data such as current, voltage and illuminance. The system consists of three parts: data acquisition side,  \ndata processing side and data display side. The hot spot detection algorithm model based on machine learning  \nis deployed on the edge side, which can detect the degree of hot spot effect and locate the hot spot according  \nto the sensor data of each photovoltaic panel in real time. Additionally, this system could push the data to the  \ncloud management platform and each user terminal to realize remote operation and maintenance.  \n1.Introduction  \nAs a renewable and clean energy source [1], photovoltaic energy has gradually become an important alternative to traditional energy sources to achieve the \"carbon neutral\"and \"carbon peaking\" goals [2] . During the use of photovoltaic panels, the panel defects caused by various environmental factors will directly affect the power generation efficiency [3] . The most common and most harmful is the hot spot effect of photovoltaic panels. Hotspot effect refers to the heat generation phenomenon caused by the partial shading of the photovoltaic module [4] . The severity of the hot spot effect varies and affects the working performance of photovoltaic panels differently. At present, the detection for hot spot effect generally adopts aerial photography inspection by unmanned aerial vehicle equipped with thermal radiation infrared imager, which transforms the invisible infrared energy emitted by objects into visible thermal images [5], sends the captured infrared images of photovoltaic panels to the background, and identifies and locates hot spot panels by manual analysis. The method is inefficient, not real-time, and the unmanned aerial vehicle work is vulnerable to weather [6] . In this paper, we design and implement a machine learning based edge computing system for hot spot detection of photovoltaic panels. The system contains hardware facilities such as sensors, edge computing motherboards and user terminals, as well as software such as signal processing, algorithmic models and user interaction. The system will input the real-time current, voltage and illumination data collected by the  \nsensors of photovoltaic panel array into the algorithm model, calculate the degree of hot spot effect of the photovoltaic panel, and push the data to the server of the cloud management platform, and a variety of user terminals can download the information for the degree of hot spot effect on the photovoltaic panel from the above platform to realize remote operation and maintenance.  \n2.Hardware design of the system  \n2.1. System Composition  \nThe system consists of three parts: data acquisition side, data processing side and data display side (Figure 1) . The data acquisition side mainly consists of sensors installed on each photovoltaic panel, including voltage sensors, current sensors and illuminance sensors, which are used to collect voltage, current and illuminance data from the photovoltaic panels. Data processing side including Raspberry Pi computer, 5G data transmission module and power supply module for receiving, processing and sending data. The data display side includes two parts: remote wireless terminal and touch screen for information display and user interaction. The system uploads data to the cloud management platform in real time through ","cbCaiqA7Kn6dzTse","https://ap.wps.com/l/cbCaiqA7Kn6dzTse","pdf",441177,1,4,"English","en",105,"# Abstract\n# Introduction\n# Hardware design of the system\n## System Composition\n## System hardware selection\n## System deployment and connectivity","[{\"question\":\"What causes the hot spot effect in photovoltaic panels?\",\"answer\":\"Hot spot effect is local heat generation caused by partial shading or coverage of the photovoltaic module, which can vary in severity.\"},{\"question\":\"How does the proposed system detect and locate hot spots?\",\"answer\":\"It inputs real-time current, voltage, and illumination data into a machine learning model deployed on the edge side to calculate hot spot severity and locate hot spot panels.\"},{\"question\":\"What are the main components and data flow of the system?\",\"answer\":\"The system is organized into data acquisition, data processing, and data display sides, with real-time uploading via a 5G transmission module to a cloud management platform for remote monitoring and maintenance.\"}]","Design of Edge Computing System for Photovoltaic Panel Hot Spot Detection Based on Machine Learning - 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