Research on Surface Defect Detection Method of Photovoltaic Power Generation Panels——Comparative Analysis of Detecting Model Accuracy

Q3 Engineering

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

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引用次数: 0

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. 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.

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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

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。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EAI Endorsed Transactions on Energy Web Energy-Energy Engineering and Power Technology

CiteScore

2.60

自引率

0.00%

发文量

审稿时长

10 weeks

期刊介绍： With ICT pervading everyday objects and infrastructures, the ‘Future Internet’ is envisioned to undergo a radical transformation from how we know it today (a mere communication highway) into a vast hybrid network seamlessly integrating knowledge, people and machines into techno-social ecosystems whose behaviour transcends the boundaries of today’s engineering science. As the internet of things continues to grow, billions and trillions of data bytes need to be moved, stored and shared. The energy thus consumed and the climate impact of data centers are increasing dramatically, thereby becoming significant contributors to global warming and climate change. As reported recently, the combined electricity consumption of the world’s data centers has already exceeded that of some of the world''s top ten economies. In the ensuing process of integrating traditional and renewable energy, monitoring and managing various energy sources, and processing and transferring technological information through various channels, IT will undoubtedly play an ever-increasing and central role. Several technologies are currently racing to production to meet this challenge, from ‘smart dust’ to hybrid networks capable of controlling the emergence of dependable and reliable green and energy-efficient ecosystems – which we generically term the ‘energy web’ – calling for major paradigm shifts highly disruptive of the ways the energy sector functions today. The EAI Transactions on Energy Web are positioned at the forefront of these efforts and provide a forum for the most forward-looking, state-of-the-art research bringing together the cross section of IT and Energy communities. The journal will publish original works reporting on prominent advances that challenge traditional thinking.