지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
오류제보하기
Cells of a PV (photovoltaic) module can suffer defects due to various causes resulting in a loss of power output. As a malfunctioning cell has a higher temperature than adjacent normal cells, it can be easily detected with a thermal infrared sensor. A conventional method of PV cell inspection is to use a hand-held infrared sensor for visual inspection. The main disadvantages of this method, when applied to a large-scale PV power plant, are that it is time-consuming and costly. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule was applied to automatically detect defective panels using the mean intensity and standard deviation range of each panel by array. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97% or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule. In this study, we used a panel area extraction method that we previously developed; fault detection accuracy would be improved if panel area extraction from images was more precise. Furthermore, the proposed algorithm contributes to the development of a maintenance and repair system for large-scale PV power plants, in combination with a geo-referencing algorithm for accurate determination of panel locations using sensor-based orientation parameters and photogrammetry from ground control points.
목차
Abstract
1. Introduction
2. Analysis of PV Panel Intensity Characteristics for a Thermal Infrared Image
3. Development and Evaluation of the Fault Diagnosis Algorithm
4. Conclusions
References
참고문헌 (13)
참고문헌 신청
[학술지(정기간행물)] - Bazilian, M. D. / 2002 / Thermographic analysis of a building integrated photovoltaic system / Renewable Energy 26 (3) : 449 ~ 461
[학술대회논문] - Buerhop, C. / 2014 / Field inspection of PV-modules using aerial, drone-mounted thermography / Proceedings of the 29th European Photovoltaic Solar Energy Conference and Exhibition (EUPVSEC) : 2975 ~ 2979
[학술지(정기간행물)] - Canny, J. / 1986 / A computational approach to edge detection / IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 8 (6) : 679 ~ 698
[단행본] - Gonzalez, R. C. / 2004 / Digital Image Processing using Matlab / Pearson Prentice Hall
[학술지(정기간행물)] - Grimaccia, F. / 2015 / Planning for PV plant performance monitoring by means of unmanned aerial systems (UAS) / International Journal of Energy and Environmental Engineering 6 (1) : 47 ~ 54
[학술대회논문] - Buerhop, C. / 2014 / Field inspection of PV-modules using aerial, drone-mounted thermography / Proceedings of the 29th European Photovoltaic Solar Energy Conference and Exhibition (EUPVSEC) : 2975 ~ 2979
[학술지(정기간행물)] - Canny, J. / 1986 / A computational approach to edge detection / IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 8 (6) : 679 ~ 698
[단행본] - Gonzalez, R. C. / 2004 / Digital Image Processing using Matlab / Pearson Prentice Hall
[학술지(정기간행물)] - Grimaccia, F. / 2015 / Planning for PV plant performance monitoring by means of unmanned aerial systems (UAS) / International Journal of Energy and Environmental Engineering 6 (1) : 47 ~ 54
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