Early defect diagnosis in installed PV modules exploiting spatio-temporal information from thermal images

Rogotis, Savvas; Ioannidis, Dimosthenis; Tsolakis, Apostolos C.; Likothanassis, Spiridon D.

doi:10.21611/qirt.2014.038

Cited by 11 publications

(6 citation statements)

References 29 publications

(36 reference statements)

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“…Rogotis et al [ 5 ] developed yet another PV defect detection algorithm based on classical techniques. They adopt a two-step approach in which they first localize the PV modules using image segmentation based on Otsu thresholding.…”

Section: Related Workmentioning

confidence: 99%

“…For that reason, it is beneficial to use infrared thermography to accurately identify and locate faults and defects in PV plants. Unfortunately, many techniques for PV inspection that use thermographic images acquired by drones are based on the analysis of the temperature distribution [ 5 , 6 , 7 ]. The main problem with these is that they suffer from thermal drift or varying ambient temperatures making them less reliable.…”

Section: Introductionmentioning

confidence: 99%

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Region-Based CNN for Anomaly Detection in PV Power Plants Using Aerial Imagery

Vlaminck

Heidbuchel²,

Philips

et al. 2022

Sensors

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Today, solar energy is taking an increasing share of the total energy mix. Unfortunately, many operational photovoltaic plants suffer from a plenitude of defects resulting in non-negligible power loss. The latter highly impacts the overall performance of the PV site; therefore, operators need to regularly inspect their solar parks for anomalies in order to prevent severe performance drops. As this operation is naturally labor-intensive and costly, we present in this paper a novel system for improved PV diagnostics using drone-based imagery. Our solution consists of three main steps. The first step locates the solar panels within the image. The second step detects the anomalies within the solar panels. The final step identifies the root cause of the anomaly. In this paper, we mainly focus on the second step comprising the detection of anomalies within solar panels, which is done using a region-based convolutional neural network (CNN). Experiments on six different PV sites with different specifications and a variety of defects demonstrate that our anomaly detector achieves a true positive rate or recall of more than 90% for a false positive rate of around 2% to 3% tested on a dataset containing nearly 9000 solar panels. Compared to the best state-of-the-art methods, the experiments revealed that we achieve a slightly higher true positive rate for a substantially lower false positive rate, while tested on a more realistic dataset.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Region-Based CNN for Anomaly Detection in PV Power Plants Using Aerial Imagery

Vlaminck

Heidbuchel²,

Philips

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…In (Tsanakas et al, 2015) and in (Rogotis et al, 2014),the authors have presented methods for the ROI extraction from terrestrial thermal infrared image sequences, applying image segmentation techniques (Gonzalez et al, 2004).…”

Section: Related Workmentioning

confidence: 99%

Deep Convolutional Neural Network for Automatic Detection of Damaged Photovoltaic Cells

Pierdicca

Malinverni

Piccinini

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

114

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The number of distributed Photovoltaic (PV) plants that produce electricity has been significantly increased, and issue of monitoring and maintaining a PV plant has become of great importance and involves many challenges as efficiency, reliability, safety, and stability. This paper presents the novel approach to estimate the PV cells degradations with DCNNs. While many studies have performed images classification, to the best of our knowledge, this is the first exploitation of data acquired with a drone equipped with a thermal infrared sensor. The experiments on “Photovoltaic images Dataset”, a collected dataset, are presented to show the degradation problem and comprehensively evaluate the method presented in this research. Results in terms of precision, recall and F1-score show the effectiveness and the suitability of the proposed approach.

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“…The first one is technology for automatically extracting the ROI (region of interest) of a PV array field from the given images. Tsanakas et al (2015) and Rogotis et al (2014) presented methods for extracting the ROI from terrestrial thermal infrared image sequences using the Canny edge operator (Canny, 1986) or image segmentation techniques (Gonzalez et al, 2004). More recently, Kim et al (2016aKim et al ( , 2016b proposed an algorithm for panel area extraction from thermal infrared images captured with a UAV using the Canny edge operator and image segmentation techniques.…”

Section: Introductionmentioning

confidence: 99%

Automatic Fault Recognition of Photovoltaic Modules Based on Statistical Analysis of Uav Thermography

Kim

Youn

Kim

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:As a malfunctioning PV (Photovoltaic) cell has a higher temperature than adjacent normal cells, we can detect it easily with a thermal infrared sensor. However, it will be a time-consuming way to inspect large-scale PV power plants by a hand-held thermal infrared sensor. 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 based on the mean intensity and standard deviation range was developed to detect defective PV modules from individual array automatically. 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.

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Early defect diagnosis in installed PV modules exploiting spatio-temporal information from thermal images

Cited by 11 publications

References 29 publications

Region-Based CNN for Anomaly Detection in PV Power Plants Using Aerial Imagery

Region-Based CNN for Anomaly Detection in PV Power Plants Using Aerial Imagery

Deep Convolutional Neural Network for Automatic Detection of Damaged Photovoltaic Cells

Automatic Fault Recognition of Photovoltaic Modules Based on Statistical Analysis of Uav Thermography

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