Fault detection and diagnosis in photovoltaic panels by radiometric sensors embedded in unmanned aerial vehicles

Ramírez, Isaac Segovia; Das, Bikramaditya; Márquez, Fausto Pedro García

doi:10.1002/pip.3479

Cited by 39 publications

(22 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A comparison is made between the findings of this study and those of recent studies (Table VI) on the detection of PV bypass diodes' faults [23][24][25][26][27]. According to our knowledge, no previous work has been done on detecting short bypass diode fault identification (even though some papers claim that, but in reality, it is open bypass diode).…”

Section: E Comparative Studymentioning

confidence: 57%

See 1 more Smart Citation

Photovoltaic Bypass Diode Fault Detection Using Artificial Neural Networks

Dhimish

Tyrrell

2023

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

Due to the importance of determining faulty bypass diodes in photovoltaic systems, faulty bypass diodes have been of widespread interest in recent years due to their importance to improving PV system durability, operation, and overall safety. This paper presents new work in developing an artificial intelligence (AI) based model using the principles of artificial neural networks (ANN) to detect short and open PV bypass diodes fault conditions. With only three inputs from the PV system, namely the output power, short-circuit current, and open-circuit voltage, the developed ANN model can determine whether the PV bypass diodes are defective. In the experimentally validated case of short and open bypass diodes, 93.6% and 93.3% of faulty bypass diodes can be detected. Furthermore, the developed ANN model has an average precision and sensitivity of 96.4% and 92.6%, respectively.

show abstract

Section: E Comparative Studymentioning

confidence: 57%

“…It is, however, challenging to operate offline (using only the data from the power converters). In [24], the bypass diodes were detected using thermal testing; once again, the algorithm requires a physical inspection of the PV site, similar to those using drones for identifying failures in [26] and [27].…”

Section: E Comparative Studymentioning

confidence: 99%

Photovoltaic Bypass Diode Fault Detection Using Artificial Neural Networks

Dhimish

Tyrrell

2023

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

show abstract

“…In [35], by varying the modulation of the injected current, the panel image was made to exhibit certain features that allowed the detection of different types of shunt faults. In [36], the authors have verified that high accuracy fault identification is possible by performing thermal imaging analysis of PV panels and using radiation sensors. V. Kirubakaran et al [37] use a thermal imaging system combined with image processing to record PV panel failure points.…”

Section: Pv Panel Defect Detectionmentioning

confidence: 95%

GBH-YOLOv5: Ghost Convolution with BottleneckCSP and Tiny Target Prediction Head Incorporating YOLOv5 for PV Panel Defect Detection

Wang

Zhang

2023

Electronics

View full text Add to dashboard Cite

Photovoltaic (PV) panel surface-defect detection technology is crucial for the PV industry to perform smart maintenance. Using computer vision technology to detect PV panel surface defects can ensure better accuracy while reducing the workload of traditional worker field inspections. However, multiple tiny defects on the PV panel surface and the high similarity between different defects make it challenging to accurately identify and detect such defects. This paper proposes an approach named Ghost convolution with BottleneckCSP and a tiny target prediction head incorporating YOLOv5 (GBH-YOLOv5) for PV panel defect detection. To ensure better accuracy on multiscale targets, the BottleneckCSP module is introduced to add a prediction head for tiny target detection to alleviate tiny defect misses, using Ghost convolution to improve the model inference speed and reduce the number of parameters. First, the original image is compressed and cropped to enlarge the defect size physically. Then, the processed images are input into GBH-YOLOv5, and the depth features are extracted through network processing based on Ghost convolution, the application of the BottleneckCSP module, and the prediction head of tiny targets. Finally, the extracted features are classified by a Feature Pyramid Network (FPN) and a Path Aggregation Network (PAN) structure. Meanwhile, we compare our method with state-of-the-art methods to verify the effectiveness of the proposed method. The proposed PV panel surface-defect detection network improves the mAP performance by at least 27.8%.

show abstract

“…Create 100,000 evenly spaced points between the maximum and minimum values of the stored process variables ii. Compute p B for each of those points, using the mathematical model; that is, Equation (14).…”

Section: Sensitivity For Committer Probability (P B )mentioning

confidence: 99%

Multivariate alarm systems to recognize rare unpostulated abnormal events

Sudarshan,

Seider,

Patel

et al. 2023

AIChE Journal

View full text Add to dashboard Cite

Most chemical and manufacturing plants have safety/reliability systems in place that are well equipped to handle commonly occurring postulated abnormal events, but often prove to be ineffective in predicting highly unanticipated and randomly occurring unpostulated abnormal events. In this paper, an advisory system is developed to analyze and recognize such events, consisting of novel, multivariate alarm systems and response actions introduced using process modeling and path‐sampling for unpostulated abnormal events. It augments existing safety/reliability systems, suggesting actions when unanticipated abnormal events are approached. Forward‐flux sampling (FFS), developed to discover rare molecular dynamics pathways, is applied. With an approximate process model, for an exothermic continuous‐stirred tank reactor using a perturbed feed concentration, the FFS algorithm is applied to identify rare trajectories between high‐conversion and low‐conversion steady‐states, with key process variables saved at various “crossing points.” Then, committer probabilities, , are computed at each crossing point, yielding a mathematical model that expresses as a function of the key process variables; that is, the reactor temperature (T), cooling‐water flow rate (), and cooling‐water temperature (), selected as choices for the primary alarm variables. For these, alarm thresholds; that is, L—low, LL—low low, and LLL—low low low, are suggested by computing their critical ranges, given the ranges for every alarm threshold.

show abstract

Fault detection and diagnosis in photovoltaic panels by radiometric sensors embedded in unmanned aerial vehicles

Cited by 39 publications

References 68 publications

Photovoltaic Bypass Diode Fault Detection Using Artificial Neural Networks

Photovoltaic Bypass Diode Fault Detection Using Artificial Neural Networks

GBH-YOLOv5: Ghost Convolution with BottleneckCSP and Tiny Target Prediction Head Incorporating YOLOv5 for PV Panel Defect Detection

Multivariate alarm systems to recognize rare unpostulated abnormal events

Contact Info

Product

Resources

About