A fault locating method for PV arrays based on improved voltage sensor placement

Pei, Tingting; Zhang, Jiangfeng; Li, Li; Hao, Xiaohong

doi:10.1016/j.solener.2020.03.019

Cited by 30 publications

(10 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the algorithm complexity is O(mn(log(m) + 1)), where the logarithmic term comes from the ordering required in line 9 in Algorithm 1. Notice that in other research papers such as [7,32] or specialize reviews such as [8,23], the computational complexity of the solutions are not addressed.…”

Section: Discussionmentioning

confidence: 99%

“…Imaging methods are currently expensive and time consuming, whereas electrical characterization is cheaper and more flexible [24]. The latter methods also can be further split into several branches: signal processing and statistical methods (e.g., [8,25,26]), i − v curve characteristics analysis (e.g., [27,28]), power losses analysis or efficiency analysis (e.g., [17,29,30]), current and/or voltage measurements (e.g., [31,32]), and artificial intelligent methods (e.g., [9,33,34]). The diagnosis algorithm implemented in this work is based on the IF-THEN rules presented by the authors in [35] and follows the diagnosis branch based on current-voltage measurements.…”

Section: Automated Fault Management System Approachmentioning

confidence: 99%

See 1 more Smart Citation

Automated Fault Management System in a Photovoltaic Array: A Reconfiguration-Based Approach

Murillo-Soto

Meza

2021

Energies

View full text Add to dashboard Cite

This work proposes an automated reconfiguration system to manage two types of faults in any position inside the solar arrays. The faults studied are the short-circuit to ground and the open wires in the string. These faults were selected because they severely affect power production. By identifying the affected panels and isolating the faulty one, it is possible to recover part of the power loss. Among other types of faults that the system can detect and locate are: diode short-circuit, internal open-circuit, and the degradation of the internal parasitic serial resistance. The reconfiguration system can detect, locate the above faults, and switch the distributed commutators to recover most of the power loss. Moreover, the system can return automatically to the previous state when the fault has been repaired. A SIMULINK model has been built to prove this automatic system, and a simulated numerical experiment has been executed to test the system response to the faults mentioned. The results show that the recovery of power is more than 90%, and the diagnosis accuracy and sensitivity are both 100% for this numerical experiment.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Automated Fault Management System Approachmentioning

confidence: 99%

Automated Fault Management System in a Photovoltaic Array: A Reconfiguration-Based Approach

Murillo-Soto

Meza

2021

Energies

View full text Add to dashboard Cite

show abstract

“…The fault diagnosis of photovoltaic array can be carried out by analyzing the variation of time series waveform. The authors of [19] deployed the voltage sensor by distinguishing the odevity of the number of the photovoltaic array string, meanwhile, formulated the positioning rules for each fault to achieve accurate location of faults. Abd El-Ghany et al [20] installed a diode in each photovoltaic string and adopted the change rate of instantaneous voltage and current to detect the fault of photovoltaic system.…”

Section: Introductionmentioning

confidence: 99%

Ghost-RetinaNet: Fast Shadow Detection Method for Photovoltaic Panels Based on Improved RetinaNet

Wu¹,

Fan²,

Sun³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Based on the artificial intelligence algorithm of RetinaNet, we propose the Ghost-RetinaNet in this paper, a fast shadow detection method for photovoltaic panels, to solve the problems of extreme target density, large overlap, high cost and poor real-time performance in photovoltaic panel shadow detection. Firstly, the Ghost CSP module based on Cross Stage Partial (CSP) is adopted in feature extraction network to improve the accuracy and detection speed. Based on extracted features, recursive feature fusion structure is mentioned to enhance the feature information of all objects. We introduce the SiLU activation function and CIoU Loss to increase the learning and generalization ability of the network and improve the positioning accuracy of the bounding box regression, respectively. Finally, in order to achieve fast detection, the Ghost strategy is chosen to lighten the size of the algorithm. The results of the experiment show that the average detection accuracy (mAP) of the algorithm can reach up to 97.17%, the model size is only 8.75 MB and the detection speed is highly up to 50.8 Frame per second (FPS), which can meet the requirements of real-time detection speed and accuracy of photovoltaic panels in the practical environment. The realization of the algorithm also provides new research methods and ideas for fault detection in the photovoltaic power generation system.

show abstract

“…PV modules are often installed in the harsh outdoor environment and thus tend to suffer several kinds of faults, causing unexpected safety issues, power losses, and even fire hazards 4 – 6 . Actually, not only will the transport of the PV modules but also their storage and unsuitable handling during installation lead to performance reduction or potential faults 7 . Dust is an unavoidable factor that affects solar PV module performance and is considered as one of the major factors that contribute to the formation of hot spots 8 , 9 in a PV module.…”

Section: Introductionmentioning

confidence: 99%

Study on performance degradation and damage modes of thin-film photovoltaic cell subjected to particle impact

Xiao

Song

et al. 2021

Sci Rep

View full text Add to dashboard Cite

It has been a key issue for photovoltaic (PV) cells to survive under mechanical impacts by tiny dust. In this paper, the performance degradation and the damage behavior of PV cells subjected to massive dust impact are investigated using laser-shock driven particle impact experiments and mechanical modeling. The results show that the light-electricity conversion efficiency of the PV cells decreases with increasing the impact velocity and the particles’ number density. It drops from 26.7 to 3.9% with increasing the impact velocity from 40 to 185 m/s and the particles’ number densities from 35 to 150/mm2, showing a reduction up to 85.7% when being compared with the intact ones with the light-electricity conversion efficiency of 27.2%. A damage-induced conversion efficiency degradation (DCED) model is developed and validated by experiments, providing an effective method in predicting the performance degradation of PV cells under various dust impact conditions. Moreover, three damage modes, including damaged conducting grid lines, fractured PV cell surfaces, and the bending effects after impact are observed, and the corresponding strength of each mode is quantified by different mechanical theories.

show abstract

A fault locating method for PV arrays based on improved voltage sensor placement

Cited by 30 publications

References 30 publications

Automated Fault Management System in a Photovoltaic Array: A Reconfiguration-Based Approach

Automated Fault Management System in a Photovoltaic Array: A Reconfiguration-Based Approach

Ghost-RetinaNet: Fast Shadow Detection Method for Photovoltaic Panels Based on Improved RetinaNet

Study on performance degradation and damage modes of thin-film photovoltaic cell subjected to particle impact

Contact Info

Product

Resources

About