Review of O&amp;M Practices in PV Plants: Failures, Solutions, Remote Control, and Monitoring Tools

Lorenzo, Gianfranco Di; Araneo, Rodolfo; Mitolo, Massimo; Niccolai, Alessandro; Grimaccia, Francesco

doi:10.1109/jphotov.2020.2994531

Cited by 45 publications

(22 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1) Gramian Angular Difference Field (GADF) [32]: It calculates the inner product of the input vector via the characterization of angular difference and preserves the temporal dependency. The size of the generated matrix (Dmatrix) =Dvector 2 , where Dvector is the number of features of the original vector. In this sense, when Dvector =50, each current vector is transformed to one matrix with Dmatrix = 2500.…”

Section: Feature Transformationmentioning

confidence: 99%

“…The phase space is generated by embedding a shift (τ) in the input vector, where τ is determined by optimization. The Dmatrix of RP equals (Dvector− τ) 2 . In this study, τ=1, thus, Dmatrix=2401.…”

Section: Feature Transformationmentioning

confidence: 99%

“…The solar photovoltaic (PV) installed capacity has experienced rapid growth among all the main energy types in recent years [1]. However, due to the environmental threats and the potential damages in the manufacturing, transportation, installation, or other procedures, various PV faults may arise up [2,3]. These faults could not only affect the power generation but also could cause severe safety hazards [4], e.g., fire risk, electrical shock, physical danger.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fault diagnosis of photovoltaic panels using full I–V characteristics and machine learning techniques

Delpha

Migan‐Dubois

et al. 2021

Energy Conversion and Management

View full text Add to dashboard Cite

Section: Feature Transformationmentioning

confidence: 99%

Section: Feature Transformationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fault diagnosis of photovoltaic panels using full I–V characteristics and machine learning techniques

Delpha

Migan‐Dubois

et al. 2021

Energy Conversion and Management

View full text Add to dashboard Cite

“…In general, failures on PV systems are more concentrated in the inverters and PV modules. In the PV modules, because of dirty equipment, environmental conditions, or manufacturing problems the PV plant energy output can be reduced by 31% [3][4][5]. To detect these problems, it is necessary to consider that the PV systems are commonly located on roofs, rooftops, and farms.…”

Section: Introductionmentioning

confidence: 99%

Automatic Boundary Extraction for Photovoltaic Plants Using the Deep Learning U-Net Model

2021

View full text Add to dashboard Cite

Nowadays, the world is in a transition towards renewable energy solar being one of the most promising sources used today. However, Solar Photovoltaic (PV) systems present great challenges for their proper performance such as dirt and environmental conditions that may reduce the output energy of the PV plants. For this reason, inspection and periodic maintenance are essential to extend useful life. The use of unmanned aerial vehicles (UAV) for inspection and maintenance of PV plants favor a timely diagnosis. UAV path planning algorithm over a PV facility is required to better perform this task. Therefore, it is necessary to explore how to extract the boundary of PV facilities with some techniques. This research work focuses on an automatic boundary extraction method of PV plants from imagery using a deep neural network model with a U-net structure. The results obtained were evaluated by comparing them with other reported works. Additionally, to achieve the boundary extraction processes, the standard metrics Intersection over Union (IoU) and the Dice Coefficient (DC) were considered to make a better conclusion among all methods. The experimental results evaluated on the Amir dataset show that the proposed approach can significantly improve the boundary and segmentation performance in the test stage up to 90.42% and 91.42% as calculated by IoU and DC metrics, respectively. Furthermore, the training period was faster. Consequently, it is envisaged that the proposed U-Net model will be an advantage in remote sensing image segmentation.

show abstract

“…The most common threats are the failures in the inverters and PV modules [8]. Through dirty equipment, the state of the environment, or manufacturing defects in the PV modules, PV plant energy generation can be curtailed by 31% in the worst cases [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Coverage Path Planning with Semantic Segmentation for UAV in PV Plants

et al. 2021

View full text Add to dashboard Cite

Solar energy is one of the most strategic energy sources for the world’s economic development. This has caused the number of solar photovoltaic plants to increase around the world; consequently, they are installed in places where their access and manual inspection are arduous and risky tasks. Recently, the inspection of photovoltaic plants has been conducted with the use of unmanned aerial vehicles (UAV). Although the inspection with UAVs can be completed with a drone operator, where the UAV flight path is purely manual or utilizes a previously generated flight path through a ground control station (GCS). However, the path generated in the GCS has many restrictions that the operator must supply. Due to these restrictions, we present a novel way to develop a flight path automatically with coverage path planning (CPP) methods. Using a DL server to segment the region of interest (RoI) within each of the predefined PV plant images, three CPP methods were also considered and their performances were assessed with metrics. The UAV energy consumption performance in each of the CPP methods was assessed using two different UAVs and standard metrics. Six experiments were performed by varying the CPP width, and the consumption metrics were recorded in each experiment. According to the results, the most effective and efficient methods are the exact cellular decomposition boustrophedon and grid-based wavefront coverage, depending on the CPP width and the area of the PV plant. Finally, a relationship was established between the size of the photovoltaic plant area and the best UAV to perform the inspection with the appropriate CPP width. This could be an important result for low-cost inspection with UAVs, without high-resolution cameras on the UAV board, and in small plants.

show abstract

Review of O&M Practices in PV Plants: Failures, Solutions, Remote Control, and Monitoring Tools

Cited by 45 publications

References 49 publications

Fault diagnosis of photovoltaic panels using full I–V characteristics and machine learning techniques

Fault diagnosis of photovoltaic panels using full I–V characteristics and machine learning techniques

Automatic Boundary Extraction for Photovoltaic Plants Using the Deep Learning U-Net Model

Coverage Path Planning with Semantic Segmentation for UAV in PV Plants

Contact Info

Product

Resources

About