A novel approach to optimize the positioning and measurement parameters in photovoltaic aerial inspections

Segovia, Isaac; Pliego, Alberto Barbabosa; Márquez, Fausto Pedro García

doi:10.1016/j.renene.2022.01.071

Cited by 16 publications

(3 citation statements)

References 43 publications

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“…Due to its great mobility and the fact that it is not directly connected to wires and other devices that limit its reach, this equipment is a great ally in the performance of several activities linked to the most diverse areas. According to the systematic literature review performed, the areas that most use this type of technology can be observed: a) In the scope of photovoltaic energy generation, the association of the use of Unmanned Aerial Vehicles -UAVs together with management and inspection techniques by infrared thermography has shown promise since proper maintenance increases efficiency and energy production and is an effective way to avoid, repair, or mitigate the effects of possible failures and degradation mechanisms (Ramírez et al, 2022).…”

Section: Cmentioning

confidence: 99%

Method for inspection of atmospheric discharge protection systems: Grain storage plants

Kunh,

Christ,

Coelho

et al. 2024

Rev. bras. eng. agríc. ambient.

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Brazil has the highest incidence of atmospheric discharges, on average of 77.8 million hitting the country yearly. Companies providing post-harvest services have a high monetary value concentrated in their predominantly metallic structures. Atmospheric discharge protection systems (ADPS) are used in this context to protect the facilities and people, composed of several elements that require visual inspection with a certain periodicity, an activity framed as working at height. This research aimed to establish a methodology for employing a Remotely Piloted Aircraft System (RPAS) in the visual analysis of the operational condition of external atmospheric discharge protection systems components located in grain storage structures to collect information during the preliminary risk analysis and planning of activities at height. A survey of the current literature related to the subject was carried out, in addition to the norms, inspection agencies, and operational prerequisites of this type of equipment in the national territory. Finally, a method was created and validated based on the inspection checklist with the main points for checking, which generates a score that represents the urgency level of intervention, applied in a unit located in western Paraná, Brazil, on 11/28/2022. This procedure eliminated the risk of workers falling during the planning and inspection stage of the structures, which helped the qualified professional to issue the technical inspection report more quickly. The disadvantage is related to the dependence on favorable weather conditions.

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Section: Cmentioning

confidence: 99%

Method for inspection of atmospheric discharge protection systems: Grain storage plants

Kunh,

Christ,

Coelho

et al. 2024

Rev. bras. eng. agríc. ambient.

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“…Fig. 3 A typical work flow of solar power forecasting using ML [162] Machine learning is very necessary in order to maximize the efficiency of the positioning and orientation of solar panels [163], [164]. By analyzing the patterns of sunshine, the topography, and other geographical aspects, machine learning algorithms are able to provide recommendations for the optimal locations and angles for the deployment of solar panels in order to maximize energy capture [165], [166].…”

Section: A Ai and ML Techniques In Renewable Energy Forecasting 1) So...mentioning

confidence: 99%

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Nguyen,

Paramasivam,

Dong

et al. 2024

JOIV : Int. J. Inform. Visualization

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Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, and solar power, can revolutionize the energy industry. Biomass and biofuels have benefited significantly from implementing AI and ML algorithms that optimize feedstock, enhance resource management, and facilitate biofuel production. By applying insight derived from data analysis, stakeholders can improve the entire biofuel supply chain - including biomass conversion, fuel synthesis, agricultural growth, and harvesting - to mitigate environmental impacts and accelerate the transition to a low-carbon economy. Furthermore, implementing AI and ML in combustion systems and engines has yielded substantial improvements in fuel efficiency, emissions reduction, and overall performance. Enhancing engine design and control techniques with ML algorithms produces cleaner, more efficient engines with minimal environmental impact. This contributes to the sustainability of power generation and transportation. ML algorithms are employed in solar energy to analyze vast quantities of solar data to improve photovoltaic systems' design, operation, and maintenance. The ultimate goal is to increase energy output and system efficiency. Collaboration among academia, industry, and policymakers is imperative to expedite the transition to a sustainable energy future and harness the potential of AI and ML in renewable energy. By implementing these technologies, it is possible to establish a more sustainable energy ecosystem, which would benefit future generations.

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“…The analysis of images acquired by aerial inspections with thermal cameras is a challenge in the current state of the art, being necessary new reliable techniques for object detection and region identification that allow the detection of hot spots, delamination or faults with visual patterns. Machine Learning (ML) algorithms have emerged as reliable solutions for fault detection in images to detect Regions of Interest (ROIs) [22]. Artificial Neural Networks (ANNs) are novel processing systems based on biological nervous systems applied for classification and feature extraction, being capable of learning with complex nonlinear input-output connections through training processes.…”

Section: Introductionmentioning

confidence: 99%

Internet of Things Platform for Photovoltaic Maintenance Management: Combination of Supervisory Control and Data Acquisition System and Aerial Thermal Images

Ramírez

Márquez

2023

E3S Web Conf.

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Suitable maintenance management plants of solar photovoltaic plants are required for global energy demands. The volume and variety of data acquired by thermographic cameras carried by unmanned aerial vehicles and Supervisory Control and Data Acquisition Systems increase the complexity of fault detection and diagnosis. The maintenance industry is requiring novel fault detection techniques that can be implemented in Internet of Thing platforms to automate the analysis and increase the suitability and reliability of the results. This paper presents a novel platform built with PHP, HTML, CSS and JavaScript for the combined analysis of data from Supervisory Control and Data Acquisition Systems and thermal images. The platform is designed. A real case study with thermal images and time series data from the same photovoltaic plant is presented to test the viability of the platform. The analysis of thermal images showed a 97% of accuracy for panel detection and 87% for hot spot detection. Shapelets algorithm is selected for time series analysis, providing an 84% of accuracy for the pattern selected by user. The platform has proven to be a flexible tool that can be applied for different solar plants through data upload by users.

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A novel approach to optimize the positioning and measurement parameters in photovoltaic aerial inspections

Cited by 16 publications

References 43 publications

Method for inspection of atmospheric discharge protection systems: Grain storage plants

Method for inspection of atmospheric discharge protection systems: Grain storage plants

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Internet of Things Platform for Photovoltaic Maintenance Management: Combination of Supervisory Control and Data Acquisition System and Aerial Thermal Images

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