What is it about?
In this work, we present a methodology that uses artificial intelligence to automatically detect faults in solar panel systems. The idea is to identify problems early, before they seriously affect energy production or lead to high repair costs. We use artificial neural networks to analyse system data and recognise patterns that indicate a fault, even when it is not yet visible to the human eye. Our aim is to help solar systems operate more efficiently, safely, and for longer.
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Why is it important?
This research is important because it addresses one of the key challenges in solar energy: ensuring that systems operate at their maximum potential over time. What makes it unique is the use of artificial neural networks to automatically detect faults, allowing issues to be identified early and addressed before they cause significant energy losses or costly damage. By combining advanced data analysis with an automated approach, this work provides a practical, scalable, and proactive tool that can help maintain the reliability and efficiency of photovoltaic systems, ultimately supporting a more sustainable and resilient energy future.
Perspectives
Writing this article was a truly engaging experience because it allowed us to combine two areas I am deeply passionate about renewable energy and artificial intelligence into a practical solution with real-world impact. Working with my co-authors to develop and test an AI based method for detecting faults in solar panels showed me how technology can directly improve efficiency, reduce maintenance costs, and extend the lifespan of clean energy systems. For me, the most rewarding part was seeing how a concept that starts in data and algorithms can translate into more reliable solar power for communities and industries. I hope this work inspires others to explore innovative ways AI can support the global shift towards sustainable energy.
Dr. Ramon Fernando Colmenares Quintero
Fundación Berstic and Universidad Cooperativa de Colombia
Read the Original
This page is a summary of: Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks, Cogent Engineering, January 2021, Taylor & Francis,
DOI: 10.1080/23311916.2021.1981520.
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