Despite many benefits and relative popularity as a renewable energy source, eventually, the sun does set on even the best solar panels. Over time, solar cells face damage from weather, temperature changes, soiling, and UV exposure. Solar cells also require inspections to maintain cell performance levels and reduce economic losses.
So, how does one inspect panels in real time, in a way that is both cost-effective and time-efficient? Parveen Bhola, a research scholar at India’s Thapar Institute of Engineering and Technology, and Saurabh Bhardwaj, an associate professor at the same institution, spent the last few years developing and improving statistical and machine learning-based alternatives to enable real-time inspection of solar panels. Their research found a new application for clustering-based computation, which uses past meteorological data to compute performance ratios and degradation rates. This method also allows for off-site inspection.
Clustering-based computation is advantageous for this problem because of its ability to speed up the inspection process, preventing further damage and hastening repairs, by using a performance ratio based on meteorological parameters that include temperature, pressure, wind speed, humidity, sunshine hours, solar power, and even the day of the year. The parameters are easily acquired and assessed, and can be measured from remote locations. [click for full article]