Measuring Solar Panel Degradation with Machine Learning

The duo has spent the last few years developing and improving statistical and machine learning-based alternatives to enable real-time inspection of solar panels.

Experimental Setup at NISE, Gurugram

Parveen Bhola, a research scholar at India’s Thapar Institute of Engineering and Technology, and Saurabh Bhardwaj, an associate professor at the same institution, have developed an innovative technique to inspect solar panels in real time, in a way that is both cost-effective and time-efficient. The duo has spent the last few years developing and improving statistical and machine learning-based alternatives to enable real-time inspection of solar panels.

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.

Their research which was published in the Journal of Renewable and Sustainable Energy has 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.

“As a result of real-time estimation, the preventative action can be taken instantly if the output is not per the expected value,” Parveen said. “This information is helpful to fine-tune the solar power forecasting models. So, the output power can be forecasted with increased accuracy.”

Improving PV cell inspection systems could help inspectors troubleshoot more efficiently and potentially forecast and control for future difficulties. Clustering-based computation is likely to shed light on new ways to manage solar energy systems, optimising PV yields, and inspiring future technological advancements in the field.

Source: Journal of Renewable and Sustainable Energy