Parveen Bhola, a research scholar and Saurabh Bhardwaj, an associate professor at the institute spent the last few years developing and improving statistical and machine learning based alternative to enable real-time inspection of solar panels.
Improving PV cell inspection systems could help inspectors troubleshoot more efficiently and potentially forecast and control for future difficulties.
1) The method allows for off-site inspection
2) The method is fast and furious
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.
3) This method v/s other methods
"The majority of the techniques available calculate the degradation of PV (photovoltaic) systems by physical inspection on site. This process is time-consuming, costly, and cannot be used for the real-time analysis of degradation," Bhola said.
"The proposed model estimates the degradation in terms of performance ratio in real time," he added.
4) The method can improve current solar power forecasting models
Clustering-based computation is likely to shed light on new ways to manage solar energy systems, optimizing PV yields, and inspiring future technological advancements in the field.
Real-time estimation and inspection also allows for real-time rapid response.
"As a result of real-time estimation, the preventative action can be taken instantly if the output is not per the expected value," Bhola said.
"This information is helpful to fine-tune the solar power forecasting models. So, the output power can be forecasted with increased accuracy."