Jan 11 2019
Although solar panels are comparatively popular and beneficial as a source of renewable energy, even the best ones could fail eventually.
In due course, solar cells confront damage caused by UV exposure, soiling, temperature changes, and weather. Moreover, in order to maintain cell performance levels and minimize economic losses, solar cells need inspections.
So, how can panels be inspected in real time such that it is time-efficient as well as cost-effective? In the past few years, 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 time developing and enhancing statistical and machine learning-based alternatives to allow real-time inspection of solar panels. With their study, they have come up with an innovative application for clustering-based computation, which involves using past meteorological data to calculate the performance ratios and degradation rates. Off-site inspection is also enabled by this approach.
This issue can be overcome by using clustering-based computation as it can accelerate the inspection process, avoiding more damage and hastening repairs, by employing a performance ratio based on meteorological parameters that include pressure, temperature, humidity, wind speed, solar power, sunshine hours, and even the day of the year. The parameters can be easily obtained and evaluated and can be measured from remote areas.
Inspectors would be able to fix issues more efficiently and potentially predict and control for future difficulties by enhancing PV cell inspection systems. Clustering-based computation probably casts light on innovative means to deal with solar energy systems, optimizing PV yields, and motivating future technological advancements in the field.
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. The proposed model estimates the degradation in terms of performance ratio in real time.
Parveen Bhola, Research Scholar, Thapar Institute of Engineering and Technology
Bhola earlier worked with Bhardwaj and created the model to assess solar radiation with the help of a combination of the Generalized Fuzzy Model and the Hidden Markov Model.
The Hidden Markov Model is used to model randomly varying systems with hidden or unobserved states; the Generalized Fuzzy Model tries to use imprecise information in its modeling process. These models include the processes of recognition, classification, clustering, and information retrieval, and are helpful for adapting PV system inspection methods.
The advantages of real-time PV inspection exceed that of cost-efficient and time-sensitive measures. Existing solar power forecasting models can also be improved by this newly proposed approach. Bhola suggested that it is possible to predict the output power of a solar panel, or a set of solar panels, with even higher accuracy. In addition, real-time estimation and inspection facilitate 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. This information is helpful to fine-tune the solar power forecasting models. So, the output power can be forecasted with increased accuracy.
Parveen Bhola, Research Scholar, Thapar Institute of Engineering and Technology.
Source: https://www.aip.org/