The algorithm is said to be able to look into the relationship between weather forecast data and projection of electric circuit parameters. Through this innovation, Purdue University researchers claim they can interpret the routinely collected maximum power point (MPP) time-series data to assess the time-dependent “health” of installed solar modules.
A research team from Purdue University’s School of Electrical and Computer Engineering has developed a new physics model to measure the degradation of solar panels.
The model, called Suns-Vmp Method, is based on an algorithm that is still at an experimental stage, but is said to be able to combine the analysis of weather forecast data with routinely collected time series maximum power point (MPP) data of a solar power system.
According to the scientists, their algorithm, which requires environment data, cell temperature and irradiance, is able to recreate IV curves by utilizing the natural illumination‐dependent and temperature‐dependent daily MPP characteristics as constraints to fit physics‐based circuit models. This enables the determination of the time‐dependent evolution of circuit parameters, while also providing an insight on the dominant degradation modes of solar modules such as solder bond failures.
The algorithm, defined as continuous self-filtering, has already been tested at a facility of the US Department of Energy’s National Renewable Energy Laboratory. “Our analysis indicates that the solar modules degraded at a rate of 0.7%/year because of discoloration and weakened solder bonds,” the research group noted.
Furthermore, the algorithm is able to diagnose the pathology of solar modules with non-uniform degradation such as non-uniform delamination- and PID-induced degradation, although it can not yet correctly extract the degraded circuit parameters under severe performance variability.
The researchers now are aiming at improving the algorithm by also using other physics-based models. “We hope the algorithm could show how much energy a solar farm produces in 30 years by looking at the relationship between weather forecast data and projection of electric circuit parameters,” they stated.
Source PV Magazine