We are a strategic partner for companies seeking to maximize energy efficiency and the competitiveness of photovoltaic systems.
We develop innovative solutions that integrate artificial intelligence and data science, leveraging our extensive experience in the solar sector in Chile and Latin America.
Our goal is to reduce maintenance costs and increase the operational efficiency of solar systems.
Electroluminescence is an optical and non-invasive technique that allows the evaluation of the brightness of solar panel cells. This technique makes it possible to detect structural defects, which may be caused by improper handling during transportation, inadequate cleaning, or natural or accelerated degradation, among other factors.
These defects often appear as cracks and negatively affect the efficiency of the photovoltaic system.
Alongside IV curve tracing and low-uncertainty field degradation measurements, electroluminescence stands out as a key mechanism to determine the current and future status of a solar field, enabling the detection and prediction of future failures.
This process involves a technical visit to the plant, followed by an analysis of the solar field’s condition and statistical evaluation of the failures detected.
This technology has been successfully developed and tested under outdoor conditions at plants such as Engie and AES-Gener, as well as in Argentina, where string-level assessments were carried out without the need to remove modules from their structures or losing energy production, since the measurements were conducted at night.
Industry 4.0 marks a turning point in the evolution of industrial production, shifting towards smart and connected ecosystems.
In these advanced environments, systems not only interact with one another but also leverage Information and Communication Technologies (ICT) for streamlined management and operation.
Applying Industry 4.0 principles to the solar energy sector promises to revolutionize operational efficiency and service quality, while simultaneously reducing costs and strengthening competitiveness.
Our research focuses on the development and application of advanced algorithms—including statistical models, neural networks, support vector machines (SVM), clustering techniques, and other machine learning methods—to optimize both photovoltaic and solar thermal energy systems and their related services.
The solar energy industry generates vast volumes of sensor data every day. When properly processed, this data offers a wealth of invaluable insights.
At Fraunhofer, we help companies maximize the value of this data. Through rigorous and in-depth analysis, we provide critical intelligence that empowers informed strategic decision-making.
Furthermore, we facilitate asset failure identification and performance loss detection, directly contributing to increased energy efficiency across systems.
