Tracking PV Boost
Control algorithms based on sky images and a predictive strategy to improve the performance of solar trackers power plants during cloudy episodes.
10 times more efficient than market solutions.
A customized solution that adapts to the mechanical constraints of trackers.
Software embedded in the camera.
Plug-In Play solution.
No camera cleaning is required on-site.
SESA is developing a solution that improves the performance of PV plants equipped with trackers during cloudy sky situations. Standard tracking strategies orient the solar panels towards the sun to maximize the share of direct irradiance received during clear sky days. However, the panels remain oriented towards the sun even during cloudy episodes when the direct irradiance share is almost zero.
SESA’s Tracking PV Boost solution aims to optimize the share of diffuse irradiance captured by the solar field in cloudy sky situations. We have developed a predictive control strategy based short-term solar resource forecasting model and atmospheric information extracted from images captured by our sky imager. To facilitate the integration of our solution, we integrate the mechanical constraints of the tracking systems on a case-by-case basis to respect a specific number of cycles per day.
AI models applied to image analysis
Intelligent control strategies optimized to our customer’s needs
A high-resolution camera that embeds the optimization strategy
NOWCASTING
Short-term solar resource solution
Forecasting of solar resource ramps.
3 to 10 min time horizons.
Software embedded in the camera.
Sky imager that generates high quality HDR images.
SESA’s solution uses mixed instrumentation (a wide-angle camera and a pyranometer) taking advantage of GHI (Global Horizontal Irradiance) measurements and atmospheric observations. The forecasting algorithms are based on state-of-the-art artificial intelligence tools that are not yet widely used in the field of weather forecasting. They take advantage of information extracted from images to predict future irradiance.
Anticipation of more than 50% of solar resource ramps
Hybrid model based on knowledge models and AI tools.
Integrated soil removal software to reduce cleaning interventions.
SESA 