Inventus Holdings, LLC

Hector K Lopez

Marc Benedict C Tinio

Jeroen Leverman

Jay K Limbasiya

Janine Bacquie

Michael T Rothenberg

Brian F Heredia

Jason R Skopek

Maikel Martin

In some examples, sensor data comprising a first set and second set of sensor data can be received. The first set of sensor data can be provided to a rule-based classifier to identify a first normalized data tag. The second set of sensor data can be provided to a trained classifier to identify a second normalized data tag. The trained classifier can include a machine-learning model that has been trained based on tag sensor training data for classifying the second set of sensor data into a respective class associated with the second normalized data tag. The first and second sets of sensor data can be updated with respective first and second normalized data tags to normalize the first and second sets of sensor data to provide a standardized data label for each of the first and second sets of sensor data.

Systems and methods for classifying sensor data

A system and a method for regulating charging and discharging of an energy storage device as part of an electrical power distribution network is described. The invention is a smart control algorithm for a bi-directional switch in which an energy storage device, such as a battery set, is charged when electricity prices are low and discharged when electricity prices are high. The invention uses two different types of pricing data: forecasted price data and real-time price data. The forecasted price data is used to set a threshold. When the real-time price data of electricity exceeds this threshold, the energy storage device is set to discharge and send power to the grid. Otherwise the energy storage device is set to charge. The threshold is set periodically, typically in 30 minute to several hour intervals to capture the latest data.

Regulating charging and discharging of an energy storage device as part of an electrical power distribution network

Various embodiments distributively control a wind park having a plurality of wind turbines. Each wind turbine controlled by its own turbine controller. Each turbine controller determines power production capability of its dedicated wind turbine. The power production capability of the single wind turbine is communicated with a plurality of other turbine controllers in the wind park. A consensus power distribution schedule to distribute power to a power grid is determined using votes from each turbine controller in the wind park. Energy is discharged from each wind turbine to a power grid according to the power distribution schedule. When one wind turbine fails, a new power distribution schedule is determined using votes from each remaining turbine controller and energy is discharged from each wind turbine to the power grid according to the new power distribution schedule.

Distributed wind park control

One example includes a test illumination system comprising a light source and a location controller configured to generate location data associated with a real-time location of the test illumination system. The test illumination system can be configured to move relative to a solar panel array in response to a solar testing protocol to provide illumination from the light source at a defined illumination time to each solar panel of the solar panel array in a sequence to provide a voltage amplitude. The system also includes a solar panel testing controller configured to correlate the location data of the test illumination system, the defined illumination time, and the voltage amplitude for each solar panel of the solar panel array in the sequence to determine an efficacy of each solar panel of the solar panel array.

Solar panel array performance testing system with defined time and location illumination

Groups of wind turbines at a wind farm are controlled based on detection of volant animal swarms. A risk score is defined for each of a plurality of wind turbine groups of the wind farm at a default risk score based on predetermined data. A respective operational parameter is set for each wind turbine of each of the wind turbine groups based on the respective risk score. The presence of a volant animal swarm is detected in a region associated with a given one of the wind turbine groups via a volant animal detection system. The risk score of the given one wind turbine group and an adjacent wind turbine group is changed from the default risk score to temporary risk scores in response to detection of the volant animal swarm.

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Jupiter, FL, United States of America

Hector K Lopez