Inventus Holdings, LLC

Florida Power & Light Company

Donville D Smith

Hooman Yousefizadeh

Yi Li

Carmine A Priore, Iii

Daniel M Brake

Gary D Moncrief, Jr

Eduardo J More

Andres F Girardot

LaMargo V Sweezer-Fischer

Marcos Suero

Jennifer D Chaney

Boris A Zarate

Sentibaleng M Ncube

John F Granger

A system for generating power includes an environmental engine operating on one or more computing devices that determines a wind flowing over a blade of a wind turbine, wherein the wind flowing over the blade of the wind turbine varies based on environmental conditions and operating parameters of the wind turbine. The system also includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a plurality of different models for the wind turbine. Each model characterizes a relationship between at least two of a rotor speed, a blade pitch, the wind flowing over the blade, a wind speed and a turbulence intensity for the wind turbine. The AI ensemble engine selects a model with a highest efficiency metric, and simulates execution of the selected model to determine recommended operating parameters.

Wind turbine control system including an artificial intelligence ensemble engine

Systems and methods for monitoring an operational system. An initial set of sensor data is accumulated from a system over a substantially shorter time than is required to collect data to characterize a regression model for an operating parameter of the system. An initial regression model is created based on the initial set of sensor data. A subsequent set of sensor data is received from the at least one sensor after creating the initial regression model. An expected dependent value for the subsequent independent value is determined using the initial regression model. An operator is prompted to update the initial regression model based on a difference between a subsequent dependent value and the expected dependent value. The initial regression model is updated to incorporate the subsequent set of sensor data. A notification is provided based on a difference between presently received sensor data and the updated regression model.

Adaptive alarm and dispatch system using incremental regressive model development

A system for generating power includes an environmental engine operating on one or more computing devices that determines a Reynolds number for a wind turbine, wherein the Reynolds number characterizes wind flowing over a blade of the wind turbine that varies based on the wind speed, a rotor speed and characteristics of the blade of the wind turbine. The system also includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a plurality of different models for the wind turbine. Each model characterizes a relationship between the rotor speed and a blade pitch for the wind turbine, the Reynolds number, wind speed and turbulence intensity for the wind turbine. The AI ensemble engine selects a model with a highest efficiency metric; and simulates execution of the selected model to determine recommended operating parameters.

A defect detector can receive measured data characterizing an operational performance of a population of sets of parallel operating power sources of a power supplier for a power distribution system. The defect detector can employ statistical analysis to detect a potential defect in a subset of the parallel operating power sources of the power supplier based on the measured data. Each of the parallel operating power sources can be configured to provide a given output in response to a given input.

Defect detection in power distribution system

A system and method of monitoring equipment performance and predicting failures. The system can include a data historian that stores data for a piece of equipment and designates the data to tags. The tags can correspond to sensors that gather the data from the piece of equipment. A matrix model builder application can allow a user to generate regression models for various time spans to determine whether new data is within a normal operating range.

Data analysis applications

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Hobe Sound, FL, United States of America

Donville D Smith