View source for Inventus Holdings, LLC (20240337249). WIND TURBINE CONTROL SYSTEM INCLUDING AN ARTIFICAL INTELLIGENCE ENSEMBLE ENGINE simplified abstract

=WIND TURBINE CONTROL SYSTEM INCLUDING AN ARTIFICAL INTELLIGENCE ENSEMBLE ENGINE=


==Organization Name==

[[:Category:Inventus Holdings, LLC|Inventus Holdings, LLC]]


[[Category:Inventus Holdings, LLC]]

==Inventor(s)==

[[:Category:Andres F. Girardot of Jupiter FL (US)|Andres F. Girardot of Jupiter FL (US)]][[Category:Andres F. Girardot of Jupiter FL (US)]]

[[:Category:George Alexander Pantouris of Jupiter FL (US)|George Alexander Pantouris of Jupiter FL (US)]][[Category:George Alexander Pantouris of Jupiter FL (US)]]

[[:Category:Yi Li of Jupiter FL (US)|Yi Li of Jupiter FL (US)]][[Category:Yi Li of Jupiter FL (US)]]

==WIND TURBINE CONTROL SYSTEM INCLUDING AN ARTIFICAL INTELLIGENCE ENSEMBLE ENGINE - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240337249 titled 'WIND TURBINE CONTROL SYSTEM INCLUDING AN ARTIFICAL INTELLIGENCE ENSEMBLE ENGINE

The abstract describes a system for generating power using wind turbines, where an environmental engine determines performance metrics for each turbine at different wind farms. An artificial intelligence ensemble engine generates models for each turbine using different machine learning algorithms to optimize efficiency.

* The system analyzes performance metrics for wind turbines at various wind farms.
* An artificial intelligence ensemble engine generates models for each turbine using different machine learning algorithms.
* The AI engine selects the most efficient model for each turbine and provides recommended operating parameters.
* Edge computing systems at the wind farms receive the selected model and operating parameters for improved performance.

Potential Applications:
- Renewable energy generation
- Optimization of wind turbine efficiency
- Environmental monitoring and analysis

Problems Solved:
- Maximizing power generation from wind turbines
- Enhancing overall performance of wind farms
- Improving operational efficiency and sustainability

Benefits:
- Increased energy output from wind turbines
- Cost savings through optimized operations
- Reduced environmental impact of power generation

Commercial Applications:
Title: "AI-Optimized Wind Turbine Power Generation System"
This technology can be used in commercial wind farms to enhance power generation efficiency, reduce operational costs, and improve overall sustainability. It can also be integrated into smart grid systems for more efficient energy distribution.

Prior Art:
Researchers can explore prior patents related to AI optimization of renewable energy systems, machine learning algorithms for wind turbine performance, and edge computing applications in power generation.

Frequently Updated Research:
Researchers in the field of renewable energy and artificial intelligence are constantly developing new algorithms and technologies to improve the efficiency of wind turbine power generation systems. Stay updated on the latest advancements in AI optimization for renewable energy sources. 

Questions about AI-Optimized Wind Turbine Power Generation System:

1. How does the system determine the most efficient model for each wind turbine?
The system uses an artificial intelligence ensemble engine to generate models for each turbine using different machine learning algorithms, selecting the model with the highest efficiency metric.

2. What are the potential cost savings associated with implementing this technology in wind farms?
By optimizing the performance of wind turbines, the technology can lead to significant cost savings through increased energy output and improved operational efficiency.


==Original Abstract Submitted==

a system for generating power includes an environmental engine that determines performance metrics for a plurality of wind turbines deployed at a plurality of windfarms, such that each windfarm includes a corresponding subset of the plurality of windfarms. the performance metrics for a given wind turbine of the plurality of wind turbines characterizes wind flowing over blades of the given wind turbine. the system includes an artificial intelligence (ai) ensemble engine operating on the one or more computing devices that generates a set of models for each wind turbine of the plurality of wind turbines, wherein each model of each set of models is generated with a different machine learning algorithm and selects, for each respective set of models, a model with a highest efficiency metric. the ai engine provides edge computing systems operating at the plurality of windfarms with a selected model and corresponding recommended operating parameters.



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