Cloud-Based Turbine Yaw Control for Wake Calculation Bottlenecks
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Solution Overview
Problem
Conventional wind farms face inefficiencies due to complex calculations required for managing wake turbulence, which are difficult to execute in real-time at individual turbines or local networks, leading to suboptimal performance and energy production.
Innovation Solution
A system that centralizes complex calculations in a processing center to generate optimized yaw settings for wind turbines, using real-time data streaming and look-up tables, allowing for efficient power production and reduced operating costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex wake turbulence calculations are performed at individual turbines or local networks, then real-time control precision is improved, but device complexity and computing power requirements increase significantly
Solution Approach 1:
The patent extracts the complex wake turbulence calculations from individual turbines and local networks, relocating them to a centralized remote location. This separates the heavy computational burden from the edge devices, allowing precise yaw control without overloading turbine computers.
Solution Approach 2:
The system introduces a centralized processing center as an intermediary between wind turbines and the control algorithm. This intermediary handles all complex calculations centrally, receiving data from turbines and returning optimized yaw settings, thus avoiding the need for complex computing at each turbine.
2Measurement precision
If complex wake turbulence calculations are performed locally, then control accuracy is improved, but productivity and real-time response capability deteriorate due to insufficient computing power
Solution Approach 1:
The patent removes complex calculations from the local turbine environment and extracts them to a centralized remote location with sufficient computing resources. This enables real-time processing of wake turbulence data without being constrained by limited turbine computing power.
Solution Approach 2:
The system replaces the mechanical constraint of limited onboard computing power with a communication-based solution. Instead of relying on local processing capability, the system uses data transmission to a centralized facility that performs calculations and returns results, effectively substituting computational limitation with network communication.
3Measurement precision
If more computing power is installed at turbines, then calculation accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts the computational burden from individual turbines entirely, relocating all wake turbulence calculations to a centralized remote location. This eliminates the need to upgrade turbine computing hardware while maintaining high calculation accuracy through access to powerful centralized resources.
4Device complexity
If conventional control systems are used at each turbine, then system simplicity is maintained, but wind farm overall performance and energy production are suboptimal
Solution Approach 1:
The patent merges the control functions of multiple individual turbines into a single centralized control system. By combining wake turbulence calculations and yaw optimization for the entire wind farm in one location, the system achieves superior overall performance while individual turbines maintain simple control hardware.
Data Source
AI summary
A method and apparatus for applying optimized yaw settings to wind turbines including receiving operating data from at least one wind turbine on a wind farm and sending the data to a supervisory control and data acquisition (SCADA) system on the at least one wind turbine to generate current SCADA data. The current SCADA data is sent a central processing center away from the wind farm. The central processing center includes an optimization system that can generate a new look up table (LUT) including at least one new wind turbine yaw setting calculated using information comprising wind direction, wind velocity, wind turbine location in the wind farm, information from a historic SCADA database, and yaw optimizing algorithms. The new LUT is then sent to a yaw setting selection engine (YSSE) where instructions regarding the use of the new LUT are generated.