Wind Farm Yaw Control Using Cloud Wake Optimization
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Solution Overview
Problem
Conventional wind farms face inefficiencies due to wake turbulence caused by wind deflecting off turbines, leading to poor performance downstream, and existing complex calculations require significant computing power, making them unsuitable for real-time implementation in wind farms.
Innovation Solution
A system that processes data from wind turbines in a central processing center to generate optimized yaw settings, using a SCADA system, edge computing, and yaw setting selection engine to update turbine positions, optimizing energy production by correcting wake turbulence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex wake turbulence calculations are performed using well-known wind theories and algorithms, then wake turbulence can be corrected and controlled, but significant computing power is required which is not available at individual turbines or local wind farm networks
Solution Approach 1:
A cloud-based computing platform serves as an intermediary between wind turbines and local control systems. The platform receives raw meteorological data from turbines, performs complex wake turbulence calculations using wind theories and algorithms, and returns optimized yaw angle recommendations. This mediator approach enables sophisticated calculations without requiring powerful computing hardware at turbine sites.
Solution Approach 2:
The solution moves the computational workload from the physical dimension of individual turbine hardware to a different dimension - a centralized cloud computing environment. This dimensional shift allows complex calculations to be performed remotely where computing resources are abundant, while maintaining real-time control capabilities at the turbine level.
2Productivity
If complex wake turbulence calculations are performed in real-time, then downstream turbines can be optimized, but the computational complexity and data processing requirements are too high for local systems
Solution Approach 1:
The system segments the control architecture into distinct functional layers: (1) data collection at turbine level, (2) complex calculation and optimization at cloud platform level, and (3) simple execution of yaw angle adjustments at turbine level. This segmentation allows each layer to perform its specialized function efficiently without overwhelming local systems.
Solution Approach 2:
The cloud-based platform acts as an intermediary that handles all complex computational tasks, receiving meteorological data from turbines and returning simplified yaw angle recommendations. This intermediary approach resolves the contradiction by centralizing computational complexity while maintaining distributed control simplicity.
3Ease of operation
If conventional SCADA systems are used at individual turbines, then basic wind direction and speed monitoring is available, but the systems lack the capability to perform complex wake turbulence calculations and optimizations
Solution Approach 1:
The cloud-based platform provides universal optimization services to multiple wind turbines and entire wind farms simultaneously. A single platform can serve numerous turbines, performing complex wake turbulence calculations for each while maintaining basic SCADA functionality. This multi-functional approach enhances adaptability without requiring each turbine to have sophisticated local optimization capabilities.
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.