Wind Turbine Yaw Control via Consensus Wind Estimates
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Solution Overview
Problem
Wind turbines in a wind farm typically operate individually, controlling their own yaw direction without considering information from nearby turbines, leading to suboptimal performance and increased dynamic yaw misalignment.
Innovation Solution
A system comprising at least one processor that receives wind measurements from a first wind turbine and estimates from nearby second wind turbines, using an augmented Lagrangian method to determine a consensus wind estimate, which is then used to adjust the operating parameters of the first wind turbine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If wind turbines operate individually to maximize their own performance, then each turbine can control its yaw direction independently, but the overall wind farm performance is suboptimal and dynamic yaw misalignment increases
Solution Approach 1:
The patent merges individual turbine measurements with neighbor turbine measurements to create a consensus wind direction estimate. Each turbine combines its own anemometer data with estimates from neighboring turbines using an augmented Lagrangian method, achieving both individual autonomy and collective accuracy. This resolves the contradiction by maintaining independent operation while improving overall reliability through information sharing.
Solution Approach 2:
The system implements feedback by continuously exchanging wind measurement estimates between neighboring turbines and updating the consensus estimate iteratively. Each turbine receives feedback from neighbors about their wind measurements and adjusts its own estimate accordingly, improving yaw alignment accuracy while maintaining individual turbine independence.
2Ease of operation
If wind turbines use local wind measurements only, then each turbine can operate autonomously, but measurement accuracy is reduced due to local turbulence and wake effects
Solution Approach 1:
The patent introduces an intermediary consensus algorithm that mediates between individual turbine measurements. Each turbine's local measurements serve as inputs to the consensus algorithm, which acts as an intermediary to filter out local turbulence and wake effects by comparing measurements across multiple turbines. This maintains autonomous operation while improving measurement precision through the intermediary processing layer.
Solution Approach 2:
The system merges local wind measurements from multiple turbines to create a more accurate consensus estimate. By combining data from multiple sources, the system reduces the impact of local turbulence and wake effects on any single turbine's measurement accuracy, while each turbine maintains autonomous operation based on the improved consensus data.
3Device complexity
If wind turbines operate independently without information sharing, then system complexity is low, but dynamic yaw misalignment increases reducing power output
Solution Approach 1:
The patent segments the wind farm control into independent turbine-level operations that each perform simple consensus calculations with neighbors. Each turbine runs its own augmented Lagrangian optimization locally, exchanging only essential wind measurement data with immediate neighbors. This segmentation maintains low overall system complexity while improving power output through better yaw alignment based on shared information.
Data Source
AI summary
Disclosed herein are methods, systems, and devices for utilizing distributed reinforcement learning and consensus control to most effectively generate and utilize energy. In some embodiments, individual turbines within a wind farm may communicate to reach a consensus as to the desired yaw angle based on the wind conditions.