Free-Flow Wind Speed Determination Using Ensemble Kalman Filter
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current wind turbine control and monitoring techniques fail to accurately determine free-flow wind speed in the rotor plane, relying on expensive LiDAR sensors and complex data processing, and do not account for uncertainties in wind field characteristics like turbulence and induction factors.
Innovation Solution
A method using SCADA measurements and an ensemble Kalman filter to robustly determine free-flow wind speed in real-time, incorporating wind speed, turbulence intensity, and wind direction measurements, with a wind farm model that connects these parameters to optimize wind farm control and diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR sensors are used to measure wind speed, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses SCADA measurements (which are already available from the wind turbine's control system) as a substitute for direct LiDAR measurements. The ensemble Kalman filter creates a virtual model of the wind field that copies the essential characteristics without requiring expensive physical LiDAR sensors, thereby achieving acceptable measurement precision while avoiding the complexity and cost of LiDAR hardware
Solution Approach 2:
The patent replaces the mechanical/optical LiDAR measurement system with a computational approach using SCADA data and an ensemble Kalman filter. This substitution uses software-based wind field reconstruction instead of physical sensors, eliminating the need for complex LiDAR hardware while still providing wind speed estimates for control purposes
2Measurement precision
If LiDAR sensors and complex data processing are used, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The ensemble Kalman filter system is self-service in that it automatically processes existing SCADA measurements to determine free-flow wind speed without requiring manual intervention or complex external processing. The filter continuously updates the wind field model using readily available turbine data, making the system easy to operate while maintaining high precision
Solution Approach 2:
The patent leverages the existing SCADA system, which is already in place for wind turbine control, to also perform wind field reconstruction and free-flow wind speed determination. This multi-functional use of the SCADA system eliminates the need for separate complex processing systems, thereby improving ease of operation while maintaining measurement precision
3Productivity
If conventional control techniques are used, then device complexity is reduced, but productivity deteriorates
Solution Approach 1:
The ensemble Kalman filter provides continuous feedback about the reconstructed wind field to the wind turbine control system. This feedback enables real-time adjustments to blade pitch and rotor speed based on predicted wind conditions, improving energy recovery while using only standard SCADA measurements and computational algorithms that do not significantly increase hardware complexity
Data Source
AI summary
The present invention is a method of determining the free-flow wind speed (V∞) for a wind farm, using measurements (MES), a wind farm model (MOD) and an ensemble Kalman filter (KEN).