Wind Turbine LIDAR Induction Estimation for Rotor-Plane Wind Speed
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Solution Overview
Problem
Current techniques for controlling and monitoring wind turbines do not accurately measure wind speed at the rotor plane, leading to inefficiencies in power production and maintenance, as they rely on imprecise anemometers and LIDAR sensors that cannot continuously estimate the induction zone's dynamic wind braking effects.
Innovation Solution
A method using a LIDAR sensor to measure wind speed in multiple planes and apply linear Kalman filters to determine wind induction factors, allowing for continuous updating of the induction zone and precise estimation of wind speed at the rotor plane.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR sensor is used to measure wind speed in multiple planes, then measurement precision of wind speed at rotor plane is improved, but device complexity increases
Solution Approach 1:
The patent introduces an induction factor as an intermediary parameter that connects wind speed measurements from multiple planes to the rotor plane. Instead of directly measuring at the rotor plane, the system uses LIDAR to measure wind speeds at several upstream planes, calculates induction factors between these planes, and uses these factors to infer the rotor plane wind speed. This intermediary approach resolves the contradiction by achieving precise measurement without requiring direct contact or complex instrumentation at the rotor plane.
Solution Approach 2:
The patent replaces traditional mechanical anemometers with LIDAR (Light Detection and Ranging) technology. LIDAR uses optical methods (laser beams and light scattering) to measure wind speed remotely, eliminating the need for physical contact with the wind flow. This substitution improves measurement precision while reducing mechanical complexity and maintenance requirements, as LIDAR systems have no moving parts that wear out.
2Reliability
If induction factor determination using Kalman filter is implemented, then reliability of wind speed estimation is improved, but loss of time for processing increases
Solution Approach 1:
The patent applies Kalman filters to determine induction factors between measurement planes in advance, before final rotor plane wind speed estimation is needed. By pre-calculating and storing induction factors between intermediate planes, the system reduces the computational burden during real-time operation. When rotor plane wind speed is needed, the system only needs to apply one additional filter using the pre-computed induction factors, rather than performing complex multi-plane calculations from scratch.
Solution Approach 2:
The patent divides the wind speed estimation problem into segmented steps: first determining induction factors between individual measurement planes using Kalman filters, then using these segmented results to calculate the overall rotor plane wind speed. This segmentation allows the complex estimation problem to be broken down into smaller, more manageable computational tasks that can be performed efficiently and reliably.
3Adaptability or versatility
If continuous updating of induction zone is performed, then adaptability to dynamic wind conditions is improved, but use of energy increases
Solution Approach 1:
The patent implements continuous updating of induction zone characteristics by periodically performing wind speed measurements at multiple planes and recalculating induction factors using Kalman filters. Rather than attempting truly continuous measurements, the system uses periodic updates at appropriate intervals, which maintains adaptability to changing wind conditions while reducing energy consumption compared to truly continuous operation. The periodic nature allows the system to balance responsiveness with energy efficiency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables real-time, precise control and diagnosis of wind turbines by accurately accounting for wind braking, optimizing power production and reducing structural loads, thereby improving efficiency and reducing maintenance costs.
Implementation Method 1
A second technique involves using a LIDAR sensor (an acronym for 'light detection and ranging'). LIDAR is a remote sensing or optical measurement technology based on analyzing the properties of a beam reflected back to its emitter.
Implementation Method 2
at least two wind induction factors are determined between two of said measurement planes by means of said wind speed measurements at said measurement planes and a linear Kalman filter
Data Source
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AI summary
The present invention relates to a method for determining a wind induction factor for a wind turbine (1) equipped with a LIDAR sensor (2). For this method, wind speed measurements are taken in several measurement planes (PM) using the LIDAR sensor (2), then induction factors are determined between the measurement planes (PM) using the measurements and a linear Kalman filter, and the induction factor between a measurement plane (PM) and the rotor plane (PR) of the wind turbine (1) is deduced using a second linear Kalman filter.