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NASA is using its expertise in the areas of intelligent controls and systems health management to help broaden our nation’s renewable energy portfolio. Wind turbine manufacturers and operators continually search for ways to maximize energy capture and production, as well as minimize turbine down-time and maintenance costs. Low frequency, time-varying structural modes can interfere with the control systems of utility scale wind turbines resulting in increased fatigue loads on the tower and blades. Disturbance accommodating adaptive residual mode filter control algorithms that allow wind turbines to operate optimally in the presence of these modes have been developed and tested in computer simulation by researchers at NASA Ames Research Center.

Hardware Test Bed and Data Acquisition System for Wind Turbine Adaptive Control Algorithms Overview

A hardware test platform composed of physical analogues to the wind turbine is used to enable validation of control algorithms in a physical setting. Forces and torques obtained from simulation data are applied to a small-scale, dynamic model of a wind turbine tower and generator to facilitate observations of the system’s modal response. Data acquired from this test bed, combined with fatigue analyses, may be used in the development and implementation of advanced adaptive controls for wind turbines.

Two keys to decreasing cost of wind power are:

• Decreasing maintenance costs
• Increasing turbine uptime and lifetime

Intelligent control algorithms can mitigate excitation of tower resonance and reduce tower loads, however:

• Simulations cannot represent tower complexity
• Expensive to field-test new control algorithms


• Create a small-scale test platform consisting

o Physical analogue to wind turbine tower
o Ability to emulate forces on tower and drivetrain

• Use data obtained from high-fidelity simulations to apply analogous forces and torques to tower and generator model

• Acquire acceleration and load data from test bed and analyze to detect any modal behavior not predicted by simulation:

o torsional moments
o other excited modes


PHM Team members:

Kai Goebel

Eric Kato

Jeremy Nelson

Adaptive Control Group Members:

Susan Frost

Khanh Trinh

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Hardware Test Bed and Data Acquisition System for Wind Turbine Adaptive Control Algorithms
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