Wind Turbine Load Sensor Auto-Calibration
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Solution Overview
Problem
Conventional calibration methods for wind turbine load sensor systems are not automatic and require accurate initial installation, lacking a robust system to validate sensor health and verify the accuracy of load measurements across various locations.
Innovation Solution
A computer-implemented method and system that automatically calibrate load sensor systems by receiving sensor signals, determining load estimations based on turbine geometry and input parameters, comparing these to load measurements to calculate correlation coefficients, and calibrating sensors using these coefficients, which also assesses sensor health over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional calibration methods are used, then sensors can be calibrated, but the process is not automatic and requires accurate initial installation with hardwired gages
Solution Approach 1:
The calibration system performs self-calibration by using the relationship between measured loads and estimated loads computed from a computer model. The system automatically determines calibration coefficients without requiring external manual intervention or pre-installed calibration equipment, enabling the system to calibrate itself during normal operation.
Solution Approach 2:
The patent replaces the mechanical hardwired gage system with a computational approach using a computer model that estimates loads based on sensor signals and turbine geometry. This substitution eliminates the need for physical calibration equipment and hardwired connections, enabling automated calibration through software-based computation.
2Reliability
If more sensors are used on the wind turbine, then load validation at various locations is improved, but a robust calibration system is needed to handle the increased complexity
Solution Approach 1:
The calibration system is designed to handle multiple sensors and various load types uniformly through a single computer model framework. The system can calibrate different sensor configurations (strain gauges, accelerometers, proximity probes) and validate loads at multiple locations using the same automated process, making it universally applicable regardless of sensor quantity or type.
Solution Approach 2:
The system manages sensor complexity by dynamically adjusting calibration coefficients for each sensor based on its specific characteristics and location. The computer model incorporates sensor-specific parameters and automatically determines appropriate calibration values, allowing the system to scale to multiple sensors without requiring manual configuration for each one.
3Adaptability or versatility
If manual calibration methods are used, then calibration can be performed, but it requires pre-bridged and hardwired gages that are not flexible
Solution Approach 1:
The patent replaces manual mechanical calibration procedures with an automated computational system. The computer model estimates loads and compares them with measured loads to automatically determine calibration coefficients, eliminating the need for manual gage bridging and hardwired connections. This enables calibration flexibility without compromising installation simplicity.
Solution Approach 2:
The calibration system is dynamic and adaptive, allowing calibration coefficients to be adjusted based on actual operating conditions and sensor performance. Unlike static manual calibration, the system can continuously update calibration values during turbine operation, providing flexibility to adapt to changing conditions without requiring physical reconfiguration of calibration equipment.
Data Source
AI summary
The present disclosure is directed to systems and methods for automatically calibrating a load sensor system of a wind turbine and determining health of same. In one embodiment, the method includes receiving a plurality of sensor signals generated by the plurality of load sensors from the load sensor system. The method also includes determining, via a computer model, a load estimation of the wind turbine based on the sensor signals, turbine geometry, and one or more additional input parameters (e.g. rotor azimuth angle, pitch angle, rotor position, etc.). Another step includes comparing the load estimation to a load measurement to determine one or more correlation coefficients. Thus, the method also includes calibrating the plurality of sensors in the load sensor system based on the correlation coefficients.