Wind Turbine Control Optimization via Iterative Parameter Adjustment
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Solution Overview
Problem
Theoretical models used in wind turbine control are unreliable due to manufacturing errors, sensor inaccuracies, and changes in turbine properties over time, leading to suboptimal turbine operation.
Innovation Solution
A method that establishes a relation between measured wind turbine responses and actively adjusted controller settings, optimizing control for specific conditions and correcting deviations caused by errors or changes, without requiring accurate ambient condition data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If theoretical models are used for turbine control, then control can be implemented based on predicted turbine response, but the control becomes suboptimal due to model inaccuracies and sensor errors
Solution Approach 1:
The patent implements feedback by continuously measuring actual turbine response variables (power output, rotor speed, torque) and using these measurements to iteratively update the performance model parameters. The controller compares predicted vs. actual responses and adjusts model parameters to minimize discrepancies, ensuring the control strategy remains optimal despite model inaccuracies or sensor drift over time
Solution Approach 2:
The patent dynamically adjusts model parameters based on operating conditions and measured data. The performance model parameters are updated iteratively to reflect actual turbine behavior under different ambient conditions (wind speed, temperature, air density), allowing the control system to adapt to changing turbine properties and maintain optimal performance
2Ease of manufacture
If theoretical models with approximations are used, then practical implementation is enabled, but deviations from real conditions reduce control accuracy
Solution Approach 1:
The performance model automatically updates itself using data from the turbine's own operation. The controller uses measured response variables from the actual turbine to iteratively refine model parameters, allowing the system to self-correct and improve accuracy without external intervention or complex calibration procedures
Solution Approach 2:
The patent performs preliminary iterative optimization of model parameters during initial operation and before each control cycle. By pre-adjusting model parameters based on historical data and current operating conditions, the system ensures accurate predictions are available before control decisions are made, improving real-time control accuracy
3Loss of information
If sensor data is used for model input, then ambient conditions can be measured, but systematic errors and sensor drift reduce data reliability
Solution Approach 1:
The system uses feedback to detect and compensate for sensor errors. By comparing predicted turbine response (based on ambient conditions and model parameters) with actual measured response, the controller can identify discrepancies caused by sensor drift or systematic errors and adjust model parameters accordingly, effectively filtering out sensor inaccuracies
Solution Approach 2:
The patent performs preliminary validation and adjustment of sensor data before using it for control decisions. Ambient condition measurements are processed and cross-checked against turbine response data to ensure consistency, and model parameters are pre-adjusted to account for known sensor characteristics or drift patterns
Data Source
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AI summary
A method for optimizing the operation of a wind turbine is provided, the method comprising the steps of: (a) adjusting at least one control parameter of said wind turbine to a predetermined starting value; (b) measuring at least one response variable of said wind turbine and at least one further variable indicative of an ambient condition of the wind turbine; (c) repeating step (b) N times, wherein N is a predetermined integer, wherein said at least one control parameter is varied at each repetition; (d) determining a measured relation between the at least one control parameter with respect to the at least one response variable and the at least one further variable indicative of an ambient condition; (e) determining an optimized value of said at least one control parameter with respect to said response variable from said measured relation; (f) adjusting a set point of said at least one control parameter to said optimized value.