Wind Turbine Power Output Estimation Using Dynamic Transfer Functions
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Solution Overview
Problem
Wind farm operators face challenges in accurately estimating revenue loss and determining excess capacity when wind turbines are operated in a curtailed state, as existing methods lack precision in calculating potential power output due to varying operating conditions.
Innovation Solution
A method and system that utilize a transfer function to relate power output to operating conditions, allowing for the calculation of possible power output by creating a model from data sampled during normal operation and updating it in real-time, enabling accurate estimation of power output even when turbines are curtailed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If wind turbines are operated in a curtailed state to meet grid power output requirements, then grid power output control is improved, but revenue loss and excess capacity determination become inaccurate
Solution Approach 1:
The system dynamically adapts the transfer function by continuously updating it with recent performance data samples. This allows the model to adjust to changing operating conditions and maintain accuracy even when turbines are curtailed, resolving the contradiction between grid power control and estimation accuracy.
Solution Approach 2:
The system changes the parameters of the transfer function over time by incorporating weighted recent data samples. This parameter adaptation enables accurate power output estimation under curtailment conditions while maintaining the ability to meet grid power requirements.
2Measurement precision
If a transfer function model is created from historical data to estimate power output, then power output estimation is improved, but the system requires extensive historical data storage
Solution Approach 1:
The system extracts only the essential recent performance data samples needed for transfer function updates, discarding the need to store extensive historical data. This extraction approach maintains estimation accuracy while minimizing data storage requirements.
Solution Approach 2:
Instead of using all historical data, the system uses a partial set of recent performance data samples with appropriate weighting. This partial action approach provides sufficient accuracy for curtailment detection without requiring comprehensive historical data storage.
3Measurement precision
If the transfer function is updated frequently to reflect changing operating conditions, then estimation accuracy is improved, but computational complexity increases
Solution Approach 1:
The system performs transfer function updates at periodic intervals based on new performance data samples rather than continuously. This periodic action maintains accuracy while reducing computational complexity compared to continuous updates.
Data Source
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AI summary
A device for use in calculating a possible power output of a wind turbine (100) is provided. The device includes a sensor interface (220) configured to receive an operating condition and a power output at a plurality of first times from one or more sensors associated with a wind turbine, a memory device (210) coupled in communication with the sensor interface and configured to store a series of performance data samples that include an operating condition and a power output, and a processor (205) coupled in communication with the memory device and is programmed to calculate a transfer function relating power output to the operating condition based at least in part on the series of performance data samples, and calculate a possible power output based on the transfer function and an operating condition received by the sensor interface at a second time.