Dynamic Wind Farm Control Optimizing Energy Output
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Solution Overview
Problem
Conventional wind farm control systems operate independently, leading to sub-optimal performance at the farm level due to inaccurate modeling of ambient conditions and neglect of prevailing wind inflow, resulting in marginal improvements in energy output and increased fatigue loads.
Innovation Solution
A system and method that collects and processes wind parameters and operating data from multiple turbines to determine optimal control settings across time intervals, using SCADA data and data quality algorithms to estimate energy production and adjust settings for maximum energy output while maintaining load thresholds, dynamically selecting algorithms based on performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional control models are used, then initial setup is simplified, but accuracy deteriorates due to changing ambient conditions
Solution Approach 1:
The patent transitions from static conventional control models to dynamic models that adapt to changing ambient conditions. The system continuously updates its predictions and control parameters based on real-time weather data, wind conditions, and turbine performance measurements, maintaining accuracy despite environmental variability while preserving ease of implementation through automated adaptation.
Solution Approach 2:
The system dynamically adjusts control parameters based on changing ambient conditions such as wind speed, direction, and temperature. By modifying operational parameters in response to environmental changes, the system maintains high accuracy in energy production estimates without requiring complex manual reconfiguration, thus balancing simplicity with precision.
2Measurement precision
If more data is collected from turbines, then energy production estimates improve, but data quality and availability challenges increase
Solution Approach 1:
The patent introduces data quality assessment algorithms as intermediaries between raw turbine data and energy production estimates. These algorithms filter, validate, and weight data from multiple sources, resolving conflicts and uncertainties to produce reliable estimates. This intermediary layer enables the system to utilize comprehensive data while maintaining high reliability by systematically handling data quality issues.
Data Source
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AI summary
The present disclosure is directed to a system and method 100 for controlling a wind farm. The method 100 includes operating 102 the wind farm based on multiple control settings over a plurality of time intervals. A next step 104 includes collecting one or more wind parameters of the wind farm over the plurality of time intervals and 106 one or more operating data points for each of the wind turbines in the wind farm for the plurality time intervals. The method 100 also includes calculating 108 a contribution of the operating data points for each of the wind turbines as a function of the one or more wind parameters. Further steps of the method 100 include estimating 110 an energy production for the wind farm for each of the control settings based at least in part on the contribution of the operating data points and controlling 112 the wind farm based on optimal control settings.