Wind Farm Controller Optimizing Power and Fatigue Loads
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Solution Overview
Problem
Wind turbines in a farm often maximize their own power output at the expense of neighboring turbines, leading to sub-optimal power output and reduced longevity due to wake effects, with existing optimization techniques focusing solely on power output and neglecting fatigue loads and other metrics.
Innovation Solution
A method and system that optimize farm-level metrics by identifying decision variables and calculating optimum values for wind turbines based on wake-affected conditions, power capture, and damage equivalent loads, using a farm controller and turbine controllers to apply these values and balance power output and fatigue loads across the wind farm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If each wind turbine maximizes its own power output, then individual turbine power output is improved, but wake effects reduce power output and increase fatigue loads on neighboring turbines
Solution Approach 1:
The system changes operational parameters (blade pitch angle, rotor speed, generator torque) of wind turbines to optimize farm-level metrics. By adjusting these parameters dynamically based on wake conditions, the system reduces wake effects while maintaining acceptable power output, resolving the contradiction between individual turbine maximization and farm-wide performance.
Solution Approach 2:
The system implements feedback control by continuously monitoring wake conditions, power output, and fatigue loads, then adjusting turbine operational parameters in response. This closed-loop control enables the system to balance individual turbine performance with farm-wide optimization, mitigating wake effects through real-time parameter adjustments.
2Productivity
If conventional operation maximizes power output, then power capture is improved, but fatigue loads increase reducing turbine life
Solution Approach 1:
The system dynamically adjusts operational parameters including blade pitch angle and rotor speed to balance power capture with fatigue load management. By modifying these parameters based on real-time conditions, the system maintains acceptable power output while reducing fatigue loads to extend turbine life.
Solution Approach 2:
The system applies partial action by not always maximizing power capture at full capacity. Instead, it selectively optimizes power output based on wake conditions and fatigue load thresholds, accepting reduced power capture in certain conditions to protect turbine longevity, thereby resolving the contradiction between productivity and reliability.
3Productivity
If existing optimization techniques focus on power output, then power capture is improved, but other farm-level metrics like fatigue loads are neglected
Solution Approach 1:
The system implements multi-functionality by optimizing multiple farm-level metrics simultaneously, including power output, fatigue loads, and other performance indicators. The control module evaluates and balances these diverse metrics through a unified optimization framework, enabling the system to address multiple objectives rather than focusing solely on power capture.
Solution Approach 2:
The system transitions from single-objective optimization (power output only) to multi-dimensional optimization by incorporating additional metrics such as fatigue loads and turbine life. This dimensional expansion allows the system to consider a broader range of farm-level performance indicators, resolving the limitation of existing techniques.
Data Source
AI summary
A method for optimizing one or more farm-level metrics in a wind farm is presented. The method includes identifying an optimization objective and one or more decision variables for optimization. Furthermore, the method includes optimizing the optimization objective based on wake-affected wind conditions, power capture values, or damage equivalent load values, to calculate optimum decision variable values for each wind turbine. The method also includes transmitting the optimum decision variable values to the respective wind turbines. In addition, the method includes applying the optimum decision variable values to the respective wind turbines to achieve the optimization objective. Systems and non-transitory computer readable medium configured to perform the method for optimizing one or more farm-level metrics in a wind farm are also presented.