Wind Turbine Control Feature Optimization for Lifetime and Yield
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Wind turbines face challenges in operating efficiently due to varying environmental conditions and different control schemes, making it difficult for operators to achieve desired outcomes such as maximum lifetime or energy production.
Innovation Solution
A method using a controller to optimize the activation states of multiple control features, such as high wind ride-through, adaptive control, and power boost, by determining optimization parameters like lifetime or energy production, and adjusting these settings to meet specific targets through a series of estimation and evaluation steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple control features are activated to improve wind turbine performance, then energy production and lifetime can be optimized, but the complexity of control system configuration increases
Solution Approach 1:
The controller automatically determines the optimal activation states of control features based on measured operating parameters and stored optimization strategies, eliminating the need for manual operator configuration. The system self-adjusts by selecting from pre-stored strategy sets that define combinations of control feature activations, thereby simplifying operation while maximizing energy production.
Solution Approach 2:
The system stores multiple optimization strategies, each defining different activation states for control features under specific operating conditions. By changing the active strategy based on measured parameters (wind speed, power output, etc.), the system optimizes energy production without requiring complex real-time calculations or manual intervention.
2Reliability
If operators manually configure control features to achieve maximum lifetime or energy production, then optimization targets can be met, but the ease of operation decreases due to the need for specialized knowledge
Solution Approach 1:
The controller automatically selects and applies optimization strategies based on measured operating parameters, eliminating the need for operators to manually configure control features. The system self-determines the optimal activation states for maximum lifetime or energy production by comparing current conditions against stored strategy definitions.
Solution Approach 2:
Optimization strategies are pre-calculated and stored in the controller before operation. Each strategy defines the optimal activation states for control features under specific operating conditions. During operation, the controller simply retrieves and applies the appropriate pre-defined strategy, eliminating the need for operators to perform complex real-time optimization calculations.
3Productivity
If control features are adjusted to maximize energy production, then productivity increases, but the lifetime of the wind turbine may be reduced due to increased wear and stress
Solution Approach 1:
The system stores multiple optimization strategies, each representing a different balance between energy production and lifetime considerations. By changing the active strategy based on measured operating parameters, the system can adjust the activation states of control features to optimize for either maximum energy production or maximum lifetime, or find a balanced compromise between the two competing objectives.
Solution Approach 2:
The optimization strategy is not fixed but dynamically adjusted based on measured operating conditions. The controller continuously monitors parameters such as wind speed, power output, and operational state, and selects the appropriate pre-defined strategy that best balances energy production and lifetime under current conditions, allowing flexible adaptation to changing environmental and operational contexts.
Data Source
AI summary
A method of operating a wind turbine is provided. A controller is configured to activate or deactivate each of two or more distinct control features, each control feature changing the operating characteristic of the wind turbine and having an impact on at least one of lifetime and energy production . The method includes determining a type of optimization parameter and an optimization target for the optimization parameter, wherein the optimization parameter is related to at least one of lifetime or energy production of the wind turbine. The method further performs one or more optimization steps, wherein each optimization step is performed for a different combination of activation states of the two or more control features. Based on the one or more optimization steps, an optimal combination of activation states of the two or more control features for which the estimated optimization parameter achieves the optimization target is determined.