Wind Farm Power Allocation Under Turbine Fatigue Constraints
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Solution Overview
Problem
Wind farms face challenges in optimizing power output under limited power conditions due to terrain complexity, fluctuating wind resources, and grid transmission limitations, leading to suboptimal economic benefits and increased maintenance costs.
Innovation Solution
A method and apparatus that utilize historical and real-time fatigue load data from wind turbines, combined with machine learning models, to determine optimal power adjustments for individual turbines, ensuring the total output meets grid demands while minimizing fatigue and maintenance costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If wind turbines are stopped to meet limited power requirements, then the power output constraint is satisfied, but the economic benefits and power generation efficiency deteriorate
Solution Approach 1:
The patent applies local quality by differentiating the control strategy for individual wind turbines based on their specific fatigue load characteristics. Instead of uniform stopping or proportional reduction, each turbine receives customized power adjustment instructions based on its historical and real-time fatigue load data, thereby optimizing the balance between meeting power constraints and maintaining economic benefits.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting the power output parameter of wind turbines based on fatigue load parameters. The control system monitors historical equivalent fatigue load, real-time entire fatigue load, and pay per unit power generation to determine optimal power values, transforming static power constraints into dynamic, adaptive control.
2Productivity
If wind turbines operate at high power output, then economic benefits improve, but fatigue load on components increases leading to higher maintenance costs
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring the entire fatigue load and equivalent fatigue load of wind turbine components in real-time. This feedback information is fed back to the control system, which then adjusts the power output accordingly to prevent excessive fatigue accumulation, thereby maintaining reliability while optimizing power generation.
Solution Approach 2:
The patent applies preliminary action by using historical equivalent fatigue load data to predict future fatigue trends before critical damage occurs. The control system proactively adjusts power output based on accumulated fatigue history, preventing excessive fatigue damage before it leads to component failure or expensive maintenance.
3Ease of operation
If simple stopping strategies are used to meet power limits, then control simplicity is maintained, but overall economic benefits and fatigue load distribution deteriorate
Solution Approach 1:
The patent applies self-service by enabling the wind farm control system to automatically optimize power distribution across turbines based on their individual fatigue characteristics. The system autonomously calculates optimal power values using historical and real-time fatigue data, eliminating the need for manual intervention while maximizing economic benefits and ensuring uniform fatigue consumption.
Data Source
AI summary
The present disclosure provides a method and an apparatus for controlling power of a wind farm. The method includes: acquiring a historical equivalent fatigue load of a target component of each of a plurality of wind turbines; acquiring an entire fatigue load, an equivalent fatigue load of the target component and a pay per unit power generation of each of the plurality of wind turbines at a current moment; determining an optimal power value for at least one of the wind turbines according to the historical equivalent fatigue load, as well as the entire fatigue load, the equivalent fatigue load, and the pay per unit power generation; and adjusting a power value of the at least one of the wind turbines to the optimal power value for the wind turbine.