Wind Farm Power Prediction Using Localized Reference Stations
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Solution Overview
Problem
Current methods for predicting wind power generation in wind farms are inadequate as they fail to account for localized variations in wind energy due to environmental conditions, leading to inefficiencies and unquantified power losses, particularly in large-scale farms where wind gusts and air pressure changes affect turbine performance.
Innovation Solution
A method involving the collection of turbine data and regional weather data, including air density, to generate power curves that identify and quantify power losses from curtailment, availability, and subcurve losses, allowing for the calculation of potential energy production by aggregating data across wind turbines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If wind farms cover large terrain areas to increase energy generation capacity, then power generation potential increases, but localized variations in wind energy become more significant and harder to account for
Solution Approach 1:
The patent divides the wind farm into multiple measurement zones with localized reference stations. Each reference station measures wind resources for a specific group of turbines, allowing localized variations to be captured. This segmentation enables accurate assessment of wind energy availability across large terrain areas while maintaining measurement precision through distributed monitoring points.
2Ease of operation
If traditional power curve methods are used for predicting wind power generation, then prediction simplicity is maintained, but localized environmental variations are not accounted for leading to power losses
Solution Approach 1:
The patent creates localized power curves for different groups of turbines based on their specific environmental conditions measured by reference stations. Instead of using a single farm-wide power curve, each zone has its own power curve that accounts for local wind resource characteristics, terrain features, and environmental factors. This approach maintains operational simplicity while significantly reducing power generation losses by matching power curves to local conditions.
3Measurement precision
If localized reference stations are deployed for each turbine group to measure wind resources accurately, then wind resource measurement precision improves, but system complexity and cost increase
Solution Approach 1:
The patent implements reference stations for representative groups of turbines rather than for every individual turbine. This partial action approach provides sufficient measurement precision for power curve development and performance assessment without the excessive complexity and cost of fully distributed measurement. The system achieves adequate accuracy by monitoring wind resources at strategic locations that represent local conditions for each turbine group.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables a comprehensive assessment of wind farm performance by accurately quantifying power losses and potential energy production, improving efficiency and enabling better planning and budgeting for wind energy generation.
Implementation Method 1
Wind turbines transform kinetic energy, provided by a wind source, into mechanical energy, which may in turn be used to produce electricity
Implementation Method 2
The local wind speed and air density will affect the amount of force exerted onto the wind turbine blades, which in turn may affect the maximum power output of the wind turbine
Data Source
AI summary
A system and method is disclosed for calculating potential power generation for a wind farm, the wind farm including a plurality of wind turbines. The system and method include measuring the power generated by the wind farm; acquiring turbine data from at least a subset of the plurality of wind turbines, the wind turbine data including local wind speed and power generated at the local wind speed; acquiring wind resource data for the wind farm, the wind resource data including wind speed; generating a power curve from the turbine data and the wind resource data, the power curve plotting the relationship between wind speed and power generated; calculating power lost due to availability, subcurve, and curtailment, the power loss calculated for at least said subset of turbines; and aggregating the power lost in order to determine an aggregate power loss for the wind farm.