Wind Farm Power Curve Validation via Turbine Data Aggregation
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Solution Overview
Problem
Current wind farm control methods prioritize individual turbine optimization, leading to sub-optimal performance at the farm level due to wake effects and inefficiencies in assessing upgrades, particularly due to imprecise nacelle anemometer measurements and lack of farm-level power curve generation.
Innovation Solution
A method and system for generating farm-level power curves by aggregating turbine-level operational data into representative time-series, analyzing, and comparing data from baseline and upgraded operational modes to assess the benefit of upgrades, using techniques like data binning, regression analysis, and mitigating data loss through power scaling and back-filling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If nacelle anemometer measurements are used to assess wind turbine performance, then the assessment method is simple and widely available, but measurement precision is insufficient leading to inaccurate AEP estimates
Solution Approach 1:
The patent introduces farm-level power curves as an intermediary tool that uses aggregated operational data from multiple turbines to create more accurate wind speed and power relationships, compensating for the imprecision of individual nacelle anemometer measurements
Solution Approach 2:
The system combines data from multiple turbine nacelle anemometers to create aggregated operational datasets, improving measurement precision through statistical averaging and reducing the impact of individual sensor errors
2Ease of operation
If traditional individual turbine power curves are used to assess upgrades, then the assessment is straightforward, but the method cannot discern benefits of wake minimization technologies that create more wind for the farm
Solution Approach 1:
The patent transitions from individual turbine-level assessment to farm-level assessment by creating power curves that aggregate data across multiple turbines, adding the dimension of farm-wide performance evaluation that captures wake minimization benefits
Solution Approach 2:
The farm-level power curves serve multiple functions: they assess individual turbine upgrades, evaluate wake minimization technologies, and provide a comprehensive view of farm-level performance improvements that individual turbine curves cannot capture
3Measurement precision
If operational data is aggregated from multiple turbines to create farm-level power curves, then measurement precision and farm-level optimization improve, but data complexity and processing requirements increase
Solution Approach 1:
The patent segments the wind farm into multiple operational modes (baseline and upgraded) and processes data for each mode separately, then compares the results to determine upgrade benefits, reducing the complexity of analyzing all data simultaneously
Data Source
AI summary
The present disclosure is directed to systems and methods for generating one or more farm-level power curves for a wind farm that can be used to validate an upgrade provided to the wind farm. The method includes operating the wind farm in a first operational mode. Another step includes collecting turbine-level operational data from one or more of the wind turbines in the wind farm during the first operational mode. The method also includes aggregating the turbine-level operational data into a representative farm-level time-series. Another step includes analyzing the operational data collected during the first second operational mode. Thus, the method also includes generating one or more farm-level power curves for the first operational mode based on the analyzed operational data.