Wind Turbine Layout Optimization via Pre-screening
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Solution Overview
Problem
Current methods for determining wind turbine layouts in wind power plants require significant computational time and resources, as they involve manual optimization and extensive numerical calculations to account for various constraints, leading to inefficiencies in power production and increased costs.
Innovation Solution
A method that generates multiple random layout candidates, performs pre-screening to reduce the number of candidates through general optimization, and then applies detailed optimization on a selected subset, using statistical and physical models to optimize wind turbine placement based on wind resources, spacing, and exclusion zones, thereby reducing computational demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If commercial software performs extensive numerical calculations to account for all constraints and parameters, then the layout optimization accuracy is improved, but the computational time and computing power requirements increase significantly
Solution Approach 1:
The patent segments the optimization process into multiple stages: generating multiple random layout candidates, performing pre-screening on all candidates, selecting a subset of promising candidates, and conducting detailed optimization only on the selected subset. This segmentation allows the system to maintain high accuracy through detailed optimization while reducing overall computational time by limiting intensive calculations to only the most promising candidates.
Solution Approach 2:
The patent applies partial optimization by performing comprehensive detailed optimization on only a subset of layout candidates rather than all candidates. The pre-screening stage performs basic evaluation on all candidates to identify the most promising ones, and then detailed optimization is applied partially to only those selected candidates, reducing total computational effort while maintaining sufficient accuracy for the final layout.
2Reliability
If manual optimization is performed to meet additional site requirements, then the layout meets all constraints, but the computational time and resource requirements increase
Solution Approach 1:
The patent implements self-service optimization where the computational system automatically performs the entire optimization process including generating candidates, pre-screening, selecting subsets, and detailed optimization without requiring manual intervention. The system autonomously handles all constraint satisfaction and optimization tasks, eliminating the need for manual optimization while maintaining high reliability in meeting all site constraints.
3Measurement precision
If high computing power is used for detailed numerical calculations, then the optimization precision is improved, but the cost and resource requirements increase
Solution Approach 1:
The patent segments the computational workload into two distinct phases: a pre-screening phase that uses minimal computing resources to evaluate all layout candidates with basic criteria, and a detailed optimization phase that uses high computing power and precise numerical calculations only on a selected subset of promising candidates. This segmentation reduces total computing resource consumption while maintaining high optimization precision for the final result.
Solution Approach 2:
The patent applies partial detailed optimization only to a subset of layout candidates selected through pre-screening, rather than performing resource-intensive detailed calculations on all candidates. This partial application of high-precision optimization reduces the quantity of computing resources required while still achieving sufficient precision for the final layout determination.
Data Source
AI summary
The invention provides a method for determining a wind turbine layout in a wind power plant comprising a plurality of wind turbines. The method comprises the steps of generating a plurality of random layout candidates fulfilling a set of basic requirements, and then performing a pre screening process on each of the plurality of random layout candidates. Based on the pre-screening process, a subset of layout candidates is selected and detailed optimization is performed on the layout candidates of the selected subset of layout candidates. Based on the detailed optimization, an optimized layout for the wind power plant is selected among the optimized layout candidates of the subset of layout candidates.