Mesoscale Wind Turbine Layout Optimization via Taboo Search
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Solution Overview
Problem
Conventional wind turbine automatic arrangement algorithms are limited to micro-siting and lack applicability in the macro-siting stage, where mesoscale data with lower precision is used, leading to inefficiencies and inaccurate results due to consideration of wind speed and terrain factors.
Innovation Solution
A method and device utilizing mesoscale wind atlas data and terrain data to perform screenings based on wind speed and slope limits, followed by the taboo search algorithm to optimize wind turbine arrangement, focusing on maximizing annual power generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional wind turbine automatic arrangement algorithms based on fluid simulation and wind atlas data are used, then accurate optimization can be achieved in micro-siting stage, but they are not applicable in macro-siting stage where mesoscale data is used
Solution Approach 1:
The patent changes the data input parameters from high-precision wind atlas data (micro-siting) to mesoscale data (macro-siting), and adjusts the algorithm parameters accordingly by introducing wind speed limits and slope limits as screening criteria. This allows the algorithm to adapt to lower precision data while maintaining optimization effectiveness through modified operational parameters.
Solution Approach 2:
The patent segments the optimization process into distinct stages: first screening based on wind speed limits, second screening based on slope limits, and final optimization using taboo search. This segmentation allows the algorithm to handle mesoscale data effectively by breaking down the complex optimization problem into manageable steps that account for data precision limitations.
2Reliability
If comprehensive multi-objective optimization including wake, project cost, investment income, and noise is performed, then accurate and comprehensive results can be obtained, but computational complexity and time consumption increase significantly
Solution Approach 1:
The patent performs preliminary actions by conducting first screening (wind speed limit) and second screening (slope limit) before the main optimization process. This pre-screening eliminates obviously unsuitable areas in advance, reducing the search space and computational burden for the subsequent taboo search optimization, thereby saving computational time while maintaining optimization accuracy.
Solution Approach 2:
The patent applies partial action by focusing the optimization on the most critical factors for macro-siting (wind speed and terrain slope) rather than attempting to optimize all possible parameters simultaneously. This selective approach achieves sufficient optimization accuracy for the macro-siting stage without the excessive computational cost of comprehensive multi-objective optimization.
3Productivity
If no screening is performed on wind field area before optimization, then all potential areas are considered, but the optimization process becomes inefficient and produces inaccurate results due to non-optimal areas
Solution Approach 1:
The patent performs preliminary screening actions before the main optimization process by establishing wind speed limits and slope limits. These preliminary screenings identify and exclude non-optimal areas in advance, ensuring that the subsequent optimization process only considers potentially suitable locations, thereby improving both efficiency and accuracy.
Solution Approach 2:
The patent extracts and removes unsuitable areas from the wind field through systematic screening based on wind speed and slope criteria. By taking out obviously non-optimal areas before optimization, the algorithm focuses computational resources on promising locations, improving both the efficiency and precision of the final arrangement results.
Data Source
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AI summary
A mesoscale data-based automatic wind turbine layout method and device. The method comprises: initially screening an input wind field region on the basis of input mesoscale wind map data by means of a wind speed limit value to obtain a first wind field region (S100); re-screening the first wind field region on the basis of input terrain data by means of a slope limit value to obtain a second wind field region (S200); and determining, by means of tabu search in which a target wind turbine count and the second wind field region are used as inputs, a wind turbine layout that optimizes an objective function (S300), wherein the objective function is the sum of the annual energy production for wind turbine locations.