Wind Turbine Tower Digital Twin via Proper Orthogonal Decomposition
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Solution Overview
Problem
The high computational cost of traditional numerical methods for solving physical field differential equations makes it difficult to construct a digital twinning model for the tower of a wind turbine generator system, which is essential for online monitoring of its operation state.
Innovation Solution
A digital twinning method using model reduction techniques, specifically proper orthogonal decomposition (POD), to obtain a low-dimensional reduced-order model from a high-dimensional finite element model, reducing computational costs and enabling real-time calculation of physical quantities like stress and strain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional numerical methods (finite element method, finite volume method, finite difference method) are used to solve physical field differential equations for digital twinning, then the model can capture complex physical behavior, but the computational cost becomes excessively high due to the large number of degrees of freedom in discretized equations
Solution Approach 1:
The patent extracts the essential physical behavior from the complex finite element model by identifying and retaining only the most significant modes through proper orthogonal decomposition. This separates the critical physical information from the computationally expensive details, allowing accurate simulation with reduced computational cost.
Solution Approach 2:
The patent transforms the high-dimensional finite element model into a low-dimensional reduced-order model by projecting the physical field onto a subset of dominant modes. This dimensional reduction maintains the essential physics while dramatically reducing the number of degrees of freedom and computational requirements.
2Measurement precision
If traditional numerical methods are used to construct digital twinning model of the tower, then the model can provide detailed physical field information, but the high computational cost makes it difficult to achieve real-time online monitoring
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing the dominant modes through proper orthogonal decomposition during an offline phase. These pre-computed modes are then used to rapidly assess the tower state during online monitoring, eliminating the need for time-consuming real-time finite element analysis.
Solution Approach 2:
The patent creates a simplified copy of the physical tower system through the reduced-order model that captures the essential physical behavior. This digital copy can be rapidly queried for stress, strain, and other physical quantities without re-solving the complex original model, enabling real-time monitoring.
3Productivity
If model reduction method is used to obtain low-dimensional reduced order model, then computational cost is reduced and real-time calculation is enabled, but the complexity of model construction and verification increases
Solution Approach 1:
The patent develops a universal model reduction framework based on proper orthogonal decomposition that can be applied to various tower configurations and loading conditions. The same reduction procedure and resulting modal basis can serve multiple purposes including stress analysis, vibration analysis, and real-time monitoring, reducing overall system complexity.
Data Source
AI summary
Disclosed is a digital twinning method for monitoring an operation state of a tower of a wind turbine generator system online. The method includes: 1) constructing a simplified model of the tower of the wind turbine generator system, and discretizing the simplified model according to a finite element method to obtain a finite element model; 2) reducing an order of the finite element model of the tower according to proper orthogonal decomposition, analyzing precision of a reduced-order model under different orders, and selecting the reduced-order model having a smallest reduced order as a final reduced-order model on the premise that the precision satisfies actual engineering requirements; and 3) programming upper computer software in a computer, deploying the reduced-order model to the upper computer software, further building a physical entity of the tower, and monitoring a stress and a strain of the physical entity online through the reduced-order model.