Offshore Wind Turbine Design via Knowledge Distillation
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Solution Overview
Problem
The design of offshore wind turbine structures is complex and time-consuming due to the intricate interactions of environmental loads such as wind, wave, and ocean currents, and the confidentiality of wind turbine parameters hinders the large-scale application of intelligent design methods.
Innovation Solution
A method based on sequential knowledge distillation and transfer learning is employed for the intelligent design and application of offshore wind turbine structures. This involves pre-training an AI regression model with open data, performing knowledge distillation to obtain lightweight models, and using transfer learning to adapt these models to specific commercial wind turbines, thereby overcoming confidentiality issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models with increased depth and complexity are used to meet offshore wind power design needs, then design accuracy is improved, but calculation burden and model transmission difficulty increase significantly
Solution Approach 1:
The patent segments the complex deep learning model into multiple lightweight models through knowledge distillation. The teacher model (complex) is divided into student models (lightweight) that can be independently deployed and transmitted, reducing the burden of model transmission while maintaining design accuracy through the distilled knowledge.
Solution Approach 2:
The patent creates simplified copies of the complex deep learning model through knowledge distillation. The student models are lightweight copies that replicate the essential design capabilities of the teacher model without requiring the full computational resources or complexity of the original model.
2Loss of information
If confidential wind turbine parameters are protected to maintain enterprise security, then information security is improved, but large-scale application and promotion of intelligent design products are hindered
Solution Approach 1:
The patent introduces knowledge distillation as an intermediary process that allows confidential parameters to remain protected within the teacher model while transferring essential design knowledge to student models. This mediator mechanism enables secure parameter protection while still allowing the intelligent design product to be widely applied and promoted.
Solution Approach 2:
The patent creates copies of the design knowledge without copying the confidential parameters. The student models replicate the design capabilities and performance characteristics while the original confidential parameters remain secure in the teacher model, enabling both security and scalability.
3Measurement precision
If multiple rounds of iteration are performed for accurate load calculation and structural design, then design accuracy is improved, but design time increases significantly
Solution Approach 1:
The patent performs preliminary action by pre-training the teacher model on comprehensive datasets including various environmental loads and structural configurations. This preliminary training allows the model to learn complex patterns in advance, so that during actual design iterations, the model can provide accurate results more quickly without requiring extensive retraining or multiple rounds of calculation.
Solution Approach 2:
The patent creates lightweight student model copies that can be rapidly deployed for design iterations. These copied models maintain the accuracy learned during preliminary training but execute calculations faster, reducing the time required for multiple design iteration rounds while preserving design accuracy.
Data Source
AI summary
The provided is a method for intelligent design and application of offshore wind turbine structures based on sequential knowledge distillation and transfer learning. The method includes the following steps: S1, obtaining the open data set of the offshore wind turbine structures; S2, more than three random initialization lightweight network models are obtained, under the supervision of the intelligently designed artificial intelligence regression model of the offshore wind turbine structure as the teacher model, the knowledge distillation of the lightweight network model is performed to obtain the student model; S3, transfer learning is used for the student model, and the undeclared environmental parameters, wind turbine parameters and structural design parameters of the offshore wind power commercial wind turbine of the enterprise are accessed to obtain a lightweight model for the megawatt commercial wind turbine, and the regression model with the highest accuracy is screened.