Power transmission network planning method based on deep learning

Through the transmission network planning method based on deep learning, the convolutional neural network model is used to self-learn the grid operation rules, and the problems of insufficient high-dimensional complexity and dynamic adaptability in traditional methods are solved, achieving more efficient and accurate transmission network planning and dynamic response capabilities.

CN119692731AInactive Publication Date: 2025-03-25CHANGCHUN UNIV OF TECH
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Patent Information

Application Number
CN202510205931.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-25
Publication Date
2025-03-25
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The traditional transmission grid planning method based on mathematical planning model has high-dimensional complexity, nonlinearity and discrete challenges, as well as insufficient scalability and adaptability, which is difficult to effectively solve the planning needs of large-scale power grids.

Method used

Using a transmission network planning method based on deep learning, the convolutional neural network model is constructed, and the grid operation rules are learned by using massive spatiotemporal data and uncertain scenarios to generate more robust planning solutions, and directly coordinate the complex trade-offs between multiple targets through end-to-end global optimization capabilities.

Benefits of technology

It improves the solution efficiency and accuracy of transmission network planning, enhances the dynamic adaptability to large-scale power grids, reduces the complexity of modeling and solution, and supports high-precision timing prediction and topological analysis, and optimizes the layout of transmission corridors and disaster resilience design.

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Abstract

The invention discloses a power transmission network planning method based on deep learning, and belongs to the technical field of power system planning. The invention provides a power transmission network planning model based on a deep learning method for solving the problem that current power transmission network planning excessively depends on a mathematical formula. The method comprises the following steps of: firstly, acquiring a generator set output and line planning scheme by changing a load by utilizing a traditional mathematical planning model so as to generate a data set required by deep learning model training; secondly, a deep learning model based on the convolutional neural network is constructed, the model comprises a convolutional layer, a pooling layer and a full connection layer, the input of the model is composed of load data and generator set output data, and the output of the model is line planning data; the invention provides a simpler and more efficient planning method for the power system under the background that the current power transmission network planning mathematical model is increasingly complex, and has important value in the technical field of power system planning.
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