Smart grid fault self-healing method and system based on generative artificial intelligence

By applying generative artificial intelligence technology in smart grids, the problem of degradation of diagnostic accuracy and reliability in the face of complex faults is solved, and more efficient fault diagnosis and self-healing capabilities are achieved, and dynamic changes in the power grid environment are adapted to.

CN119782725APending Publication Date: 2025-04-08STATE GRID LIAONING ECONOMIC TECHN INST
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Patent Information

Application Number
CN202411594468.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-09
Publication Date
2025-04-08

AI Technical Summary

Technical Problem

When existing smart grid fault diagnosis and self-healing systems face complex and sudden grid faults, the diagnostic accuracy and reliability are reduced, and lack real-time and adaptability, making it difficult to adapt to dynamic changes in the power grid environment.

Method used

A method based on generative artificial intelligence is adopted to obtain multi-source heterogeneous data for preprocessing, generate pre-trained language models and perform domain adaptive fine-tuning, feature fusion is performed in combination with multimodal data fusion method, and a deep reinforcement learning algorithm is used to model the fault self-healing process, generate the optimal fault self-healing solution and convert it into device operation instructions.

Benefits of technology

It improves the accuracy and reliability of fault diagnosis, enhances the adaptability and real-time nature of the system, can more effectively adapt to changes in the power grid environment, and improves the reliability and self-healing ability of the smart grid.

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Abstract

The present invention discloses a method and system for smart grid fault self-healing based on generative artificial intelligence, which relates to the field of smart grid fault self-healing. The method includes: obtaining and preprocessing multi-source heterogeneous data; generating a pre-trained language model and performing domain adaptability fine-tuning, adding a task-specific output layer; performing feature fusion using a multimodal data fusion method, and using annotated historical fault data sets for supervised training to obtain fault diagnosis results. Fault self-healing is modeled as a Markov decision process, and a deep reinforcement learning algorithm is used to interactively learn the optimal decision-making strategy with a power grid simulation environment, and an optimal fault self-healing plan is generated and converted into specific operation instructions. The present invention integrates multi-source heterogeneous data, performs accurate positioning and description generation, improves the reliability and self-healing ability of the smart grid, and ensures rapid recovery of normal operation when a power grid fails.
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