A fast evaluation method for power transmission section power limit based on hybrid supervised learning

Through a hybrid supervised learning method, the power limit of the power grid transmission section is evaluated online using machine learning models, which solves the problem of difficulty in quickly and accurately evaluating the power limit of the transmission section in the prior art, and realizes the real-time safety, stability and economic needs of the power grid.

CN114936527BActive Publication Date: 2025-03-21ZHEJIANG UNIV
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Patent Information

Application Number
CN202210627947.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-06-06
Publication Date
2025-03-21
Estimated Expiration
2042-06-06

AI Technical Summary

Technical Problem

The prior art is difficult to quickly and accurately evaluate the power limit of the transmission section in large power grids, making it difficult for the grid to meet real-time requirements.

Method used

A hybrid supervised learning method is adopted to collect grid cross-sectional current information and SCADA data to form a sample library, and supervised learning training is used using a variety of machine learning models (such as deep neural networks, random forests, support vector regression, gradient improvement decision tree, etc.) to select the best model for online evaluation.

Benefits of technology

It realizes a fast and accurate online evaluation of the power limit of the transmission section, and can be effectively verified in the 500-node transmission network model with real operation characteristics, improving the safety, stability and economics of the power grid.

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Abstract

The present invention discloses a method for rapid assessment of power limit of power section based on hybrid supervised learning, comprising: (1) collecting a large amount of power section flow and SCADA data for different operation modes of the power grid to form a power grid operation mode file; (2) simulating and analyzing different operation modes of the power grid to obtain the power limit value of the power section of the corresponding operation mode; (3) using flow state information as data features, and putting the data features and the corresponding power limit value of the power section into a sample library; (4) using multiple machine learning models for supervised learning training, and selecting the optimal model from them; (5) using the selected optimal model to perform rapid assessment of power limit of power section. The present invention can form an accurate matching relationship between the operation state of the power grid and the power limit through a large amount of simulation analysis and feature engineering of historical operation modes, training and regularly updating the machine learning model, so as to quickly and accurately assess the power limit of the power section in real time.
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