Power transmission line fault type discrimination method and system based on capsule network

Abstract

The invention provides a power transmission line fault type discrimination method and system based on a capsule network, and the method comprises the steps: obtaining a large amount of PMU (Phasor Measurement Unit) data, and carrying out the imaging of the PMU data, and generating a radar map; constructing a power transmission line fault type discrimination model based on a capsule network, dividing a training set and a test set, inputting the training set into the model for training, and extracting change features of graphical PMU data; inputting a test set into the trained model for testing, and classifying and outputting test results in the form of a confusion matrix; therefore, the fault type of the power transmission line is discriminated. Logical reasoning does not need to be carried out, a large amount of knowledge for describing protection system behaviors does not need to be introduced, and only the features corresponding to different fault types need to be extracted for the graphical PMU data. Modeling and model modification processes are simple and easy to operate, PMU data are graphical, computing resources are saved, the model training test speed is high, and the diagnosis accuracy is relatively high.

Description

technical field [0001] The present invention relates to the field of grid fault diagnosis, and more specifically, to a capsule network-based transmission line fault type discrimination method and system. Background technique [0002] As wind energy, solar energy and other new energy sources, as well as flexible loads, controllable loads, and distributed power sources, are connected to the grid on a large scale, the complexity and uncertainty of the grid are increasing. The development of complex power grids and power markets brings obvious economic benefits, but also poses severe challenges to the security of power grids. Power grid fault diagnosis is the basis for accident analysis and accident handling, and is an important application to realize the self-healing function of smart grid, which is of great significance to improve the stability of power grid. [0003] The current power grid fault diagnosis is mainly divided into rule-based fault diagnosis and data-driven faul...

AI Technical Summary

Problems solved by technology

Among them, rule-based fault diagnosis methods include expert systems, information fusion, Petri nets, analytical models, etc., and data-driven fault diagnosis methods are mainly based on neural networks, but most of the neural networks used are 3-4 layer structures, and deep knowledge Insufficient extraction capacity

[0004] For the diagnosis of online power grid faults, the existing fault diagnosis technology needs to introduce a large number of protection and equipment action rules in the early stage of modeling, the modeling process and model maintenance process are difficult, and the versatility is low

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