Deep convolutional neural network tensor input construction method for electric power system analysis

Abstract

The invention provides a deep convolutional neural network tensor input construction method for electric power system analysis and evaluation. The deep convolutional neural network tensor input construction method for the electric power system analysis and evaluation comprises the steps that by using a space distribution representing method of electric power system nodes, an electrical distance between the nodes is described through contract impedance between arbitrary nodes of a power grid; and high-dimensional space node distribution represented by the electrical distance is subjected to dimensionality reduction to a two-dimensional plane by using a node space distribution dimensionality reduction method, and meanwhile, a distance relationship between the nodes of original high-dimensional space is kept. The deep convolutional neural network tensor input construction method is used, electric power system operation data is assigned to the nodes in the two-dimensional plane and a two-dimensional tensor characteristic pattern is obtained, and a plurality of two-dimensional tensor characteristic patterns are superimposed to obtain the electric power system operation data with a tensor form. The deep convolutional neural network tensor input construction method for the electric power system analysis and evaluation improves the precision of the electric power system analysis and evaluation.

Description

technical field [0001] The invention belongs to the technical field of power system safety analysis, and relates to a deep convolutional neural network tensor input construction method for power system analysis. Background technique [0002] The modern large power system forms a regional interconnected transmission pattern through high-voltage and ultra-high voltage AC-DC transmission. The interconnection of the power system makes the network structure more complex, the distribution area is wider, the components are more, and the dynamic behavior is more complex. On the other hand, new energy grid-connected with characteristics of randomness, volatility, and intermittency brings great uncertainty to the operation of the power system, increasing the power angle instability, frequency instability, and voltage instability of the system. stable risk. The stability of the power system is the key to the safe operation of the power grid. Once it is damaged, it will cause huge eco...

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Problems solved by technology

Some deep learning methods, including multi-layer perceptrons, deep belief networks, etc., have also been applied to power system security analysis. However, the input feature quantities

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