voltage sag disturbance classification method based on LSTM

Abstract

The invention belongs to the technical field of electric energy quality classification and identification, and discloses a voltage sag disturbance classification method based on a Long Shore-Term Memory algorithm. According to the method, firstly, voltage sag disturbance data are processed through a data processing layer, then feature values are extracted through two layers of LSTM networks, feature dimension reduction processing is conducted through a full connection layer, and finally classification recognition is conducted through a sigmoid network layer. The electric energy quality sag disturbance correlation characteristics are extracted through the deep learning algorithm, and the limitation of a classification recognition modeling method based on time domain, frequency domain, transformation domain and other physical characteristics in the complex power grid environment in the aspects of adaptability, algorithm efficiency and accuracy is overcome. By using the model, the defectsof tedious and complex extraction steps, possible loss of original characteristics of part of signals and the like in the traditional method can be well overcome; The defect that gradient explosion is likely to happen to a traditional recurrent neural network (RNN) is overcome, and high voltage sag disturbance recognition accuracy is achieved.

Description

technical field [0001] The invention relates to an LSTM-based voltage sag disturbance classification method, which belongs to the technical field of power quality classification and identification. Background technique [0002] The scale of the power grid continues to expand, and the grid structure and load types are becoming more and more complex; distributed new energy sources with significant randomness, intermittentity and volatility are connected to the distribution network directly or in the form of micro-grids; introduced to actively accommodate distributed power sources The application of a large number of nonlinear power electronic devices and the interaction of multiple factors between power sources and devices, devices and devices, such as switching of power sources and compensation capacitors, make the concept of modern power quality a complex field covering many topics. [0003] In a complex power grid environment, the factors causing voltage sag disturbances in...

AI Technical Summary

Problems solved by technology

[0009] The purpose of the present invention is to provide a new LSTM-based voltage sag disturbance classification method for problems such as complex and cumbersome manual feature extraction steps for sag signals, easy loss of part of signal features, and low accuracy of signal classification and recognition.

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