Voltage sag source recognition model construction method and device, terminal and medium

Abstract

The invention discloses a voltage sag source recognition model construction method and device, a terminal and a medium. The method comprises the steps of extracting a feature curve of historical voltage sag source data through an S transformation formula based on the historical voltage sag source data, and obtaining feature index data based on the feature curve and in combination with a feature index calculation formula; inputting the feature index data into an initial neural network model, the initial neural network model being a hyper-parameter obtained through optimization based on a Bayesian optimization algorithm, enabling the constructed LSTM network model to train the initial neural network model through the feature index data, and analyzing actual voltage sag data by using the trained voltage sag source recognition model. According to the method, the Bayesian algorithm is used for optimizing the parameters of the long-short-term memory network model, the optimal parameters are obtained and set in the long-short-term memory network, the low efficiency of manual parameter adjustment is avoided, and the accuracy of identifying different voltage sag sources by the network is effectively improved.

Description

technical field [0001] The present application relates to the field of electric power technology, in particular to a method, device, terminal and medium for constructing a voltage sag source identification model. Background technique [0002] With the development of technology in the field of industrial control automation and intelligent manufacturing, digital and power electronic loads represented by microprocessors are very sensitive to voltage sags while the proportion of the system is increasing. Short-circuit faults, induction motor starting, and transformer switching will all cause voltage sags to varying degrees. [0003] When events such as short-circuit faults, induction motor startup, lightning strikes, switch operations, switching of transformers and capacitor banks occur in the transmission and distribution system, voltage sags can be caused. Among them, short-circuit fault, induction motor startup, and lightning strike are the most important causes of voltage s...

AI Technical Summary

Problems solved by technology

[0005] This application provides a voltage sag source identification model construction method, device, terminal and medium, which are used to solve the existing technical problem of low accuracy in voltage sag source identification

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