Transformer substation electrical equipment temperature prediction method

Abstract

The invention relates to a transformer substation electrical equipment temperature prediction method, which comprises the following steps of taking the collected electrical equipment operation parameters and environment parameters as the input variables, and establishing a prediction model by utilizing a deep belief network (DBN) to predict the temperatures of the electrical equipment. Accordingto the present invention, by firstly carrying out the deep feature extraction on the input electrical equipment parameter data by adopting a deep belief network stacked by a RBM (restricted boltzmannmachine) to complete an unsupervised learning process, taking the high-dimensional characteristic quantity outputted by the last layer of the DBN as the input of the neural network, and carrying out the conventional fitting to obtain a prediction result, and finally, using the trained DBN-NN model for predicting the temperatures of electrical equipment in a transformer substation, through the temperature prediction method provided by the invention, the temperatures of the electrical equipment can be predicted more accurately, so that a new method is provided for solving the prediction estimation problem and reducing the faults of the electrical equipment of the transformer substation.

Description

technical field [0001] The invention relates to the technical field of temperature prediction of electrical equipment, in particular to a method for temperature prediction of electrical equipment in substations. Background technique [0002] The substation is the hub of the power supply system and an important part of the power grid system to achieve power distribution and voltage conversion. Its safety is directly related to the safety of the entire power grid. The substation link is a link with a high incidence of disasters and accidents in the power grid, and most of the accidents are equipment fires. The temperature can well reflect the operating status of electrical equipment, and the early prediction of the operating status of electrical equipment can provide guarantee for the safe and stable operation of substations. Therefore, selecting the equipment temperature as the monitoring parameter, combining multiple parameters to jointly predict the equipment temperature, ...

AI Technical Summary

Problems solved by technology

This makes the collected time series data have great nonlinear and unstable characteristics, which poses a huge challenge to its accuracy prediction

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