Transformer substation electrical equipment temperature prediction method

A technology of electrical equipment and prediction methods, applied in the fields of electrical digital data processing, computer-aided design, design optimization/simulation, etc., to achieve the effect of improving modeling ability and prediction accuracy

Active Publication Date: 2019-08-27
SHAANXI UNIV OF SCI & TECH
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Problems solved by technology

This makes the collected time series data have great nonlinear and unstabl

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  • Transformer substation electrical equipment temperature prediction method
  • Transformer substation electrical equipment temperature prediction method
  • Transformer substation electrical equipment temperature prediction method

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Embodiment example

[0097] Step 1: Data preprocessing. The experimental data comes from the historical data of a 330KV main transformer in a substation in Shaanxi Province from March to June 2018, and the data collection time interval is 2 hours. According to the characteristics of the researched object, six variables affecting the transformer winding temperature, load current, active power, reactive power, grid frequency, ambient temperature and top oil temperature, are used as input to predict the winding temperature. The first 1404 groups are selected as the training set, and the last 36 groups are selected as the test set, that is, the last 36 groups of data are selected for testing. Divide the 1404 training sets into 39 small-batch data sets, each with 36 small-batch data sets.

[0098] Step 2: Establish a temperature prediction model for electrical equipment. In order to optimize the prediction model, the number of hidden layer nodes is selected layer by layer by enumeration method. ...

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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, ...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06N3/04G06Q50/06
CPCG06Q50/06G06F30/20G06N3/048G06N3/045Y04S10/50
Inventor 郭文强王立贤董瑶张梦梦李清华侯勇严全定可
Owner SHAANXI UNIV OF SCI & TECH
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