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Prediction method for corrosion rate of Q235 galvanized steel grounding net of transformer station

A prediction method and corrosion rate technology, applied in the field of power systems, can solve problems such as inconvenient detection methods, hidden dangers of equipment safe operation, and degradation of grounding performance

Inactive Publication Date: 2016-06-29
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Since the material of the grounding grid is mostly Q235 steel, and it is buried underground all year round, it will often be corroded due to the complex soil environment underground, resulting in deterioration of its grounding performance, which in turn affects the normal operation of the entire power grid
Due to the particularity of the grounding grid, the traditional measurement of the corrosion of the grounding grid is carried out by manually excavating the site. This original and intuitive detection method is not only inconvenient, but also brings hidden dangers to the safe operation of the equipment.

Method used

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  • Prediction method for corrosion rate of Q235 galvanized steel grounding net of transformer station
  • Prediction method for corrosion rate of Q235 galvanized steel grounding net of transformer station
  • Prediction method for corrosion rate of Q235 galvanized steel grounding net of transformer station

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Experimental program
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Embodiment 1

[0055] In the present embodiment, step C adopts RBF neural network to carry out training and learning, and specifically includes the following steps:

[0056] C11. Determine the Spread value.

[0057] The size of the Spread value in the RBF neural network model is determined by numerical heuristics. The specific process is to first try in the range of [1, 100], and the interval of each trial is 5, and then the average value of the network operation results can be determined. The change of the square error selects the Spread value with the best prediction performance of the model at the end. After tentative comparison, when the Spread value is equal to 20, the average relative error of the network is the smallest, and the mean square error is not the largest among the nearby values. Therefore, in this embodiment, it is most appropriate to set the Spread value of the RBF network model to 20.

[0058] C12. Use the training and learning of the RBF neural network, find out the st...

Embodiment 2

[0069] In the present embodiment, step C adopts BP neural network for training and learning, which specifically includes the following steps:

[0070] C21. Use LM algorithm to train BP neural network.

[0071] C22. Determine the number of layers of the BP neural network.

[0072] In the BP neural network model, the number of neurons in the input layer and output layer of the sample data is initially determined, so relatively speaking, the neurons in the hidden layer are uncertain. From the perspective of data processing in this embodiment, a neural network composed of a hidden layer can meet the specified accuracy requirements, so the number of layers of the neural network model established below is 3, which are 1 input layer, 1 hidden layer and 1 output layer.

[0073] C23. Determine the number of nodes in the input layer and output layer.

[0074] The network prediction model constructed in this example has 6 input factors, that is, 6 factors of soil physical and chemical...

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Abstract

The invention discloses a prediction method for the corrosion rate of a Q235 galvanized steel grounding net of a transformer station. The method comprises the following steps that A) an influential factor of soil corrosivity is determined, and a data sample is obtained; B) data is normalized; C) a neural network is used for training and learning to obtain a corrosivity test evaluation network model; D) whether an error of the corrosivity test evaluation network model satisfies requirement is verified and checked; E) the corrosivity test evaluation network model that satisfies the error requirement is used to predict the corrosion rate of the Q235 galvanized steel grounding net of the transformer station; and F) a denormalization method is used to obtain the practical corrosion rate of the Q235 galvanized steel grounding net of the transformer station. According to the invention, the artificial neural network is used for data mining, the practical corrosion state of the Q235 galvanized steel grounding net is predicted on the basis of existing data, hidden safety troubles are discovered, measures are taken, and new reference is provided for protection work of the grounding net of the transformer station.

Description

technical field [0001] The invention relates to the technical field of power systems, in particular to a method for predicting the corrosion rate of a substation grounding grid. Background technique [0002] The safety of the grounding grid is an important guarantee to ensure the safe operation of the substation in the power system, the normal operation of the power equipment and the life safety of the staff. Because the material of the grounding grid is mostly made of Q235 steel, and it is buried in the ground all the year round, it will often corrode due to the complex underground soil environment, resulting in the deterioration of its grounding performance, which in turn affects the normal operation of the entire power grid. Due to the particularity of the grounding grid, the traditional measurement of the corrosiveness of the grounding grid is performed manually after excavating the site. This primitive and intuitive detection method is not only inconvenient, but also br...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/08
CPCG06Q10/04G06N3/08
Inventor 花广如李文浩郭阳阳房静
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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