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Neural network-based cable life prediction method

A prediction method and neural network technology, applied in the field of cables, can solve problems such as inaccurate cable average life, inaccurate cable life detection, and large detection deviation, so as to achieve fewer potential accidents, less deviation in evaluation results, and reduced material costs. wasteful effect

Inactive Publication Date: 2016-11-16
STATE GRID CORP OF CHINA +1
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Problems solved by technology

However, using the three parameters of electric polarization, thermal aging and electric breakdown to detect the life of the cable separately, the data is less, and there is a problem that the cable life detection is not accurate enough, and the average life value of the cable obtained by using the life value detected by the three parameters is not enough. Accurate, large detection deviation

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  • Neural network-based cable life prediction method

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Embodiment Construction

[0033] The present invention will be described in detail below by means of embodiments in conjunction with the accompanying drawings.

[0034] Artificial Neural Networks (Artificial Neural Networks), also referred to as Neural Networks (NNs) or Connection Models, refer to neural networks constructed during the process of learning from biology in the fields of information and computer science. It is an algorithmic mathematical model that imitates the behavior characteristics of the human brain neural network and is widely interconnected by a large number of neurons to form a complex network system for distributed parallel information processing. This kind of network depends on the complexity of the system, and achieves the purpose of processing information by adjusting the interconnection relationship between a large number of internal nodes. The invention proposes a neural network-based cable life prediction method, uses the parameters in the cable use environment to establish...

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Abstract

The invention provides a neural network-based cable life prediction method. The method comprises the steps of training a neural network by adopting temperature, humidity, pressure and voltage information in a cable application environment as variables; after the neural network for cable life prediction is established, inputting temperature, humidity, pressure and voltage information of a to-be-detected cable environment; and performing calculation by the neural network to obtain a life prediction value of a cable, thereby performing assessment on a current usage condition and residual life of the cable and providing a reference for cable replacement. A cable life prediction model is built by adopting the neural network, so that the deficiencies of a conventional cable life detection method are overcome, the cable structure is not destroyed, an assessment result is small in deviation, and a detection result is accurate. The reference is provided for the cable replacement, tedious cable replacement processes are reduced, material waste can be reduced, costs can be lowered, accident hidden danger is reduced, and disasters such as a fire and the like due to the cable are reduced.

Description

technical field [0001] The invention relates to the technical field of cables, and more specifically, relates to a method for predicting cable life. Background technique [0002] Predicting and evaluating the life of cables is an important technical means to reasonably arrange cable replacement and ensure the safety and reliability of power supply, and it is also an extremely important part of effective management of cables in smart grids. As electrical equipment becomes more advanced and more common, the use of power cables will also increase. [0003] The main reasons for cable aging are: [0004] 1) Electrical aging [0005] Electrical aging refers to the long-term action of the electric field, due to quality defects in cable manufacturing, mechanical and external force damage during construction, gaps and cracks in the insulation, etc., resulting in uneven local electric field, induced partial discharge, and conductor variation. Parts, gaps, and impurities are the sta...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06N3/04G06N3/08
CPCG06N3/08G06F30/367G06N3/045
Inventor 张利赵苏
Owner STATE GRID CORP OF CHINA
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