Probabilistic neural network algorithm-based cable state evaluation method

A technology of probabilistic neural network and status assessment, applied in the field of cable status assessment based on probabilistic neural network algorithm, can solve the problems of lack of cable status assessment system, lack of multi-source information database, multi-source information fusion, etc.

Active Publication Date: 2016-12-21
CHINA ELECTRIC POWER RES INST +2
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Problems solved by technology

[0004] At present, the evaluation methods and systems for the status of transmission cables are still not perfect, and there is a lack of multi-source information database to characterize the operation of cables. For incomplete information systems, there is no suitable information fusion technology to fuse multi-source information, and there is a lack of complete cable status. The evaluation system cannot objectively and accurately evaluate the operating status of the cable

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  • Probabilistic neural network algorithm-based cable state evaluation method
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[0050] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0051] The present invention builds a multi-source information database based on multi-category information sources such as cable equipment basic information, operation information, operation inspection information, and family defects; uses factor analysis to preprocess the database under incomplete information, and proposes a method based on a probabilistic neural network. The multi-source information fusion algorithm can comprehensively evaluate the overall status of the cable under the condition of incomplete information.

[0052] please see figure 1 , the ...

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Abstract

The invention discloses a probabilistic neural network algorithm-based cable state evaluation method. The method includes the following steps that: a multi-source information database is constructed according to many types of information sources such as basic information, operation information, operation inspection information and family defects of cable equipment; and the database under an incomplete information condition is pre-processed by using a factor analysis method, and a probabilistic neural network-based multi-source information algorithm is put forward, and the overall state of a cable under an incomplete information condition can be comprehensively evaluated. With the method of the invention adopted, the fault of a cable can be detected timely and judged accurately, and the operating state of the cable can comprehensively reflected.

Description

technical field [0001] The invention belongs to the technical field of power systems, and relates to a cable state evaluation method, in particular to a cable state evaluation method based on a probability neural network algorithm. technical background [0002] The power cable is a very important device in the power system. Once a fault occurs, it will cause a long-term power outage for users, and it may cause a chain reaction of cable-related equipment to fail, and even cause a partial paralysis of the power distribution system. Therefore, it is particularly important to find cable faults in time and repair them. However, the traditional planned maintenance is no longer suitable for the high reliability requirements of modern power systems. Currently, condition-based maintenance based on condition monitoring and fault diagnosis is being developed. Condition-based maintenance is based on the status evaluation of the equipment, and the status evaluation is based on the charac...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06N3/04
CPCG06Q10/06393G06N3/047
Inventor 李玉凌李二霞樊勇华亢超群史常凯
Owner CHINA ELECTRIC POWER RES INST
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