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A State Monitoring Method of Power Equipment Based on Bayesian Algorithm

A Bayesian algorithm and technology of electric equipment, applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve problems such as slow prediction speed

Active Publication Date: 2020-01-17
STATE GRID SICHUAN ELECTRIC POWER CORP ELECTRIC POWER RES INST +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, the above methods have the problem of slow prediction speed

Method used

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  • A State Monitoring Method of Power Equipment Based on Bayesian Algorithm
  • A State Monitoring Method of Power Equipment Based on Bayesian Algorithm
  • A State Monitoring Method of Power Equipment Based on Bayesian Algorithm

Examples

Experimental program
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Embodiment

[0031] figure 1 It is a flowchart of the state monitoring method of electric equipment based on Bayesian algorithm in the present invention.

[0032] In this example, if figure 1 Shown, the present invention a kind of method based on Bayesian theory and gray model electric equipment status monitoring, comprises the following steps:

[0033] S1. Data collection and preprocessing

[0034] By collecting all kinds of alarm data represented by various sensor parameters during the operation of power equipment as the basic parameters to reflect the operating status of the equipment, the difference in sensor detection accuracy will affect the results of fault diagnosis. The commonly used basic parameters include the time of fault occurrence Data, temperature data, humidity data, noise data, vibration data;

[0035] Power equipment usually operates in a complex environment full of noise, and uncertain factors in the surrounding environment will seriously distort the detection data. ...

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Abstract

The invention discloses a power equipment state monitoring method based on a Bayesian algorithm. A Bayesian network is combined with an evidence theory and is used for the state monitoring of power equipment. Specifically, a signal class in a transformer substation alarm signal and occurrence time attribute data are used as analysis objects. Through a Spark big data platform, massive transformer substation alarm signal data is processed so that an unsatisfactory prediction effect caused by an excessive data volume is avoided. Simultaneously, disadvantages that the prediction accuracy of a traditional prediction method is low, operation efficiency is low and so on are solved, and the feasibility and effectiveness of the Bayesian algorithm in a power equipment state monitoring application are achieved.

Description

technical field [0001] The invention belongs to the technical field of data mining and processing, and more specifically relates to a method for monitoring the state of electric equipment based on Bayesian algorithm combined with gray model theory. Background technique [0002] The smart grid is a modern power system based on an intelligent power transmission and distribution system, and all aspects of the power system are promoting the process of the smart grid. Smart substation is to realize the inflow, control and distribution of electric energy, the key to realize the functions of voltage conversion and power flow control, and also the key to realize the safe and reliable operation and sustainable development of the power system. Due to the harsh working environment, substation equipment will gradually age and eventually fail with the increase of working hours, which will not only cause serious losses to the power system, but also threaten the normal production of other ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/00
CPCG01R31/00
Inventor 冯世林何锐李坚何明高剑孙永超滕予非黄琦崔文虎井实
Owner STATE GRID SICHUAN ELECTRIC POWER CORP ELECTRIC POWER RES INST
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