OLTC mechanical fault diagnosis method based on MPE and SVM

A diagnostic method and technology for mechanical faults, applied in the testing of mechanical components, testing of machine/structural components, measuring devices, etc., to achieve the effect of high diagnostic accuracy and excellent diagnostic effect

Inactive Publication Date: 2018-08-03
HOHAI UNIV
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  • Application Information

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Problems solved by technology

However, studies have shown that the vibration signal during the OLTC switching process exhibits obvious nonlinear behavior, and the time-frequency analysis method is used to decompose the signal into a smooth signal, which inevitably has certain limitations.

Method used

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  • OLTC mechanical fault diagnosis method based on MPE and SVM
  • OLTC mechanical fault diagnosis method based on MPE and SVM
  • OLTC mechanical fault diagnosis method based on MPE and SVM

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Experimental program
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Effect test

Embodiment

[0062] Simulation experiment on CMIII-500-63B-10193W tap changer. This tap changer is a three-phase Y connection with a maximum of 19 tap positions. The vibration sensor adopts the LC0151 piezoelectric acceleration sensor with high resolution and strong anti-interference ability. In the present invention, the vibration sensor is installed on the top of the tap changer, the vibration signal picked up at this position has less high-frequency attenuation and the signal is more complete. The acceleration sensor collects the vibration signal under the normal state of the OLTC and the vibration signal under the fault state (OLTC contact looseness and spring performance degradation), and divides the collected vibration data into two groups, each group includes normal vibration signals and faults Signals, one set is used for SVM training and the other set is used for SVM testing;

[0063] MPE analysis is carried out on the collected vibration signal, and the feature vector is constr...

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Abstract

The present invention discloses an OLTC mechanical fault diagnosis method based on MPE and SVM. The method comprises the following steps of: 1) performing collection of vibration signals of an on-loadtap-switch (OLTC) in a normal state and vibration signals of the OLTC in fault state through an acceleration sensor to perform preprocessing of the vibration signals; 2) performing multi-scale permutation entropy (MPE) calculation of the collected vibration signals, and constructing feature vectors taken as input of a support vector machine (SVM); and 3) inputting the feature vectors obtained inthe step 2) into the SVM to perform training of the SVM, and inputting test data into the trained SVM to determine a fault mode of the OLTC. The OLTC mechanical fault diagnosis method based on the MPEand the SVM does not need a lot of data for training of the SVM and is higher in diagnosis precision, and is obviously better than a BP neural network in the diagnosis effect of the OLTC.

Description

technical field [0001] The invention relates to the technical field of on-load tap changer fault diagnosis, in particular to a method for diagnosing mechanical faults of an on-load tap changer OLTC based on MPE and SVM. Background technique [0002] With the improvement of power quality requirements, automatic voltage control and other systems are widely used in the power grid. The existing on-load tap-changer (OLTC) is adjusted quite frequently, and the failure rate is very high. According to statistics at home and abroad, tap changer failures account for more than 20% of transformer failures, and they are mainly mechanical failures. If they are not discovered and dealt with in time, their failures will seriously damage the inherent structure of OLTC and transformers, and affect the normal operation of power equipment and systems. Operate safely with serious consequences. Therefore, in order to ensure the safe and reliable operation of the tap changer, it is necessary to c...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/00G01H17/00
CPCG01M13/00G01H17/00
Inventor 马宏忠徐艳李思源刘宝稳刘勇业宋开胜李盛翀吴书煜
Owner HOHAI UNIV
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