Transformer online monitoring and fault diagnosis method

A technology for fault diagnosis and transformers, which is applied in the field of online monitoring and fault diagnosis of transformers, and the field of fault diagnosis of transformers, and can solve the problems of normal data redundancy, low failure probability, and multiple types of transformers, etc.

Active Publication Date: 2021-02-05
NORTH CHINA ELECTRIC POWER UNIV (BAODING) +2
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Traditional deep learning methods require a large amount of normal and abnormal data for training, but transformers have many types and are expensive, and the probability of failure in actual operation is low, so normal data is redundant and abnormal data is scarce

Method used

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  • Transformer online monitoring and fault diagnosis method
  • Transformer online monitoring and fault diagnosis method
  • Transformer online monitoring and fault diagnosis method

Examples

Experimental program
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Embodiment 1

[0028] In a specific transformer application scenario, first use voltage transformers and current transformers to collect the voltage and current of the three phases A, B, and C; arrange 8 vibration signal sensors on the surface of the transformer box to collect vibration signals; then A temperature sensor is used to collect the temperature signal of the transformer, and there are 15 signals in total.

[0029] Among them, the sampling frequency of voltage and current signal is 1600Hz; the sampling frequency of vibration signal is 1800Hz; the sampling frequency of temperature signal is 0.01Hz. Extract the latest 64 data collected by each signal at intervals to form a 64-dimensional vector of 15 channels, and then process the vector data of each channel with S transform to obtain a 64×64 complex matrix of 15 channels. After taking the modulus value of each element of the complex matrix, input as attached figure 1 The described unsupervised deep learning framework for diagnosis....

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Abstract

The invention discloses a method applied to online monitoring and fault diagnosis of a transformer. The method comprises four steps. The method comprises the following steps of: 1, acquiring instantaneous values of n paths of signals such as vibration, current, voltage, temperature and the like of different measuring points or different phases of a transformer by using various sensors; 2, respectively processing the acquired n paths of signals by discrete S transformation, and forming a complex matrix of n channels by calculation results; 3, carrying out modulo operation on each element in then-channel complex matrix to obtain an n-channel real matrix, namely a feature image of the signal; and 4, inputting the feature image of the signal into the trained unsupervised deep learning framework to obtain a diagnosis result.

Description

technical field [0001] The invention relates to a fault diagnosis technology of a transformer, in particular to an online monitoring and fault diagnosis method of a transformer, and belongs to the technical field of power electronics. Background technique [0002] The power transformer is one of the most important equipment in the power grid, and maintaining the normal operation of the transformer is related to the safety of the entire power grid. In operation, however, transformers are subjected to electrical, chemical, mechanical, thermal and environmental stresses that can cause winding and core failures. Transformer faults have a great impact on the power grid. Once a sudden accident occurs during operation of a large power transformer, it may cause a large-scale power outage, causing serious adverse social impacts and economic losses. Moreover, the maintenance cost of power transformers is high and the cycle is long, and various economic losses are also very huge. [...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/62G01R15/18G01M13/00G01H17/00G01K13/00
CPCG01R31/62G01R15/18G01M13/00G01H17/00G01K13/00
Inventor 郭春林王嘉宁梁延昌马慧远
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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