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Transformer fault diagnosis method based on coupled hidden Markov model

A Hidden Markov, Transformer Fault Technology, applied in instruments, scientific instruments, measuring devices, etc., can solve the problems of high probability of observation sequence and unknown model parameters, etc.

Inactive Publication Date: 2015-09-09
STATE GRID CORP OF CHINA +3
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Problems solved by technology

[0022] HMM has three basic problems: 1) Evaluation problem: Given an observation sequence and model, how to quickly calculate the probability of the observation sequence given the model? 2) Decoding problem: given an observation sequence and model, how to find the optimal hidden state sequence in a certain sense? 3) Learning problem: that is, the model parameters of the HMM are unknown, how to adjust these parameters to make the probability of the observation sequence as large as possible?

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  • Transformer fault diagnosis method based on coupled hidden Markov model
  • Transformer fault diagnosis method based on coupled hidden Markov model
  • Transformer fault diagnosis method based on coupled hidden Markov model

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Embodiment Construction

[0068] The present invention will be described in further detail below with reference to the accompanying drawings and examples.

[0069] The invention performs transformer fault diagnosis based on transformer oil chromatographic data based on a coupled hidden Markov model, and mainly includes two steps.

[0070] Step 1 is to train the coupled hidden Markov model based on the obtained oil chromatographic data. The specific process is as follows: figure 2 Shown:

[0071] 1-1) Obtain the oil chromatography data used for training the coupled hidden Markov model, which can be either online monitoring data or offline testing data. The data is required to be the data of the specified fault type or normal state, and the data of the corresponding state is used when training various model parameters. The above data content is the measured value of the five gases dissolved in oil, namely hydrogen, methane, ethane, ethylene and acetylene.

[0072] 1-2) According to the measured value...

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Abstract

The invention discloses a transformer fault diagnosis method based on a coupled hidden Markov model, which belongs to the technical field of electrical equipment state monitoring and fault diagnosis. The method adopts left and right hidden Markov chains, measured values of five gases in oil chromatography are input as observed values of one chain, corresponding ratio values of the five gases are input as observed values of the other chain, and then transformer fault diagnosis is carried out. The method provided by the invention retains the advantages of the hidden Markov model, is applicable to analysis of non-stationary signals with poor repeatability and can carry out fault diagnosis based on multichannel information.

Description

technical field [0001] The invention belongs to the technical field of state monitoring and fault diagnosis of electric equipment, and in particular relates to a transformer fault diagnosis method based on transformer oil chromatographic data. Background technique [0002] Large-scale power transformers are important electrical equipment in the power system, and their operating status directly affects the safe and stable operation of the power system, especially for power transformers that need to operate for a long time, the reliability issue is very important, once a failure occurs Huge economic losses will be caused. With the development of modern power systems towards large capacity and ultra-high voltage, there are higher requirements for power supply reliability. Therefore, it is of great significance to carry out fault diagnosis and monitoring of transformers. [0003] Dissolved Gas Analysis (DGA) in oil is quite effective in finding latent faults inside the transfor...

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Application Information

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IPC IPC(8): G01N30/00
Inventor 罗剑波杨志新卫志农倪明过浩余文杰孙国强孙永辉朱超向育鹏
Owner STATE GRID CORP OF CHINA
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