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A Transformer Fault Detection Method Based on Generative Probability Model

A technology for transformer faults and detection methods, which is applied in instrument, character and pattern recognition, calculation and other directions to achieve the effect of insensitivity to parameter measurement errors and robustness to environmental changes

Active Publication Date: 2019-07-16
ELECTRIC POWER RESEARCH INSTITUTE, CHINA SOUTHERN POWER GRID CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

These methods do not use continuous values ​​to describe the characteristics of transformer parameters, because the transformer parameters contain data of different unit categories such as voltage, temperature, and gas concentration, and it is difficult to directly describe them in a feature vector

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  • A Transformer Fault Detection Method Based on Generative Probability Model
  • A Transformer Fault Detection Method Based on Generative Probability Model
  • A Transformer Fault Detection Method Based on Generative Probability Model

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Embodiment 1

[0032] The transformer fault detection method based on generation probability model of the present embodiment, it comprises the following steps:

[0033] A. Obtain the transformer state sample data; it specifically includes the following steps:

[0034] A1. There are two possible states for defining the transformer, which are normal state and abnormal state respectively. Use S 0 Indicates normal state, use S 1 Indicates an abnormal state, then N=1. ;

[0035] A2. For the two states S of the transformer 0 and S 1 Collect 1000 training data samples respectively, then M 0 =1000, M 1 = 1000, vector and vector Respectively represent the state S 0 and S 1 A training sample data of i∈[1,M n ], n∈{0,1}, the parameters of the transformer are voltage V and current I, then C=2, a sample data can be used express.

[0036] B. Transformer state model learning based on mixed Gaussian model; it specifically includes the following steps:

[0037] B1, use the 1000 training data...

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Abstract

The present invention provides a transformer fault detection method based on a generated probability model, which includes the following steps: A, obtaining transformer state sample data; B, learning a transformer state model based on a mixed Gaussian model; C, learning a transformer state based on a mixed Gaussian model Feature extraction; D. Transformer fault detection based on support vector machine. The invention solves the problem that the transformer fault detection is not robust to environmental changes, is sensitive to parameter measurement errors, and the characteristics of different unit category parameters are uniformly described with continuous values ​​due to the traditional discrete feature extraction method for transformer parameters based on artificially set thresholds.

Description

technical field [0001] The invention relates to a power device fault detection technology, in particular to a transformer fault detection method based on a generation probability model. Background technique [0002] Transformer fault automatic detection algorithm based on transformer parameters plays a vital role in smart grid. The traditional transformer fault detection algorithm is based on human judgment to perform discrete value feature extraction, such as discretization and partition representation of parameters such as temperature and air pressure by artificially setting thresholds. There are many disadvantages in this feature extraction method of artificial discretization. On the one hand, artificially setting the threshold is very sensitive to environmental changes in the detection results. On the other hand, the discrete numerical features make the detection results very sensitive to errors. [0003] Most of the existing related research is devoted to eliminating t...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
CPCG06F18/214
Inventor 黄文琦许爱东郭晓斌陈华军蒋屹新袁小凯张福铮杨航陈富汉蒙家晓关泽武陈立明黄建理吴争荣
Owner ELECTRIC POWER RESEARCH INSTITUTE, CHINA SOUTHERN POWER GRID CO LTD