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Transformer fault diagnosis method based on random forest

A technology for transformer faults and diagnosis methods, applied in the fields of instruments, computer parts, special data processing applications, etc., can solve the problems of inaccurate selection of training samples and low accuracy, and achieve the effect of high stability and accurate diagnosis

Pending Publication Date: 2018-06-22
FOSHAN UNIVERSITY
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Problems solved by technology

[0004] Transformer fault detection methods based on random forest model have appeared in recent years, but these methods are not accurate in the selection of training samples, so the problem of low accuracy often occurs

Method used

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  • Transformer fault diagnosis method based on random forest
  • Transformer fault diagnosis method based on random forest

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Embodiment 1

[0011] Embodiment 1, a kind of transformer fault diagnosis method based on random forest, comprises:

[0012] Step 1: Collect the concentration data of hydrogen, methane, ethane, ethylene, and acetylene in insulating oil in the transformer and the corresponding fault categories as training samples and establish a sample set. Among them, the fault category is used as the classification label of the decision tree, and the fault categories include: high-energy breakdown, low-energy breakdown, overheating, and normal operation.

[0013] Step 2: Establish a fault decision tree according to the generation steps of the decision tree according to the training sample set;

[0014] Step 3: Synthesize the fault decision tree into a random forest model;

[0015] Step 4: collecting fault gas concentration data of unknown fault categories, and inputting it into the random forest model;

[0016] Step 5: Obtain the classification results obtained from all fault decision trees from the rando...

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Abstract

The invention discloses a transformer fault diagnosis method based on a random forest. The method comprises the following steps of collecting fault gas concentration data in insulating oil in a transformer and a corresponding fault type as a training sample; according to the training sample, based on the generation steps of a decision tree, establishing a fault decision tree; according to the fault decision tree, establishing a random forest model; and collecting the fault gas concentration data of a unknown fault type, inputting into the random forest model so as to acquire the fault type through the random forest model. The fault gas concentration data in the insulating oil in the transformer is taken as the training sample so as to establish the random forest model, a whole transformerfault can be accurately diagnosed, stability is high, and the method can be applied to the transformer diagnosis technology field.

Description

technical field [0001] The invention relates to the technical field of power transformer fault diagnosis, in particular to a random forest-based transformer fault diagnosis method. Background technique [0002] The power transformer is an important equipment in the power system. Due to the complex internal structure of the transformer, the distribution of the electric field is uneven, and with the increase of the voltage level, the accident rate is on the rise. As the main equipment of the power system, the failure of the transformer not only brings great losses to itself, but also has a great impact on the safety of the power system. [0003] At present, the technologies and methods of power transformer fault diagnosis mainly include expert system, artificial neural network, optimization technology, Petri network, fuzzy set theory, rough set theory and so on. [0004] Transformer fault detection methods based on random forest model have emerged in recent years, but these m...

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Application Information

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IPC IPC(8): G06K9/62G06F17/50
CPCG06F30/20G06F18/24323G06F18/214
Inventor 张彩霞王向东张文生文雪芹刘国文李斌
Owner FOSHAN UNIVERSITY
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