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Transformer fault detection method based on ant colony algorithm optimization random forest

A transformer fault and random forest technology, applied in transformer testing, instrumentation, calculation, etc., can solve complex and large data problems, achieve high classification accuracy, facilitate maintenance and management, and overcome insufficient classification accuracy.

Inactive Publication Date: 2019-01-04
DONGHUA UNIV
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Problems solved by technology

[0003] At present, there are many methods for fault diagnosis of power transformers at home and abroad, the traditional three-ratio method, Rogers method, etc., but these methods are too complicated and require a large amount of data

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  • Transformer fault detection method based on ant colony algorithm optimization random forest
  • Transformer fault detection method based on ant colony algorithm optimization random forest
  • Transformer fault detection method based on ant colony algorithm optimization random forest

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[0024] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these examples are only used to illustrate the present invention and not to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0025] The embodiment of the present invention relates to a transformer fault detection method based on ant colony algorithm optimization of random forest, including the following steps: firstly, discretizing the initial training sample and calculating the importance score of random forest features; secondly, using the importance score as heuristic information , and generate the heuristic distance, then initialize the parameters...

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Abstract

The invention relates to a transformer fault detection method based on an ant colony algorithm optimization random forest. The method comprises a step of discretizing an initial training sample and calculating a random forest feature importance score, a step of taking the importance score as heuristic information, generating a heuristic distance, and then initializing ant colony algorithm parameters including the node and node feature of each ant, a step of calculating transition probabilities of ants between nodes and constructing a solution space of a feature subset, taking random forest classification accuracy as an evaluation standard, a step of updating a pheromone and simultaneously selecting features to obtain an optimal or approximate optimal feature subset, and a step of satisfying a stop condition, outputting an optimal feature solution and carrying out fault diagnosis classification. According to the method, the improvement of the classification accuracy of a decision tree random forest is facilitated.

Description

technical field [0001] The invention relates to the technical field of transformer fault detection, in particular to a transformer fault detection method based on ant colony algorithm optimization random forest. Background technique [0002] With the increasing economic development, the power system plays an irreplaceable role, among which the power transformer is an important part of the power system. The presence or absence of transformer faults is directly related to the stable operation of the power system. [0003] At present, there are many methods for fault diagnosis of power transformers at home and abroad. The traditional three-ratio method, Rogers method, etc., but these methods are too complicated and require a lot of data. With the rise of artificial intelligence, some methods such as BP neural network, support vector machine, Bayesian network, and wavelet neural network have been proposed and applied to the power transformer fault diagnosis system, and have achi...

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Application Information

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IPC IPC(8): G01R31/02G06N3/00
CPCG01R31/62G06N3/006
Inventor 尤亚锋周武能
Owner DONGHUA UNIV
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