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Transformer oil paper insulation aging prediction method based on chicken swarm optimization BP neural network

A BP neural network, transformer oil technology, applied in biological neural network model, neural architecture, design optimization/simulation, etc., can solve the polarization and depolarization current change, the extended Debye model parameters cannot correctly reflect the aging state of oil-paper insulation, etc. problem to achieve high accuracy

Active Publication Date: 2020-03-17
JILIN ELECTRIC POWER RES INST +2
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Problems solved by technology

[0011] The purpose of the present invention is to provide a transformer oil-paper insulation aging prediction method based on chicken flock optimized BP neural network to solve the problem that when the ambient temperature changes, the polarization and depolarization currents change, resulting in the extended Debye model parameters not correctly reflecting the oil-paper insulation aging state problem

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  • Transformer oil paper insulation aging prediction method based on chicken swarm optimization BP neural network
  • Transformer oil paper insulation aging prediction method based on chicken swarm optimization BP neural network
  • Transformer oil paper insulation aging prediction method based on chicken swarm optimization BP neural network

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Embodiment Construction

[0029] In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more embodiments. It may be evident, however, that these embodiments may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing one or more embodiments.

[0030] Glossary

[0031] Chicken flock algorithm: a bionic intelligent algorithm that simulates the foraging behavior of chicken flocks in the biological world. In this algorithm, chicken flocks are divided into three types of individuals according to their foraging abilities, namely roosters, hens, and chicks. The foraging ability of these three types of individuals was the strongest in roosters, followed by hens, and the weakest in chicks.

[0032] BP neural network: Its model is a multi-layer feed-forward artificial neural network using backpropagation al...

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Abstract

The invention provides a transformer oiled paper insulation aging prediction method based on a chicken swarm optimization BP neural network, and the method comprises the steps: firstly training the BPneural network to fit the relation between a depolarization current and a polymerization degree, eliminating an error caused by an environment temperature, and achieving the prediction of oiled paperinsulation aging at different environment temperatures; and then, optimizing the weight and threshold of the neural network by adopting a chicken swarm algorithm, and solving the problems of low learning efficiency, low speed and easiness in falling into a local extreme point during BP neural network training; and finally, carrying out the simulation research, and the result shows that the methodcan eliminate the impact on the polarization and depolarization current from the temperature, and achieves the accurate prediction of the insulation aging of the oil paper.

Description

technical field [0001] The invention belongs to the technical field of detecting the aging state of transformer oil-paper insulation, and in particular relates to a method for predicting the aging of transformer oil-paper insulation based on a flock-optimized BP neural network. Background technique [0002] The transformer in the power system bears the important responsibility of energy transmission and voltage conversion, and its state directly affects the safe operation of the power system. Once a fault occurs, the resulting loss will be huge. When the transformer is in normal operation, it will be affected by electricity, heat, machinery and external interference, resulting in insulation aging. The aging of transformer insulation is one of the important reasons for transformer failure. The aging of transformer insulation can be divided into two parts: solid insulation aging and liquid insulation aging. The aging of liquid insulation is mainly the aging of insulating oil....

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Application Information

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IPC IPC(8): G06F30/20G06N3/00G06N3/04G06F119/04
CPCG06N3/006G06N3/044G06N3/045Y04S10/50
Inventor 赵春明何秋月杨代勇张雷于群英史加奇王昕许文燮刘赫孙友群杨明
Owner JILIN ELECTRIC POWER RES INST
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