Direct current master device fault diagnosis method based on hybrid neural network

A neural network and fault diagnosis technology, applied in electrical testing/monitoring, information technology support systems, etc., can solve problems such as poor consistency, differences in the fault development mechanism and rules of DC equipment and AC equipment, and diagnostic criteria that cannot be simply borrowed. To achieve the effect of improving reliability and improving maintenance efficiency

Active Publication Date: 2014-12-24
EXAMING & EXPERIMENTAL CENT OF ULTRAHIGH VOLTAGE POWER TRANSMISSION COMPANY CHINA SOUTHEN POWER GRID +1
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Problems solved by technology

[0003] At present, most of the state detection and fault diagnosis technologies of DC systems directly and simply borrow some technical means from existing AC equipment. The consistency b

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  • Direct current master device fault diagnosis method based on hybrid neural network
  • Direct current master device fault diagnosis method based on hybrid neural network
  • Direct current master device fault diagnosis method based on hybrid neural network

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Embodiment

[0042] The present invention proposes a hybrid neural network DC main equipment fault detection method, and its implementation process can refer to figure 1 shown, including steps:

[0043] S1. Obtain the data required for fault diagnosis of the DC main equipment and preprocess the data;

[0044] Among them, the data required for the fault diagnosis of the DC main equipment includes various historical data and the status classification corresponding to the data and the latest data.

[0045] S2. Apply neural network for information fusion;

[0046] Select the appropriate neural network model according to the characteristics of the source data, and then use a certain learning method according to the existing multi-source information and system fusion knowledge to conduct offline learning on the established neural network to determine the connection weight and structure, and finally the obtained network used in data fusion.

[0047] S3, implementation of fault diagnosis method...

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Abstract

The invention discloses a direct current master device fault diagnosis method based on a hybrid neural network. The method includes the following steps that firstly, associated data needed for device fault diagnosis are acquired, wherein the associated data comprise source data and real-time data, and the source data include offline experimental data, dot experimental data, online monitoring data and historical data composed of various polling data; secondly, information fusion is conducted on the associated data through a neural network; thirdly, a particle swarm optimization algorithm, a Hopfield network and a BP network are combined, the hybrid neutral network is designed, the associated data obtained after information fusion in the second step are predicted, and then the prediction state of a direct current master device is acquired; fourthly, the prediction state corresponds to the original state of the direct current master device and is shown in different modes or/and forms, wherein the original state is the historical state shown by the source data. By means of the method, the overhaul efficiency of the fault device and the running reliability of a power grid are improved.

Description

technical field [0001] The invention relates to the technical field of power system fault diagnosis, in particular to a hybrid neural network-based fault diagnosis method for DC main equipment. Background technique [0002] With the expansion of the grid scale, the construction and development of the power system, the number of power equipment is increasing, and various new equipment is put into operation, the reliability of the DC main equipment has become the guarantee for the safe operation of the power system. Research on fault diagnosis technology is particularly important. [0003] At present, most of the state detection and fault diagnosis technologies of DC systems directly and simply borrow some technical means from existing AC equipment. The consistency between them is poor, especially the diagnostic criteria cannot be simply borrowed, so problems such as DC equipment status detection and fault diagnosis are very prominent in these aspects. [0004] In addition, ...

Claims

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Application Information

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IPC IPC(8): G05B23/02
CPCY04S10/52
Inventor 邓军王奇常安张晗宋云海陈新耿大庆冮杰张武英
Owner EXAMING & EXPERIMENTAL CENT OF ULTRAHIGH VOLTAGE POWER TRANSMISSION COMPANY CHINA SOUTHEN POWER GRID
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