Equipment fault diagnosis method and system of electric power system

A technology for power equipment and equipment failure, which is applied in the field of fault diagnosis of power systems, can solve the problems of reduced diagnosis speed and accuracy, influence of diagnosis results, poor accuracy and versatility, and achieve the effect of improving reliability and stability

Inactive Publication Date: 2014-03-12
CHONGQING UNIV +1
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  • Application Information

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Problems solved by technology

However, the shortcomings of this method are: 1. The relationship between the observed symptoms and the corresponding diagnosis for the increasingly complex power system network is quite complicated. and poor versatility; 2. This method can only effectively detect the occurrence of faults, and has the function of predicting the occurrence of fault points, which belongs to the diagnosis after the fault
② When missing or wrong alarm information is a key signal, the diagnosis result will be affected
③When the power grid is complex and huge,

Method used

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  • Equipment fault diagnosis method and system of electric power system
  • Equipment fault diagnosis method and system of electric power system
  • Equipment fault diagnosis method and system of electric power system

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Embodiment 1

[0068] figure 1 It is a system flowchart of the power system equipment fault diagnosis method combined with infrared image segmentation, artificial neural network image classification algorithm and data fusion technology of the present invention, figure 2 2D Fuzzy Partition Maximum Entropy Segmentation Infrared Image Cell Model Diagram, image 3 It is a schematic diagram of the artificial neuron structure, where: x 1 ,x 2 ,x 3 ...x n Represents each component of the neuron input vector, that is, the shape feature of the power equipment image; ω 1 , ω 2 ... ω n Represents the weight of each input component; f is the activation function; y is the output of the neuron, that is, the structural image of a certain component of the power equipment, Figure 4 It is a phased data fusion model diagram, as shown in the figure: the power system equipment fault diagnosis method provided by the present invention includes the following steps:

[0069] S1: Acquiring the physical stru...

Embodiment 2

[0114] The difference between this embodiment and embodiment 1 is only:

[0115]The power system equipment fault diagnosis method combined with infrared image segmentation, artificial neural network image classification algorithm and data fusion technology provided in this embodiment first adopts two-dimensional fuzzy division and maximum entropy to segment the infrared image unit, and is responsible for the fault points of the collected power equipment. Infrared thermal image for noise reduction and target segmentation. Due to the characteristics of low contrast, high noise and fuzzy edges of the target in the infrared image, it is difficult to achieve accurate segmentation of the target. The two-dimensional maximum entropy segmentation method based on two-dimensional histogram is an effective segmentation method for noisy images because it not only reflects the gray distribution information, but also reflects the relevant information of the spatial neighborhood, and is very ...

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Abstract

The invention discloses an equipment fault diagnosis method of an electric power system. The method comprises the steps of segmenting an infrared image by utilizing a two-dimensional fuzzy-partition maximal entropy, and performing noise reduction and target segmentation on collected infrared thermogram of a fault point of electric power equipment; classifying and storing all collected image targets of the electric power equipment by utilizing an artificial neural network; finally comparing a separated fault image or a fault prediction point image with the image targets of the electric power equipment and carrying out fusion analyzing according to a stage fusion model, so as to obtain a diagnosis result; and performing the data fusion on conventional fault point empirical data of the electric power equipment and the detected fault point image data, thus obtaining a final fault result or predicted fault position of the electric power equipment. By integrating multiple algorithms, the precise diagnosis steps are formulated, and the modularization and systematic design is adopted, so that a complete and precision way for diagnosing the equipment fault of the electric power system is established, and the running reliability and stability of the electric power system can be improved.

Description

technical field [0001] The invention relates to the field of fault diagnosis of electric power systems, in particular to a method for fault diagnosis of electric power equipment. Background technique [0002] With the increasing scale of the modern power system and the continuous improvement of the voltage level, the damage caused by the failure of the transmission line to the social economy and people's life is more serious. Fast and accurate fault transient identification is the premise of fast restoration of grid power supply, and it is also an important part of fault analysis. Therefore, it is of great significance to study fast and reliable fault transient identification methods to ensure the safety and economy of power systems. [0003] For the research on fault diagnosis methods of power system equipment, predecessors have done a lot of useful exploration. For example, artificial intelligence technologies commonly used in the field of power system equipment fault di...

Claims

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Application Information

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IPC IPC(8): G01J5/00G01R31/00G06K9/62
Inventor 段其昌王洪授胡蓓陈红光毛明轩陈德林段盼黄晓刚
Owner CHONGQING UNIV
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