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A pcnn power fault area detection method based on local features

A local feature, power failure technology, applied in the electrical field, can solve problems such as electrical equipment damage personnel, electrical system accidents, omission fault system detection and maintenance, etc.

Active Publication Date: 2019-10-25
WUHAN NARI LIABILITY OF STATE GRID ELECTRIC POWER RES INST +3
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

When the above model is processing the infrared fault image, it is affected by the image of parameters and threshold settings, so that the segmented image cannot completely extract the power fault area, thus neglecting the detection and maintenance of the fault system, which leads to electrical system accidents , causing electrical equipment damage and personal injury

Method used

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  • A pcnn power fault area detection method based on local features
  • A pcnn power fault area detection method based on local features
  • A pcnn power fault area detection method based on local features

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Embodiment Construction

[0063] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0064] The PCNN power fault area detection method based on local features of the present invention optimizes the model parameters according to the synchronous ignition characteristics of the PCNN model itself. In particular, the optimal setting of the dynamic threshold and connection coefficient is based on the high Brightness and similarity characteristics, combined with some characteristics between the faulty area and the non-faulty area, proposed and adopted the local characteristics of the neighborhood change, and then set the stopping rule for the iterati...

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Abstract

The invention relates to a PCNN power fault area detection method based on local features. The method is based on the pulse-coupled neural network (PCNN) synchronous ignition mechanism, and by appropriately simplifying its internal parameters, the fault area and the fault area are combined under parameter optimization configuration. The local characteristics of the neighborhood boundary of the non-fault area, and the PCNN model iteration end rule are set, so that the model can adaptively iterate to obtain the fault area of ​​the infrared image. The experimental results show that, for the actual infrared detection image, the method of the present invention can automatically and efficiently identify electrical equipment The fault area has better fault area detection performance.

Description

technical field [0001] The invention belongs to the electrical field, and in particular relates to a PCNN power fault area detection method based on local features. Background technique [0002] Infrared thermal imaging has many advantages such as non-contact, long-distance, passive detection, etc., and has become an important means for the power sector to implement fault status detection of electrical equipment. However, infrared fault detection mainly relies on regular inspections by operation and maintenance personnel. For the staff, infrared detection of equipment requires a lot of time, and has disadvantages such as low efficiency, easy missed detection, and relatively high management costs. For this reason, automatic infrared detection has been highly valued by the majority of staff, such as Xu Xuetao [1] An improved image segmentation method of the PCNN (Pulse-coupled neural network) model is proposed to extract the fault area in the infrared image. However, due to t...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06T7/11G06T7/136G06K9/62G06Q50/06
CPCG06Q50/06G06T7/0002G06T7/11G06T7/136G06T2207/10048G06T2207/20084G06F18/23
Inventor 谷凯凯程林许晓路蔡炜周正钦倪辉徐进霞周东国赵坤黄华傅晨钊胡正勇
Owner WUHAN NARI LIABILITY OF STATE GRID ELECTRIC POWER RES INST
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