Research On Detection And Diagnosis Of Power Equipment Based On Digital Image Recognition

With the concept of a global energy Internet,the modern power industry is moving toward a new energy model.Nowadays,China has basically achieved comprehensive coverage of the power grid.In the power grid,many faults are caused by power equipment failures.Therefore,it is Significant to prevent,detect and eliminate faults,ensure that the grid can be safely and stably operated and accurately and reliably maintained.Infrared thermal imaging technology is an effective non-destructive fault detection technology.It has been widely used in the monitoring and diagnosis of equipment due to its strong adaptability,non-contact and good economic performance.Through infrared image processing and temperature monitoring,it is possible to detect hidden dangers of the device and quickly locate and diagnose the fault point.The purpose of this paper is to reduce the reliance of inspection methods on employee experience.In order to make the diagnosis more intelligent,a new device is constructed.This facilitates the transition of power detection from planned maintenance to state maintenance.Firstly,the article describes the basic theory of infrared and summarizes the related theorems and formulas of infrared imaging.Secondly,for the characteristic of noise in the infrared image of the device,the acquired image is grayed out and histogram equalized.Aiming at the shortcomings of several common filtering algorithms in different noises,this paper chooses wavelet semi-soft threshold filtering method.This method can effectively suppress noise and improve image quality.Thirdly,the paper introduces several common infrared image edge detection operators and Otsu algorithm,and proposes an improved PCNN algorithm to solve the problem of low contrast and low signal to noise ratio.It shows that the improved PCNN algorithm works better through comparative analysis.In addition,the selection of the device infrared image feature extraction and recognition method directly affects the accuracy of the detection diagnosis.Therefore,thispaper discusses Hu invariant moments and Zernike invariant moments to solve image rotation,scaling,and translation that may occur when acquiring an image.Zernike invariant moments can construct arbitrary moments,so it is easy to perform statistical classification and make accurate judgments.In order to make the feature have better invariance,this paper improves the Zernike invariant moment,and proves that this method has better adaptability and recognition rate through experiments.Finally,based on the above algorithm research results,this paper gives the functional structure of the system in combination with the equipment fault level and diagnostic rules.In this paper,surface temperature method and relative temperature difference method are selected as fault diagnosis methods.A device feature extraction and monitoring and diagnosis system based on infrared image processing is designed and implemented.With the development of sensors,computers,network communications and other technologies,advanced instruments can exchange data information in real time through the network and establish a corresponding database.In the aspect of image collection,processing and storage,the intelligent analysis of data by using the database not only helps to reduce the traditional maintenance cost and improve the industrial efficiency,but also provides a guarantee for the long-term and efficient operation of the power system.