Automatic Detection Of Substation Electrical Equipment Failure Based On Image Segmentation

Nowadays, with the rapid development of Electricity Networks and rational networkstructure, the country requires increasingly high demanding for intelligent power system andthe reliability of power supply, such as: requiring automatic rapid isolation for electricalequipment failure and restore the power supply of the whole region; requiring the functions ofonline monitoring and safety warning for the electricity system, which would detect thedefects timely and take measures to eliminate hidden dangers. Infrared detection technologyhas been widely used as an effective means. The combination of infrared detection technologyand image monitoring system greatly improve the level of power system fault detection, butcurrent detection methods still require manual diagnosis, unable to achieve real-time andaccurate. To target on the problem of electrical substation equipment failure, the automaticdetection of electrical equipment failure in unmanned substation is achieved by using digitalimage processing technology.Firstly, the noises in the infrared images are summarized by analyzing the characteristicsof the infrared image of the substation, and experiments several common de-noising methods.On this basis, then proposed de-noising method with a high signal-to-noise ratio of thecombination of median and mean, not only can eliminate the diffusion effects of noise, butalso make the gray value within the image area more smoothly, completed the process ofimage preprocessing.Secondly, considering the characteristics of the low signal to noise ratio and low contrastfor the infrared images, on the basis of analysis of the traditional image segmentation methods,a new segmentation method which combined the Watershed transformation and K-meansclustering segmentation method is proposed and verified. The results show that this methodnot only be able to overcome the phenomenon of over-segmentation of the watershed method, but also to overcome the noise sensitivity of the clustering method, and it could extract thetarget region effectively and accurately.Finally, according to the characteristics of the substation infrared image pixel spatialinformation, Target detection method based on image gray level information and Targetdetection method based on pixel spatial information, which of electrical substation equipmentfailure are proposed. Experiments show that: The algorithm can not only detect the faultlocation of substation electrical equipment, but also make the experimental error between theactual value and the reference value in the2%negligible range, which could verify thefeasibility and accuracy of the proposed method. The combination of this method and thesystem of infrared remote viewing and inspection can be applied to unmanned substations, tomake the electrical equipment to develop from the traditional "corrective maintenance" and"preventive maintenance on a regular basis" approach to the direction of "the state ofmaintenance and predictive maintenance".