Online Monitoring Method Based on Locus-analysis for High-voltage Cable Faults

Online diagnosis methods for high-voltage(HV)cable faults have been extensively studied.Power cable fault monitoring is not accurate,and the degree of data fusion analysis of current methods is insufficient.To address these problems,an online monitoring method based on the locus-analysis for HV cable faults is developed.By simultaneously measuring two circulating currents in a coaxial cable,a two-dimensional locus diagram is drawn.The fault criteria and database are established to detect the fault by analyzing the changes of the locus characteristic parameters.A cross-connected grounding simulation model of a high-voltage cable is developed,and several faults at different locations are simulated.Fault identification is established based on the changes in the long axis,short axis,eccentricity,and tilt angle of the track.The simulation and field experiments show that this method can provide an increased amount of fault-monitoring reference information,which can improve the monitoring accuracy and provide a new approach to cable fault monitoring.

Electrical Tree Image Segmentation Using Hybrid Multi Scale Line Tracking Algorithm

Electrical trees are an aging mechanismmost associated with partial discharge(PD)activities in crosslinked polyethylene(XLPE)insulation of high-voltage(HV)cables.Characterization of electrical tree structures gained considerable attention from researchers since a deep understanding of the tree morphology is required to develop new insulation material.Two-dimensional(2D)optical microscopy is primarily used to examine tree structures and propagation shapes with image segmentation methods.However,since electrical trees can emerge in different shapes such as bush-type or branch-type,treeing images are complicated to segment due to manifestation of convoluted tree branches,leading to a high misclassification rate during segmentation.Therefore,this study proposed a new method for segmenting 2D electrical tree images based on the multi-scale line tracking algorithm(MSLTA)by integrating batch processing method.The proposed method,h-MSLTA aims to provide accurate segmentation of electrical tree images obtained over a period of tree propagation observation under optical microscopy.The initial phase involves XLPE sample preparation and treeing image acquisition under real-time microscopy observation.The treeing images are then sampled and binarized in pre-processing.In the next phase,segmentation of tree structures is performed using the h-MSLTA by utilizing batch processing in multiple instances of treeing duration.Finally,the comparative investigation has been conducted using standard performance assessment metrics,including accuracy,sensitivity,specificity,Dice coefficient and Matthew’s correlation coefficient(MCC).Based on segmentation performance evaluation against several established segmentation methods,h-MSLTA achieved better results of 95.43%accuracy,97.28%specificity,69.43%sensitivity rate with 23.38%and 24.16%average improvement in Dice coefficient and MCC score respectively over the original algorithm.In addition,h-MSLTA produced accurate measurement results of global tree parameters of length and width in comparison with the ground truth image.These results indicated that the proposed method had a solid performance in terms of segmenting electrical tree branches in 2D treeing images compared to other established techniques.