Chaos Existence in Surface Discharge of Tracking Test

Tracking tests for different polymer materials were carried out to investigate the chaotic behavior of surface discharge. The discharge sequences measured during the discharge process were analyzed for finding the evidence of chaos existence. Four kinds of nonlinear analysis methods were adopted： estimating the largest Lyapunov exponent, calculating the fractal dimension with increasing the embedding dimension, drawing the recurrence plots, and plotting the Poincare maps. It is found that the largest Lyapunov exponent of the discharge is positive, and the plot of fractal dimension, as a function of embedding dimension, will saturate at a value. The recur- rence plots show the chaotic frame-work patterns, and the Poincar6 maps also have the chaotic characteristics. The results indicate that the chaotic behavior does exist in the discharge currents of the tracking test.

Chaos Analysis of Discharge Current Based on Tracking Test of Phenolic Resin

In tracking test, discharge is a complicated process and comparative tracking index (CTI) has wide varia-tion. To evaluate tracking resistance, the chaos analysis of discharge current is presented based on the tracking test of phenolic resin in accordance with IEC60112. According to the characteristics of statistical self-similarity and complexity of discharge current, the largest Lyapunov exponent is calculated, and the 2-dimensional attractor of discharge current is reconstructed. Moreover, the attractors of discharge current and recurrence plots of different discharge states are reconstructed. The results indicate that the chaos attractors have different characteristics in evo-lutionary tracks, the topological structure and grain direction of recurrence plots show significant differences. The chaos attractor can describe the tracking process, the recurrence plot can identify the tracking state clearly, while its arithmetic is simple.

Damage identification technology of substation instrument panel for solving imbalanced classification

Edge computing plays an active role in empowering the power industry as a key technology for establishing data-driven Internet of things(Io T) applications.Traditional defect diagnosis mainly relies on regular inspection of equipment by operation and maintenance personnel at all levels,and its accuracy relies on the human experience.In actual production,the image data of some dashboard damage types are easy to collect in large quantities,while some dashboard damage types occur less frequently and are more difficult to collect.The use of edge computing nodes allows flexible and fast collection of smart meter data and transmission of the reduced data or results to a cloud computing center.In this study,we provide a fresh balanced training approach to address the issue of learning from unbalanced data.In the equilibrium training phase,a new impact balance loss is introduced to reduce the influence of samples on the overfitting decision boundary.Experimental results show that the proposed balance loss function effectively improves the performance of various types of imbalance learning methods.

A fault locating method for multi-branch hybrid transmission lines in wind farm based on redundancy parameter estimation

In order to solve the problem of‘‘abandoned’’wind caused by short circuit faults in a wind farm,a wind farm fault locating method based on redundancy parameter estimation is proposed.Using the characteristics of the traveling wave,transmission equations containing the position of the fault point are constructed.Parameter estimation from statistical theory is used to solve the redundant transmission equations formed by multiple measuring points to locate the faults.In addition,the bad data error detection capability of the parameter estimation is used to determine bad data and remove them.This improves locating accuracy.A length coefficient is introduced to solve the error enlargement problem caused by a transmission line sag.The proposed fault locating method can solve the fault branch misjudgment problem caused by the short-circuit faults near the data measuring nodes of thewind farm based on the proposed fault interval criterion.It also avoids the requirements to the traveling wave speed of traditional methods,thus its fault location is more accurate.Its effectiveness is verified through simulations in PSCAD/EMTDC,and the results show that it can be used in thefault locating of hybrid transmission lines.