Wavelet method optimised by ant colony algorithm used for extracting stable and unstable signals in intelligent substations

Partial discharge(PD)signals are an important index to evaluate the operation state of intelligent substations.The correct distinction of PD pulse and interference pulse has become a challenging task.Because of the noise and the low signal-to-noise ratio,the stable signals become non-stationary.The selection of a wavelet basis,the selection rule of thresholdλand the design of the threshold function are the key factors affecting the final denoising effect.Therefore,an enhanced ant colony optimisition wavelet(ACOW)algorithm was applied to find the global optimal threshold through the continuous derivative threshold function and the ant colony optimisation(ACO)algo-rithm.At the same time the efficiency of adaptive search calculation,was also significantly improved.The method of the ACOW algorithm was compared with the soft wavelet method,gradient-based wavelet method and the genetic optimisation wavelet(GOW)method.Using these four methods to denoise four typical signals,different mean square errors(MSE),magnitude errors(ME)and time costs were obtained.Interestingly,the results show that the ACOW method can achieve the minimum MSE and has less time cost.It generates significantly smaller waveform distortion than the other three threshold estimation methods.In addition,the high efficiency and good quality of the output signals are beneficial to the diagnosis of local discharge signals in intelligent substations.

Feature extraction of partial discharge in low-temperature composite insulation based on VMD-MSE-IF

Low-temperature composite insulation is commonly applied in high-temperature super-conducting apparatus while partial discharge(PD)is found to be an important indicator to reveal insulation statues.In order to extract feature parameters of PD signals more effectively,a method combined variational mode decomposition with multi-scale entropy and image feature is proposed.Based on the simulated test platform,original and noisy signals of three typical PD defects were obtained and decomposed.Accordingly,relative moments and grayscale co-occurrence matrix were employed for feature extraction by K-modal component diagram.Afterwards,new PD feature vectors were obtained by dimension reduction.Finally,effectiveness of different feature extraction methods was evaluated by pattern recognition based on support vector machine and K-nearest neighbour.Result shows that the proposed feature extraction method has a higher recognition rate by comparison and is robust in processing noisy signals.

Novel dc flashover model for predicting flashover voltage of the iced insulators

This work first proposes a novel flashover model based on the deduced residual resis-tance of the ice layer. The most important finding of the field icing tests is that the double‐layer ice‐melting water film exists on the inner and outer surfaces of the ice layer. The conductivity of melting water on the inner and outer surface of the ice layer has been measured, and the corresponding surface conductivity has been calculated. According to the proposed flashover model, the ratio η of the resistance of the ice layer to that of the pollution layer gradually increases with the increase of the ratio k of the resistance of the outer surfaces to that of inner surfaces, and eventually approaches saturation. Further-more, the icing flashover criterion has been modified, and the voltage gradient of the inner surface is less than that of the outer surface. Test results show that the ice flashover voltage is lower than the pollution flashover voltage between 2.18-9.69% and 14.75- 20.32% under light ice and semi‐cylindrical ice, respectively. The simulation results based on the proposed model and the classic model are compared with the field test data. Results show that the proposed model could attain a high accuracy of dc flashover voltage and demonstrate the effectiveness of the proposed model.