The breakdown voltage plays an important role in evaluating residual life of stator insulation in generator.In this paper,we discussed BP neural network that was used to predict the breakdown voltage of stator insulat...The breakdown voltage plays an important role in evaluating residual life of stator insulation in generator.In this paper,we discussed BP neural network that was used to predict the breakdown voltage of stator insulation in generator of 300MW/18kV.At first the neural network has been trained by the samples that include the varieties of dielectric loss factor tanδ,the partial discharge parameters and breakdown voltage.Then we tried to predict the breakdown voltage of samples and stator insulations subjected to multi-stress aging by the trained neural network.We found that it's feasible and accurate to predict the voltage.This method can be applied to predict breakdown voltage of other generators which have the same insulation structure and material.展开更多
Objective To investigate the characteristic parameters employed to describe the aging extent of stator insulation of large generator and study the aging laws. Methods Multi stress aging tests of model generator sta...Objective To investigate the characteristic parameters employed to describe the aging extent of stator insulation of large generator and study the aging laws. Methods Multi stress aging tests of model generator stator bar specimens were performed and PD measurements were conducted using digital PD detector with frequency range from 40?kHz to 400?kHz at different aging stage. Results From the test results of model specimens it was found that the skewness of phase resolved PD distribution might be taken as the characterization parameters for aging extent assessment of generator insulation. Furthermore, the measurement results of actual generator stator bars showed that the method based on statistical parameters of PD distributions are prospective for aging extent assessment and residual lifetime estimation of large generator insulation. Conclusion Statistical parameters of phase resolved PD distribution was proposed for aging extent assessment of large generator insulation.展开更多
基金This research was supported by the Key Technology R&D Programof State Power Corporation of China During the Tenth-Five-Year Plan Period.
文摘The breakdown voltage plays an important role in evaluating residual life of stator insulation in generator.In this paper,we discussed BP neural network that was used to predict the breakdown voltage of stator insulation in generator of 300MW/18kV.At first the neural network has been trained by the samples that include the varieties of dielectric loss factor tanδ,the partial discharge parameters and breakdown voltage.Then we tried to predict the breakdown voltage of samples and stator insulations subjected to multi-stress aging by the trained neural network.We found that it's feasible and accurate to predict the voltage.This method can be applied to predict breakdown voltage of other generators which have the same insulation structure and material.
基金ThisworkwassupportedbytheNationalNaturalScienceFoundationofChina (No .5 983 72 60 )
文摘Objective To investigate the characteristic parameters employed to describe the aging extent of stator insulation of large generator and study the aging laws. Methods Multi stress aging tests of model generator stator bar specimens were performed and PD measurements were conducted using digital PD detector with frequency range from 40?kHz to 400?kHz at different aging stage. Results From the test results of model specimens it was found that the skewness of phase resolved PD distribution might be taken as the characterization parameters for aging extent assessment of generator insulation. Furthermore, the measurement results of actual generator stator bars showed that the method based on statistical parameters of PD distributions are prospective for aging extent assessment and residual lifetime estimation of large generator insulation. Conclusion Statistical parameters of phase resolved PD distribution was proposed for aging extent assessment of large generator insulation.
