Research On Reliability Evaluation And Prediction Methods Of Catenary System Considering Geographical And Meteorological Information

Catenary system is a key part of traction power supply system.It is the only source power for electric locomotives.Its safety is directly related to the safety operation of high-speed rail,and the society pays more and more attention to it.The catenary system is always exposed to the atmospheric environment.Long-term operation experience and field statistics show that the failure of catenary caused by the external weather environment is one of the main factors affecting the safety of the high-speed rail.In recent years,the global climate change is more obvious,the intensity and frequency of adverse weather and meteorological disasters are increasing.Therefore,how to know the relationship between the weather conditions and the reliability of the catenary more accurately has become an urgent problem to be solved.Considering the impact of weather conditions on the reliability evaluation of catenary and predicting the reliability of catenary based on meteorological conditions is becoming a research hotspot in traction power supply field.At present,some scholars have studied the reliability evaluation and prediction of the catenary.But there are still the following problems: Firstly,there is no relevant literature to study the fault characteristics of the catenary and the relationship with the meteorology;Secondly,the influence of weather condition is not considered in the reliability evaluation of catenary.However,the reliability of the system is quite different under different weather conditions.The mean value of the reliability of the system is difficult to reflect the actual level of different weather conditions.Thirdly,most of the current reliability prediction models for catenary are based on hypothetical conditions,which are different from the field service environment.The prediction model can not accurately represent the physical characteristics of the system.Therefore,based on the field statistical data,the paper analyzes the fault characteristics of the catenary and its relationship with the meteorological environment.Study the reliability evaluation method of the catenary considering the influence of weather condition based on the relationship between catenary reliability and weather condition.According to the time distribution characteristics of meteorological conditions and catenary trips,proposed a prediction method of catenary trips based on the time period meteorological correlation model.In view of the problem of the correlation between fault and meteorological grade,proposed a real-time fault prediction method of the catenary using the meteorological grade.The main work is as follows:Proposed a reliability assessment method of catenary considering the influence of weather condition.Firstly,build the weather condition and uncertainty modeled based on the three-state weather model and credibility theory,and derived the failure rate and repair rate of components under different weather conditions.Secondly,used the GO method to establish the equivalent model of the catenary system failure,and deduced the reliability index of the catenary system under different weather conditions and given calculation process.Finally,design example according to statistical data of Bao-Cheng railway,the calculation results show that there is a big difference between the reliability index of the catenary in different weather conditions,and the mean value can not be used to characterize the reliability of the catenary under various weather conditions.Proposed a prediction method of the number of trip times for catenary based on the time-segment meteorological association model.Firstly,analyzed the relationship between catenary trips times and meteorological conditions based on the time distribution of catenary trip times related weathe and weather conditions.According to the time distribution meteorological data,proposed a meteorological correlation model of catenary trip times based on time period selection.Secondly,according to the meteorological department's long-term weather forecast data,obtained the trip times of catenary in the coming month.Finally,verified the validity of the proposed method by the data of the trip record and the meteorological statistics provided by a power supply section.Proposed fault prediction of catenary based on meteorological correlation.According to the statistical data,excavated the relationship between the fault and the weather conditions.Standardized meteorological data into mathematical representation data and set up training data set.Proposed a method of fault prediction model by using meteorological information,using weather forecast data to realize on-line real-time fault prediction by constructing AdaBoost classifier.The validity of the proposed method is verified by an example of a historical fault data sample of a railway bureau.The theoretical study and the analysis of the results of the paper show that accurately cognizing the fault characteristics of the catenary and the relationship with the meteorology is of great significance to understand the external influences and the space-time distribution of faults and provides statistical theory support for the reliability evaluation and prediction of the catenary.The model considering the influence of weather conditions can reflect the actual operating environment accurately,and the results are closer to the reality.the prediction method of trip times of the catenary based on time-segment meteorological association model that proposed according to the time distribution characteristics of the weather-related trip times of the catenary,can realize middle and long-term prediction of catenary trip times.The proposed method of using weather conditions to establish acatenary fault prediction model can realize real-time prediction and early warning of catenary fault.The research in this paper can help railway operators understand the operating state of the catenary better and perceive the reliability level of the catenary ahead of time.