Big Data Analysis And Application Based On The Data Collection Of Existing Wind Power Remote System

With the transformation and adjustment of energy structure,wind power has become one of the main power generation.Monitoring and assessing the units' safety and economic performance is crucial to wind farms,since most of the wind farms are located in remote areas.Now the large data is driving a new round of technological innovation,the key technology has been developed to all industrials,also for wind power industry.In this paper,through the statistical analysis of the fault data of large-scale wind field comes out with the failure modes and distribution of the wind power generation's main components.On this basis,analyzed the failure mode,mechanism,reason and diagnosis method of doubly fed wind power generator(DFIG).By using fault tree analysis found out the failure mode of main components of wind power,and the technical support for the operation and maintenance of the wind power equipments.Finally,analyzed the vibration signals characteristics of the main components of the double-fed wind turbine,and the method for extracting characteristic information of fault gear and bearing.This paper takes the domestic wind power company as the object of study collects and analyzes the fault data of the wind farm equipment,obtains the main data of the wind turbine,the component failure rate and the downtime data distribution,and the component failure modes.To provide reference data and technical support for efficient operation and maintenance of wind turbine.At the same time,analyze the domestic wind farm common parts over-temperature alarm failure.As the wind power generation system is more complex,resulting in overheating failure for many reasons,the operation of the failure mode and the impact of different.Therefore,the analysis of the over temperature fault mode and its influence on the whole unit has important practical significance to improve the availability and economy of the wind turbines equipment.