Design And Development Of Power Failure Automatic Analysis System Based On Big Data

With the continuous development of China's industrial process,the society's demand for electricity is growing.Under the influence of new energy grid-connected smart grid,the stability and security of power grid system are particularly affected by the unstable characteristics of new energy.In view of the protection of the safe and stable operation of power,it is very necessary to monitor the information of power equipment,especially for the unpredictable power failure analysis is the key to ensure the safety of power consumption.However,due to the variety and quantity of power equipment,and the frequent fluctuation of monitoring data affected by natural environment,the amount of data of equipment information will increase rapidly in a short time,which is beyond the ability of ordinary computer to process.Therefore,it is feasible to use large data technology to store and accurately analyze and predict the operation of power equipment,and to make an effective auxiliary judgment of power equipment fault.At present,the monitoring of power equipment status is mainly based on the state information management of power equipment based on large data.Distributed system is used to store the state information of power equipment,and cluster analysis algorithm is used to analyze the stability of power equipment operation status.Although this method provides technical support for power fault analysis to a certain extent,it lacks certain universality because of the different performance of power equipment in different environments.In this paper,starting from the dynamic analysis of power equipment operation state,an automatic power failure analysis system with large data is constructed based on Hadoop power equipment status information system data.The purpose of the research is to set the power failure limit automatically for each power equipment under different environment conditions and ensure different equipment.It has its own independent performance index parameters to improve the accuracy of power fault diagnosis,especially to solve the problem of unpredictable power fault analysis.Firstly,the methods of power fault analysis at home and abroad are summarized by literature research.The accuracy,efficiency and intelligence of power fault analysis methods are analyzed emphatically.The key technologies involved in automatic power fault analysis system with large data are introduced,including Hadoop technology and Hadoop-based power equipment.Prepare status information system,Java performance.tracking tool and recursive random search algorithm.Secondly,the demand of automatic power fault analysis system is analyzed,and the goal of constructing automatic power fault analysis system is discussed from three aspects of function,performance and feasibility.Finally,the design of automatic power fault analysis system is carried out.Modularized automatic power fault analysis system is used to design and divide the power fault index capture module,analysis and prediction module and automatic optimization module.Parameters are optimized and the recursive random search algorithm is used to solve the problem of flexible setting of parameter limits for different power equipment operation indicators.Through the realization of the system and the test of the system,it is proved that the design and development of the automatic power fault analysis system based on large data has better flexibility in the fault diagnosis of power equipment,and can set individualized fault limits for power equipment which is not feasible,thus improving the effectiveness of power fault analysis.Sex and accuracy.