Research On Task Scheduling Algorithm In Cloud Platform Of Power Transmission And Substation Equipment Monitoring

With the development of smart grid construction, the power industry enters the "era of big data”. The demand of large data processing has far beyond other basic energy industry. In order to provide a panoramic view of real-time information of network operation, the amount of data transmission and substation equipment monitoring will surge, especially in extreme weather. So, the problem has to be able to solve that receiving and analyzing data quickly when the transmission and substation equipments spurt alarm.Transmission and substation equipment to monitor cloud platform require timely response spurt high concurrent alarms in bad weather conditions, the data grid to cross the line alarm rapid diagnosis for timely troubleshooting, to avoid accidents. In order to ensure efficient and orderly operation of cloud platforms, scientific and rational task scheduling algorithm is essential.This paper describes the research status of power transmission and substation equipments monitoring and real-time processing technology. It selects the Storm in current mainstream real-time processing platform as a processing framework from the transmission and distribution equipment monitoring requirements. Then, it detailed description of the basic concepts of Storm framework and related technologies, demonstrated the feasibility of the introduction of transmission and distribution equipment Storm monitoring platform.In this work, it proposed fair share scheduling algorithm and adaptive scheduling algorithm based on the characteristics Storm frame and power transmission and substation equipment monitoring tasks, and it written in scheduler interface provided by the framework and according to Storm. Through experiments, the proposed algorithm is validated. Through experimental results show that the proposed algorithm is compared with the Storm default algorithm has significant advantages, shorten processing delay data, the validity and effectiveness of the algorithm.