Research On Application Technology Of Big Data Based On Characteristics Of Thermal Equipment In Thermal Power Plant

Power plants have numerous thermal equipment,complex systems,and complex working conditions,which have huge-scale operating data.During the operation process,the overall system and various equipment are closely connected.How to analyze and process these data reasonably is to further promote the digital and intelligent development of the power plant.In recent years,big data technology has developed vigorously and has been applied to many fields including the power industry.With its advantages of high response speed and high processing speed,big data technology facilitates the analysis of operating data of thermal equipment in power plants.Researchers at home and abroad have conducted some explorations and experiments on the application of big data technology in power plants.They used big data technology to explore functions such as data modeling and analysis,parameter prediction,fault diagnosis,and status evaluation.Many power plants have deployed intelligent operation platform construction.But as a whole,the research on big data of thermal equipment in thermal power plants is still in its infancy,and related application research is relatively scattered.Many studies focus more on the algorithm level,ignoring the physical principles and logic of thermal equipment itself.In the big data technology,there is insufficient research on the relevance of thermal equipment,and lack of theoretical and technical research based on the characteristics of thermal equipment.In order to fully consider the impact of the characteristics of thermal power plants on the application of big data technology and improve the adaptability of the application of big data technology,this paper collected 2018 and 2019 operating data from a 660MW coal-fired unit in a power plant.Based on the operating principle of thermal equipment,through feature analysis,theoretical analysis,data analysis,big data analysis and other methods,this paper analyzes the data characteristics of thermal power plant thermal equipment,and performs data cleaning based on data characteristics;analyzes the operating characteristics of thermal equipment,performs steady-state detection and time lag analysis on operating data;studies the partitioning method of operating conditions and the establishment of related models;and uses the power plant as an example to conduct a research on the application of big data technology in the desulfurization system.By summarizing the characteristics of the thermal equipment data,this article carries out an adaptive data cleaning method for the missing values,outliers,outliers and other characteristics of the data;summarizes the operating characteristics of the thermal equipment,and uses the sliding window method for steady state detection.These can be used for big data analysis of thermal equipment and thermal systems.This paper proposes a partitioning method for all working conditions under the influence factors of multiple working conditions based on the Cartesian product operation form.The big data modeling method can be used to establish a big data model separately in each sub-interval,which improves the accuracy of big data modeling.Sex.This paper systematically analyzes the operating state delay characteristics of the main equipment of thermal power plants from the mechanism level,and obtains a method to judge the delay time by using the flow of working fluid,data changes,and correlation.This paper takes the power plant desulfurization system as an example to carry out a research on the application of big data technology,and systematically study the establishment of indicators,analysis of influencing factors,time lag analysis,zoning of all working conditions,index optimization model and model self-updating in the process of big data application.This case verifies the technical route of the application of big data technology in thermal power plants.These studies have important theoretical significance and engineering application value for the optimization operation research of thermal power plant thermal equipment using big data technology.