Research On Plant Power System Analysis And Model Based On Big Data

China's coal-based energy consumption structure is difficult to change in a short period of time.The in-depth energy-saving of coal-fired generating units is of great significance to China's energy-saving and emission-reduction strategies.The plant electricity rate is one of the important economic indicators of the unit.The traditional plant power optimization focuses on the equipment transformation.With the advent of the era of big data,this paper discusses the optimization perspectives and methods for individual subsystems and equipment based on data analysis of the entire plant power system.Including:Firstly,the composition of the plant electricity is introduced.The power consumption factors of the power systems and equipment of the plant are obtained through theoretical and historical operational data analysis.Secondly,data modeling of the plant power system is performed,and then the influence of the main influencing factors on the power consumption of the equipment is analyzed.The R-test and ECNN method were used to select the reasonable steady-state refining samples.After setting up the LSSVM model,single factors were adjusted to change the factors,and the current change trend was obtained.Establish a mathematical model for the optimization of the grinding system of the milling system.According to the unit load curve obtained by the short-term load forecasting,consider the limit conditions of the coal mill start-stop number and a series of constraints,and use the improved particle swarm optimization algorithm to start and stop the grinding in the next few hours.Plan and contribute to the allocation plan,make reasonable arrangements for the start and stop time of the mill and distribute the output of the coal mill to provide reference for the operating personnel.The research on the adjustment of the primary air volume and the loading hydraulic pressure parameter reference-value of the coal mill is carried out.In order to consider the influence of these parameters on the combustion of the furnace,a soft-sensing model of fineness of pulverized coal is established based on a method of least squares support vector machine to obtain an on-line estimated value of pulverized coal fineness.After defining a number of subsystem boundary conditions,a multivariable synchronous clustering of the primary air volume and the loaded oil pressure is performed under the typical output of the coal mill,which aims at excavating the corresponding loaded oil pressure and primary air volume under different output and inlet conditions.The loaded oil pressure and primary air volume reference value is to reduce the coal consumption per unit of coal as far as possible under the premise of satisfying the combustion of the furnace.Finally,based on the.NET platform,the power generation system analysis and optimization software for coal-fired unit plants was developed.The basic framework of the module system is given,the platform functions are introduced,and the screen effects are displayed.