Research On Energy Efficiency Status Monitoring And Evaluation System Of 300MW Air-cooled Steam Turbine Units

In the context of the common efforts of the whole society to achieve the long-term goals of "carbon peaking and carbon neutrality",energy conservation and consumption reduction in the power industry,especially in traditional thermal power industry,has become urgent.Currently,there are a large number of old subcritical thermal power units in China,which are unable to reach their optimal operating state due to equipment aging and poor maintenance.However,as the largest type of power generation capacity in China,subcritical units bear the important responsibility of ensuring energy supply in a certain period and are difficult to be phased out on a large scale for a while.This article focuses on such subcritical steam turbine units and uses data mining technology to find effective information from historical operating data to guide the efficient operation of the units in the future.First,according to the analytic hierarchy process,the steam turbine units are divided into four subsystems:main system,reheating system,cold end system,and heating system,based on the "system-subsystem" mode,and the energy efficiency indicators for each subsystem are determined based on thermodynamic related knowledge.Finally,the energy efficiency indicator system of the steam turbine unit is constructed.Then,multi-dimensional Gaussian mixture clustering analysis is used to analyze the historical data of the steam turbine units that have undergone steady-state screening and operating condition division to determine the energy efficiency benchmark state of typical operating conditions and the corresponding energy efficiency indicator benchmark values.In order to supplement the missing benchmark data for operating conditions not covered by historical data,the least squares support vector regression machine is used to regress predict the energy efficiency indicators of the steam turbine unit with operating boundary parameters,so as to obtain the energy efficiency benchmark state of all operating conditions.Gray correlation analysis is then used to determine the correlation coefficient between the energy efficiency indicators and the heat consumption rate,and the deviation distance of the energy efficiency state is defined to measure and evaluate the deviation degree between the actual operating state and the energy efficiency benchmark state of the unit.Finally,in combination with the experience of power plant personnel,an optimized operating strategy for steam turbine units is formulated.A steam turbine unit energy efficiency monitoring and evaluation system has been developed and installed in a power plant in Inner Mongolia to help improve the energy utilization efficiency of the steam turbine units,and the effectiveness of the system has been verified.