Research On Energy-saving Optimization Control Application Of Thermal Power Unit

With the vigorous adjustment of China’s energy structure and the continuous deepening of the power market,the traditional decentralized,single unit energy-saving optimization control method can no longer adapt to the new situation.The digitalization,refinement and unified energy-saving optimization control of power generation enterprises have gradually become mainstream demand.Energy-saving optimization control of thermal power unit is a comprehensive subject,including equipment upgrade,operation mode optimization,control strategy optimization,etc.,covering the whole process of unit production.Based on the concept of intelligent power plant,the energy-saving optimization intelligent control system of thermal power unit is discussed.The system is divided into intelligent management layer,intelligent optimization supervision layer and intelligent control based on the framework of group company,regional company,plant level and unit level.Layer and smart device layer.This paper has carried out in-depth research on plant-level load optimization and boiler combustion optimization.(1)On the basis of summarizing the traditional economic load distribution model of thermal power plants,by analyzing the "two rules" regulations in the southern region,the plant-level load adjustment time constraint and the unit peak-sharing compensation model based on the AGC adjustment rate assessment index are proposed.In this way,a load optimization allocation model based on "two rules" is constructed.The multi-objective problem is dimensionless processed by the extreme value method,and the fuzzy weight analysis method(FHAP)is used to determine the target weights.The results show that the new model can achieve the best balance in all indicators,and the calculation time is short,which has certain promotion significance.At the same time,a set of plant-level load optimization distribution physical simulation platform was built to simulate the plant-level AGC operation scenario and complete the transformation of research results.(2)Aiming at the urgent need to improve boiler efficiency and reduce pollutant emissions for coal-fired boilers of thermal power units,and considering that the combustion optimization algorithm based on intelligent calculation is difficult to apply to engineering problems,a power plant boiler combustion optimization system based on data mining case-based reasoning is proposed..According to the massive DCS historical data,the improved fuzzy subtractive clustering algorithm is used to determine the classification number,and the fuzzy C-means algorithm is used to establish the initial case base,and the target optimization reduction case library is optimized.When applied online,the case-based reasoning method based on non-controllable factors calculates the optimal combustion parameters of the current working conditions,and corrects the output parameters according to the controllable factors to ensure the real-time optimality of the system.Compared with the optimization effect of a certain unit,the combustion optimization system of power station boiler based on data mining case reasoning has low complexity and high stability.It is a practical and efficient method for combustion optimization of power station boilers,which has certain promotion significance.