Parameters Optimization Of Hydropower Unit Start-Up Process Based On Multi-Objective Intelligent Algorithm

China is rich in hydropower energy and has a huge output of power generation,which can provide high-quality adjustable power for the power grid.With the upgrading of the power grid structure,the penetration rate of new energy continues to increase,and hydropower undertakes the task of balancing power fluctuations,thus,the operating burden of hydraulic turbine governors increases and the startup is more frequent,which will lead to more fatigue damage and stability issues for hydraulic turbine units.Therefore,this thesis aims to explore the key parameters and stability indicators during the startup process,establish a refined model of the governing system,then carry out multi-objective optimization on the unit startup law to obtain appropriate startup parameters,shorten the speed adjustment time while suppressing the pressure pulsation and axial hydraulic thrust during the startup process.In this thesis,a nonlinear mathematical model of the governing system is built based on the hydraulic layout information of an actual power station.The thesis digitizes the comprehensive characteristic curve,models the pipeline system and boundary conditions with the characteristic line method,and verifies the mathematical model using on-site measured data.This thesis explores key parameters that have a significant impact on the performance of the startup process as well as stability parameter indicators which need to be emphatically analyzed through model simulation and field test data analysis,providing a basis for decision-making variables and objective functions for subsequent multi-objective optimization.An improved multi-objective particle swarm optimization(MOPSO)algorithm based on Gaussian mutation and reference vectors is proposed to enhance the advantages of individual updating and external file set maintenance,it ensures the convergence and diversity of solutions.Taking the correlation parameters between rotational speed,pressure pulsation,and axial hydraulic thrust as the objective functions,the aim is to shorten the rotational speed adjustment time while suppressing the pressure pulsation and axial hydraulic thrust during the startup process.Taking important parameters of the startup rule as decision vectors,the improved MOPSO algorithm is used to optimize the startup rule and will obtain Pareto frontiers.The dynamic quality of the solutions in Pareto frontiers improves compared to original scheme,the thesis analyzes the differences of the solutions in game relationships between several solutions in handling speed,pressure pulsation,and mechanical vibration.A final solution selection model based on weight assignment is proposed,which can effectively help staff to reasonably select the final solution according to the actual situation,ensuring the safe,stable and economic operation of the units.