Research On The Application Of Intelligent Optimization Algorithm In The Optimal Operation Of Hydropower Station

With the vigorous development of hydropower stations in China,how to ensure the safe and stable operation of hydropower stations is a hot spot of concern of water conservancy researchers.In order to ensure the safety of hydropower stations,accurate control of the water level in front of the dam is the basic requirement of hydropower stations.For general run-of-the-river power stations,it is mainly through adjusting the output of the unit and the opening and closing of the gate to achieve the control of the water level,in the face of the complex upstream water conditions and fixed power generation instructions,which can only rely on the opening and closing of the gate to ensure the safety of the water level of the power station,so it is necessary to study the gate control strategy to ensure the safety of the hydropower station.Pumped storage power station structure is complex,changing working conditions lead to frequent changes in the unit,if the unit suddenly dumps the load,it will lead to a sudden rise in spring casing pressure and a sudden drop in draft tube pressure and a rapid increase in the speed of the unit,excessive spring casing pressure and unit speed will affect the life of the unit and even endanger the safety of the power station,how to control the internal pressure extremum of the pipeline and the speed of the unit is also a problem worth studying.Optimization of the closing law of the guide vane is the most economical and efficient way,which can reduce the incident risk from pressure and speed excursions and guarantee the security of hydro-turbine and the whole hydraulic network.In order to optimize the closing law of the guide vane of hydraulic turbine,an improved artificial ecosystem optimization algorithm was proposed(IAEO).The reverse learning was used to initialize the population,multi-strategy bound handing schemes was used to improve the algorithm convergence speed.Twenty-three mathematical benchmark functions are used to test IAEO,the results show that the IAEO algorithm improved convergence speed and has a stronger exploration than other algorithms.IAEO algorithm was used to optimize the closing law of the guide vane of hydraulic turbine based on the hydraulic transient calculation.The results show that the maximum pressure in the spiral casing inlet,the minimum pressure in the draft tube inlet and the maximum speed all meet the design requirements by use of the closing law of the guide vane optimized by IAEO.Compared with other algorithms such as Particle Swarm Optimization(PSO),Artificial Ecosystem-based Optimization(AEO)and Grey Wolf Optimizer(GWO),the closing law of the guide vane optimized by IAEO algorithm was proved to be of great advantages in distribution of safety margin of each optimization goal.During the operation of a hydropower station,the change of unit output and gate action will affect the change of water level.Based on the characteristic curve of a run-off hydropower station,a short-term water level prediction model based on water balance is established and verified.The remaining storage capacity and reaction time of the power station during the accident cutting were studied,and the limit reaction time and the ultimate operating water level at the time of accident cutting were calculated,which provided a reference basis for confirming the safe operation range of the hydropower station.According to the operation law of the gate of the hydropower station,the door opening combination table is drawn,combined with the particle swarm optimization algorithm,and the emergency control and segmented optimization method of the gate are proposed,which can effectively realize the accurate control of the reservoir water level when the power station encounters an accident and cuts the machine.