针对不同工况下水轮机调节系统的控制要求,从计算机仿真的角度,讨论水轮机调速器参数优化的目标和意义。利用过渡过程和ITAE(integral of time-weighted absolute error)准则,着重对单机工况下的参数优化策略进行研究。通过分析PID控制...针对不同工况下水轮机调节系统的控制要求,从计算机仿真的角度,讨论水轮机调速器参数优化的目标和意义。利用过渡过程和ITAE(integral of time-weighted absolute error)准则,着重对单机工况下的参数优化策略进行研究。通过分析PID控制系统优化模式之间矛盾产生的机理,即扰动抑制性能和稳定性能在本质上的不相容性,确立了优先保证稳定性的参数优化策略,据此提出首先通过给定值跟随优化模式确定参数KP以保证稳定性,再通过扰动抑制优化模式确定参数KI、KD以优化扰动抑制性能的二次参数优化法,并对斯坦因经验公式进行了相应的修正。仿真结果证明该方法可很好地缓解该工况下稳定性与抗负荷扰动能力的矛盾,是可行和有效的。展开更多
Optimization of the closing law of the guide vane is the most economical and efficient way to reduce the risk incurred by pressure and speed excursions,thus guaranteeing the security of the hydro-turbine and the whole...Optimization of the closing law of the guide vane is the most economical and efficient way to reduce the risk incurred by pressure and speed excursions,thus guaranteeing the security of the 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 were used to test the IAEO.Results showed an improvement in the IAEO algorithm convergence speed and 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 showed 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.展开更多
文摘针对不同工况下水轮机调节系统的控制要求,从计算机仿真的角度,讨论水轮机调速器参数优化的目标和意义。利用过渡过程和ITAE(integral of time-weighted absolute error)准则,着重对单机工况下的参数优化策略进行研究。通过分析PID控制系统优化模式之间矛盾产生的机理,即扰动抑制性能和稳定性能在本质上的不相容性,确立了优先保证稳定性的参数优化策略,据此提出首先通过给定值跟随优化模式确定参数KP以保证稳定性,再通过扰动抑制优化模式确定参数KI、KD以优化扰动抑制性能的二次参数优化法,并对斯坦因经验公式进行了相应的修正。仿真结果证明该方法可很好地缓解该工况下稳定性与抗负荷扰动能力的矛盾,是可行和有效的。
基金supported by the National Natural Science Foundation of China(Grant Nos.51879140,11972144 and 12072098)supported by the One Hundred Outstanding Innovative Scholars of Collegessand Universities inHebeiProvince(Grant No.SLRC2019022)+2 种基金the State Key Laboratoryof Hydroscience and Engineering,Tsinghua University(Grant No.2021-KY-04)Tsinghua-Foshan Innovation Special Fund(TFISF)(Grant No.2021THFS0209)the Creative Seed Fund of Shanxi Research Institute for Clean Energy,Tsinghua University.
文摘Optimization of the closing law of the guide vane is the most economical and efficient way to reduce the risk incurred by pressure and speed excursions,thus guaranteeing the security of the 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 were used to test the IAEO.Results showed an improvement in the IAEO algorithm convergence speed and 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 showed 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.
