【目的】双碳目标下,为加速火电机组从主力型电源向提供调峰调频服务的辅助型电源角色转型,提升机组的负荷响应能力至关重要。传统控制方法在火电机组大范围变负荷运行过程中,容易出现响应不及时、稳态精度差、计算量大等问题。【方法...【目的】双碳目标下,为加速火电机组从主力型电源向提供调峰调频服务的辅助型电源角色转型,提升机组的负荷响应能力至关重要。传统控制方法在火电机组大范围变负荷运行过程中,容易出现响应不及时、稳态精度差、计算量大等问题。【方法】本文以某1 000 MW超超临界火电机组协调控制系统为研究对象,提出了一种基于Koopman算子的模型预测控制(Koopman model predictive control,KMPC)方法。该方法采用4阶龙格库塔法离散化原非线性系统获取数据集,通过扩展动态模态分解法有限维近似Koopman算子构建机组高维线性近似模型,并基于该模型预测系统未来动态,引入滚动时域优化策略,综合考虑控制约束、控制目标和性能指标等约束条件,设计超超临界火电机组的模型预测控制算法。以局部线性MPC(locallinear model predictive control,LMPC)为基准对比算法,通过仿真实验验证本文所提出的KMPC算法的有效性。【结果】研究表明,对应于机组的主蒸汽压力、分离器蒸汽焓值、汽轮机发电功率,(1)在连续阶跃升负荷仿真实验中高维近似系统与原非线性系统输出量相对均方根误差分别为1.00%,0.40%和0.36%;(2)标称工况下,KMPC算法在升负荷实验中输出量的时间加权绝对误差积分(integral of time-weighted absolute error,ITAE)相较LMPC分别减少了46.67%、48.66%和21.46%;(3)在模型失配工况下,相较于LMPC算法,KMPC算法在升负荷实验中输出量的ITAE分别减少了19.57%、22.45%和30.94%。【结论】基于Koopman算子构建的高维线性近似模型可较为精准捕捉原系统非线性动力学特征;与LMPC算法相比,KMPC算法在机组大范围变负荷运行过程中响应更加及时且稳态误差更小,同时表现出更强的鲁棒性,有利于机组现场运行。展开更多
With the growing integration of renewable energy sources(RESs)and smart interconnected devices,conventional distribution networks have turned to active distribution networks(ADNs)with complex system model and power fl...With the growing integration of renewable energy sources(RESs)and smart interconnected devices,conventional distribution networks have turned to active distribution networks(ADNs)with complex system model and power flow dynamics.The rapid fluctuation of RES power may easily result in frequent voltage violation issues.Taking the flexible RES reactive power as control variables,this paper proposes a two-layer control scheme with Koopman wide neural network(WNN)based model predictive control(MPC)method for optimal voltage regulation and network loss reduction.Based on Koopman operator theory,a data-driven WNN method is presented to fit a high-dimensional linear model of power flow.With the model,voltage and network loss sensitivities are computed analytically,and utilized for ADN partition and control model formulation.In the lower level,a dual-mode adaptive switching MPC strategy is put forward for optimal voltage control and network loss optimization in each individual partition to decide the RES reactive power.The upper level is to calculate the adjustment coefficients of the RES reactive power given in the low level by taking the coupling effects of different partitions into account,and then the final reactive power dispatches of RESs are obtained to realize optimal control of voltage and network loss.Simulation results on two ADNs demonstrate that the proposed strategy can reliably maintain the voltage at each node within the secure range,reduce network power losses,and enhance the overall system security and economic efficiency.展开更多
Dear Editor,This letter presents a novel approach to the data-driven control of unknown nonlinear systems.By leveraging online sparse identification based on the Koopman operator,a high-dimensional linear system model...Dear Editor,This letter presents a novel approach to the data-driven control of unknown nonlinear systems.By leveraging online sparse identification based on the Koopman operator,a high-dimensional linear system model approximating the actual system is obtained online.The upper bound of the discrepancy between the identified model and the actual system is estimated using real-time prediction error,which is then utilized in the design of a tube-based robust model predictive controller.The effectiveness of the proposed approach is validated by numerical simulation.展开更多
文摘【目的】双碳目标下,为加速火电机组从主力型电源向提供调峰调频服务的辅助型电源角色转型,提升机组的负荷响应能力至关重要。传统控制方法在火电机组大范围变负荷运行过程中,容易出现响应不及时、稳态精度差、计算量大等问题。【方法】本文以某1 000 MW超超临界火电机组协调控制系统为研究对象,提出了一种基于Koopman算子的模型预测控制(Koopman model predictive control,KMPC)方法。该方法采用4阶龙格库塔法离散化原非线性系统获取数据集,通过扩展动态模态分解法有限维近似Koopman算子构建机组高维线性近似模型,并基于该模型预测系统未来动态,引入滚动时域优化策略,综合考虑控制约束、控制目标和性能指标等约束条件,设计超超临界火电机组的模型预测控制算法。以局部线性MPC(locallinear model predictive control,LMPC)为基准对比算法,通过仿真实验验证本文所提出的KMPC算法的有效性。【结果】研究表明,对应于机组的主蒸汽压力、分离器蒸汽焓值、汽轮机发电功率,(1)在连续阶跃升负荷仿真实验中高维近似系统与原非线性系统输出量相对均方根误差分别为1.00%,0.40%和0.36%;(2)标称工况下,KMPC算法在升负荷实验中输出量的时间加权绝对误差积分(integral of time-weighted absolute error,ITAE)相较LMPC分别减少了46.67%、48.66%和21.46%;(3)在模型失配工况下,相较于LMPC算法,KMPC算法在升负荷实验中输出量的ITAE分别减少了19.57%、22.45%和30.94%。【结论】基于Koopman算子构建的高维线性近似模型可较为精准捕捉原系统非线性动力学特征;与LMPC算法相比,KMPC算法在机组大范围变负荷运行过程中响应更加及时且稳态误差更小,同时表现出更强的鲁棒性,有利于机组现场运行。
基金supported by the Science and Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.(J2024162).
文摘With the growing integration of renewable energy sources(RESs)and smart interconnected devices,conventional distribution networks have turned to active distribution networks(ADNs)with complex system model and power flow dynamics.The rapid fluctuation of RES power may easily result in frequent voltage violation issues.Taking the flexible RES reactive power as control variables,this paper proposes a two-layer control scheme with Koopman wide neural network(WNN)based model predictive control(MPC)method for optimal voltage regulation and network loss reduction.Based on Koopman operator theory,a data-driven WNN method is presented to fit a high-dimensional linear model of power flow.With the model,voltage and network loss sensitivities are computed analytically,and utilized for ADN partition and control model formulation.In the lower level,a dual-mode adaptive switching MPC strategy is put forward for optimal voltage control and network loss optimization in each individual partition to decide the RES reactive power.The upper level is to calculate the adjustment coefficients of the RES reactive power given in the low level by taking the coupling effects of different partitions into account,and then the final reactive power dispatches of RESs are obtained to realize optimal control of voltage and network loss.Simulation results on two ADNs demonstrate that the proposed strategy can reliably maintain the voltage at each node within the secure range,reduce network power losses,and enhance the overall system security and economic efficiency.
基金supported by the National Natural Science Foundation of China(62473020).
文摘Dear Editor,This letter presents a novel approach to the data-driven control of unknown nonlinear systems.By leveraging online sparse identification based on the Koopman operator,a high-dimensional linear system model approximating the actual system is obtained online.The upper bound of the discrepancy between the identified model and the actual system is estimated using real-time prediction error,which is then utilized in the design of a tube-based robust model predictive controller.The effectiveness of the proposed approach is validated by numerical simulation.
