The capability to perform fast load-following has been an important issue in the power industry. An output tracking control system of a boiler-turbine unit is developed. The system is composed of stable inversion and ...The capability to perform fast load-following has been an important issue in the power industry. An output tracking control system of a boiler-turbine unit is developed. The system is composed of stable inversion and feedback controller. The stable inversion is implemented as a feedforward controller to improve the load-following capability, and the feedback controller is utilized to guarantee the stability and robustness of the whole system. Loop-shaping H∞ method is used to design the feedback controller and the final controller is reduced to a multivariable PI form. The output tracking control system takes account of the multivariable, nonlinear and coupling behavior of boiler-turbine system, and the simulation tests show that the control system works well and can be widely applied.展开更多
火电机组在现代电力系统中承担着大量的调峰调频任务,通过运行参数建立出力预测模型有助于快速稳定地调整功率。提出一种改进的深度回声状态网络(Deep Echo State Networks,DESN)用于建立机组出力预测模型。该改进型具备可变的记忆能力...火电机组在现代电力系统中承担着大量的调峰调频任务,通过运行参数建立出力预测模型有助于快速稳定地调整功率。提出一种改进的深度回声状态网络(Deep Echo State Networks,DESN)用于建立机组出力预测模型。该改进型具备可变的记忆能力以应对调整部分运行参数作用于机组出力变化存在的延时性,并根据运行参数聚类生成输入权重进一步挖掘运行参数与出力之间的映射信息。利用华北地区某火电机组不同工作状况下的两种数据集验证了模型效果。结果表明,改进得到的KM-VML-DESN相较于深度回声状态网络、多层感知机、长短期记忆网络等具备更强的预测性能。展开更多
文摘The capability to perform fast load-following has been an important issue in the power industry. An output tracking control system of a boiler-turbine unit is developed. The system is composed of stable inversion and feedback controller. The stable inversion is implemented as a feedforward controller to improve the load-following capability, and the feedback controller is utilized to guarantee the stability and robustness of the whole system. Loop-shaping H∞ method is used to design the feedback controller and the final controller is reduced to a multivariable PI form. The output tracking control system takes account of the multivariable, nonlinear and coupling behavior of boiler-turbine system, and the simulation tests show that the control system works well and can be widely applied.
文摘火电机组在现代电力系统中承担着大量的调峰调频任务,通过运行参数建立出力预测模型有助于快速稳定地调整功率。提出一种改进的深度回声状态网络(Deep Echo State Networks,DESN)用于建立机组出力预测模型。该改进型具备可变的记忆能力以应对调整部分运行参数作用于机组出力变化存在的延时性,并根据运行参数聚类生成输入权重进一步挖掘运行参数与出力之间的映射信息。利用华北地区某火电机组不同工作状况下的两种数据集验证了模型效果。结果表明,改进得到的KM-VML-DESN相较于深度回声状态网络、多层感知机、长短期记忆网络等具备更强的预测性能。
