A novel Parsimonious Genetic Programming （PGP） algorithm together with a novel aero-engine optimum data-driven dynamic start process model based on PGP is proposed. In application of this method, first, the traditio...A novel Parsimonious Genetic Programming （PGP） algorithm together with a novel aero-engine optimum data-driven dynamic start process model based on PGP is proposed. In application of this method, first, the traditional Genetic Programming（GP） is used to generate the nonlinear input-output models that are represented in a binary tree structure; then, the Orthogonal Least Squares algorithm （OLS） is used to estimate the contribution of the branches of the tree （refer to basic function term that cannot be decomposed anymore according to special rule） to the accuracy of the model, which contributes to eliminate complex redundant subtrees and enhance GP＇s convergence speed; and finally, a simple, reliable and exact linear-in-parameter nonlinear model via GP evolution is obtained. The real aero-engine start process test data simulation and the comparisons with Support Vector Machines （SVM） validate that the proposed method can generate more applicable, interpretable models and achieve comparable, even superior results to SVM.展开更多
为解决传统模型过于简化以致调度计划存在电量不可交付、产生系统频率偏差的问题,提出一种考虑电量可实现性和启停功率轨迹的火电机组组合混合整数线性规划（mixed-integer linear programming,MILP）模型,该模型引入一类0-1变量表示燃...为解决传统模型过于简化以致调度计划存在电量不可交付、产生系统频率偏差的问题,提出一种考虑电量可实现性和启停功率轨迹的火电机组组合混合整数线性规划（mixed-integer linear programming,MILP）模型,该模型引入一类0-1变量表示燃煤机组的运行状态,便于在其加热、升负荷、调度和降负荷4个阶段的逻辑判断和发电量计算;根据燃气燃油机组启停迅速的特点,对其运行状态重新建模;并支持冷、温、热等多种启动类型和1 h、15 min等多种调度时段长度。10～1 000机24时段系统的计算结果表明：所提模型更符合实际运行情况,可很好地解决电量不可交付问题,且具有较高的求解效率。展开更多
基金National Defense Advanced Research Foundation of China
文摘A novel Parsimonious Genetic Programming （PGP） algorithm together with a novel aero-engine optimum data-driven dynamic start process model based on PGP is proposed. In application of this method, first, the traditional Genetic Programming（GP） is used to generate the nonlinear input-output models that are represented in a binary tree structure; then, the Orthogonal Least Squares algorithm （OLS） is used to estimate the contribution of the branches of the tree （refer to basic function term that cannot be decomposed anymore according to special rule） to the accuracy of the model, which contributes to eliminate complex redundant subtrees and enhance GP＇s convergence speed; and finally, a simple, reliable and exact linear-in-parameter nonlinear model via GP evolution is obtained. The real aero-engine start process test data simulation and the comparisons with Support Vector Machines （SVM） validate that the proposed method can generate more applicable, interpretable models and achieve comparable, even superior results to SVM.
