河南科技大学学报（自然科学版）2025，Vol.46Issue(6)：49-58,10.DOI:10.15926/j.cnki.issn1672-6871.2025.06.006
基于ISCSO算法的燃气-蒸汽联合循环机组负荷对象模型辨识
Model Identification of Gas-Steam Combined Cycle Unit Load System Based on ISCSO Algorithm
摘要
Abstract
Establishing an accurate mathematical model of the load object for a gas-steam combined cycle unit is a crucial prerequisite for improving the performance of its load control system.To address the limitations of traditional identification methods in terms of accuracy and convergence speed,this paper proposes a model identification approach based on an improved sand cat swarm optimization(ISCSO)algorithm.First,the initial population is enhanced using Logistic chaotic mapping.The sensitivity parameter is modified from linear to cosine-based variation.Additionally,a differential evolution mutation mechanism and Gaussian perturbation method are introduced to improve optimization efficiency and effectively avoid local optima.Then,the ISCSO algorithm is employed to optimize the model parameters and obtain their optimal values.Finally,model identification results from the ISCSO and SCSO algorithms are compared and validated using data obtained from an open-loop step experiment at the 312.06 MW load point of the gas-steam combined cycle unit.The effectiveness of the improvement strategies in the algorithm is validated through ablation experiments.The results demonstrate that the proposed algorithm establishes a more accurate load model compared to benchmark algorithms.The ISCSO-identified model achieves the lowest mean absolute percentage error(MAPE)and root mean square error(RMSE),exhibiting superior convergence performance.This work provides a new methodology for model identification.关键词
燃气-蒸汽联合循环机组/负荷对象/模型辨识/改进沙猫群优化算法Key words
gas-steam combined cycle unit/load object/model identification/improved sand cat swarm optimization algorithm分类
能源科技引用本文复制引用
徐晓雯,康英伟..基于ISCSO算法的燃气-蒸汽联合循环机组负荷对象模型辨识[J].河南科技大学学报（自然科学版）,2025,46(6):49-58,10.基金项目
国家自然科学基金项目(61573239) （61573239）
