EN基于改进海鸥–粒子群算法的火电机组一次调频参数辨识
Primary Frequency Modulation Parameter Identification for Thermal Power Units Based on Improved Seagull-particle Swarm Optimization Algorithm
- 现代电力 2025年第6期 页码：1239-1247
- 作者机构：
1. 华北电力大学 能源动力与机械工程学院, 昌平区,北京市,102206
2. 国网福建省电力公司电力科学研究院,福建省,福州市,350007
3. 国网山东省电力公司电力科学研究院,山东省,济南市,250000
- 作者简介：
[ "车其祥(1998)，男，硕士，研究方向为火电机组一次调频优化，E-mail：1784019392@qq.com" ]
[ "何成兵(1974)，男，博士，副教授，通信作者，研究方向为火电机组灵活性运行与节能优化，E-mail：hcbyy@126.com" ]
- 基金信息：国家电网有限公司总部管理科技项目(52060022001R)。
DOI：10.19725/j.cnki.1007-2322.2023.0299
中图分类号：-
纸质出版：2025
- 稿件说明：
移动端阅览
车其祥, 何成兵, 徐振华, 等. 基于改进海鸥–粒子群算法的火电机组一次调频参数辨识[J]. 现代电力, 2025,(6):1239-1247.
CHE Qixiang, HE Chengbing, XU Zhenhua, et al. Primary Frequency Modulation Parameter Identification for Thermal Power Units Based on Improved Seagull-particle Swarm Optimization Algorithm[J]. 2025, (6): 1239-1247.
车其祥, 何成兵, 徐振华, 等. 基于改进海鸥–粒子群算法的火电机组一次调频参数辨识[J]. 现代电力, 2025,(6):1239-1247. DOI： 10.19725/j.cnki.1007-2322.2023.0299.
CHE Qixiang, HE Chengbing, XU Zhenhua, et al. Primary Frequency Modulation Parameter Identification for Thermal Power Units Based on Improved Seagull-particle Swarm Optimization Algorithm[J]. 2025, (6): 1239-1247. DOI： 10.19725/j.cnki.1007-2322.2023.0299.
摘要
针对粒子群优化（particle swarm optimization
PSO）算法存在搜索能力不足和局部收敛的问题，提出了一种融合海鸥优化算法（seagull optimization algorithm
SOA）与PSO算法相结合的火电机组一次调频参数辨识方法。该算法采用自适应非线性惯性权重，以平衡算法局部与全局搜索能力；采取非线性飞行时间系数，以优化算法收敛速度；采用融合海鸥算法螺旋攻击行为的方式对适应度较差的粒子进行优化处理，增强算法跳出局部最优的能力，有效避免算法局部收敛问题。针对机组电液伺服系统的PID参数辨识进行仿真验证，并与传统PSO算法进行比较，针对汽轮机3个容积时间常数辨识进行试验验证，并应用于火电机组一次调频仿真建模，结果表明该方法适用于火电机组一次调频参数辨识，且辨识精度更高，算法更为稳定，收敛速度也更快。
Abstract
To address the issue of insufficient search ability and local convergence of particle swarm optimization(PSO) algorithm
in this paper we propose a primary frequency modulation parameter identification method for thermal power units combining seagull optimization algorithm (SOA) and PSO algorithm. This method utilizes adaptive nonlinear inertial weights to balance the local and global search capabilities of the algorithm. The nonlinear time-of-flight coefficient is adopted to optimize the convergence speed of the algorithm. The spiral attack behavior of the seagull algorithm is utilized to optimize the particles with poor adaptability
thereby enhancing the ability of the algorithm to jump out of the local optimization and effectively avoiding the problem of local convergence of the algorithm. The PID parameter identification of the electro-hydraulic servo system of the unit is simulated
verified
and compared with the traditional PSO algorithm. The identification of the three volumetric time constants of the steam turbine is validated through the experiment. The method is applied to the primary frequency regulation simulation modeling of thermal power units. The results indicate that the method is suitable for the identification of primary frequency regulation parameters of thermal power units
with higher identification accuracy
greater algorithm stability
and faster convergence speed.
关键词
Keywords
references
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