风光火打捆外送系统概率小干扰稳定优化控制研究

和萍; 潘志文; 李从善; 陶玉昆; 刘泽萌; 刘欣妍

doi:10.19912/j.0254-0096.tynxb.2025-0257

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风光火打捆外送系统概率小干扰稳定优化控制研究

更新时间：2026-07-22

- 风光火打捆外送系统概率小干扰稳定优化控制研究
- Vol. 47, Issue 6, Pages: 169-179(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2025-0257
  CLC： TM732
- Online First：08 July 2026，
  
  Published：2026
- 稿件说明：
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和萍, 潘志文, 李从善, et al. 风光火打捆外送系统概率小干扰稳定优化控制研究[J]. 2026, 47(6): 169-179.
DOI：

和萍, 潘志文, 李从善, et al. 风光火打捆外送系统概率小干扰稳定优化控制研究[J]. 2026, 47(6): 169-179. DOI： 10.19912/j.0254-0096.tynxb.2025-0257.

摘要

为降低电力系统中风电出力波动、低频振荡和多控制器耦合等因素带来的危害

提出一种多运行方式下多控制器参数协调优化策略。首先

基于Simulink建立风光火打捆外送系统和控制器模型

通过Voronoi分区采样法建立风电典型出力场景。然后

利用概率论方法构造优化问题目标函数

通过随机森林回归分析选取控制器关键优化参数。最后

采用改进的混合蜣螂算法对多运行方式下的控制器关键参数进行协调优化设计。IEEE 4机2区和16机5区系统仿真结果表明

对提取出来的控制器参数进行优化

能有效提高系统阻尼

抑制低频振荡引起的系统有功、功角等的波动

验证该概率优化方法的有效性。

Abstract

To mitigate the adverse effects of wind power output fluctuations

low-frequency oscillations

and multi-controller coupling in power systems

this paper proposes a coordinated optimization strategy for multi-controller parameters under diverse operating conditions. Firstly

a Simulink model of the wind-PV-thermal bundled power transmission system and its controllers is established. Typical wind power output scenarios are generated using the Voronoi partition sampling method. Subsequently

the objective function for the optimization problem is formulated based on probabilistic methods

and critical controller parameters for optimization are identified via random forest regression analysis. Finally

an improved hybrid dung beetle optimizer is employed to coordinately optimize these key controller parameters across multiple operating scenarios. Simulation results on the IEEE 4-machine 2-area system and the 16-machine 5-area system demonstrate that optimizing the extracted controller parameters effectively enhances system damping. This significantly suppresses fluctuations in active power

power angles

and other system variables caused by low-frequency oscillations

thereby validating the efficacy of the proposed probabilistic optimization approach.

关键词

Keywords

references

王海鑫, 刘铭崎, 董鹤楠, 等. 含高比例新能源的电力系统低频振荡分析与抑制综述[J]. 电力自动化设备, 2023, 43(9): 152-163.

程静, 苏乐, 岳雷. 新能源接入电力系统的宽频振荡风险识别与抑制[J]. 太阳能学报, 2023, 44(11): 565-574.

和萍, 方祺元, 武小鹏, 等. 风光火打捆外送系统STATCOM-POD协调优化设计[J]. 电力系统保护与控制, 2022, 50(1): 78-87.

夏芹芹, 罗永捷, 王荣茂, 等. 考虑新能源爬坡的风光火耦合系统源荷匹配性分析及容量优化配置[J]. 上海交通大学学报, 2024, 58(1): 69-81.

余明昊, 顾雪平, 李少岩. 风光火打捆系统暂态稳定性及其虚拟同步机策略研究[J]. 华北电力大学学报(自然科学版), 2023, 50(5): 10-18.

徐式蕴, 李宗翰, 赵兵, 等. 新型电力系统标准算例(一): 功角稳定CSEE-RAS[J]. 中国电机工程学报, 2024, 44(15): 5973-5984.

侯恺, 刘泽宇, 贾宏杰, 等. 含高比例可再生能源的电力系统运行可靠性解析评估方法综述[J]. 高电压技术, 2023, 49(7): 2697-2710.

KAMARPOSHTI M A, COLAK I, IWENDI C, et al.Optimal coordination of PSS and SSSC controllers in power system using ant colony optimization algorithm[J]. Journal of circuits, systems and computers, 2022, 31(4): 2250060.

DEVARAPALLI R, SINHA N K, GARCÍA MÁRQUEZ F P. A review on the computational methods of power system stabilizer for damping power network oscillations[J]. Archives of computational methods in engineering, 2022, 29(6): 3713-3739.

BEZA M, BONGIORNO M.A modified RLS algorithm for online estimation of low-frequency oscillations in power systems[J]. IEEE transactions on power systems, 2016, 31(3): 1703-1714.

NIU Y T.Coordinated optimization of parameters of PSS and UPFC-PODCs to improve small-signal stability of a power system with renewable energy generation[C]//2021 11th International Conference on Power, Energy and Electrical Engineering (CPEEE). Shiga, Japan, 2021: 249-254.

HUANG J K, YANG Z F, YU J, et al.Optimization for DFIG fast frequency response with small-signal stability constraint[J]. IEEE transactions on energy conversion, 2021, 36(3): 2452-2462.

马燕峰, 景雪, 赵书强, 等. 光伏接入系统的小干扰稳定灵敏度分析及其应用[J]. 电力自动化设备, 2021, 41(8): 70-75.

BHUKYA J, MAHAJAN V.Parameter tuning of PSS and STATCOM controllers using genetic algorithm for improvement of small-signal and transient stability of power systems with wind power[J]. International transactions on electrical energy systems, 2021, 31(7): e12912.

HE P, JIN H R, PAN Z W, et al.Optimal strategy for enhancing probabilistic small-signal stability in a power system with wind-PV-thermal bundled transmission[J]. Journal of energy engineering, 2023, 149(3): 04023014.

徐恒山, 李颜汝, 李文昊, 等. 融合寻优算法的双馈风力机控制参数分步辨识方法[J]. 太阳能学报, 2024, 45(4): 247-256.

李东东, 罗丁, 孙梦显, 等. 适用于VSC-HVDC和PMSG并网系统的宽频振荡稳定性判据及阻尼优化控制[J]. 电力自动化设备, 2025, 45(4): 110-117.

朱介北, 黄闽杰, 俞露杰, 等. 基于马尔科夫转换场与深度确定性策略梯度算法的VSC-HVDC系统控制参数优化方法[J]. 中国电机工程学报, 2026, 46(5): 1821-1832.

DEVARAPALLI R, BHATTACHARYYA B, SINHA N K, et al.Amended GWO approach based multi-machine power system stability enhancement[J]. ISA transactions, 2021, 109: 152-174.

DEVARAPALLI R, BHATTACHARYYA B.A hybrid modified grey wolf optimization-sine cosine algorithm-based power system stabilizer parameter tuning in a multimachine power system[J]. Optimal control applications and methods, 2020, 41(4): 1143-1159.

ALSHAMMARI B M, GUESMI T.New chaotic sunflower optimization algorithm for optimal tuning of power system stabilizers[J]. Journal of electrical engineering & technology, 2020, 15(5): 1985-1997.

熊浩清, 何鹏飞, 孙冉, 等. 双馈风电场无串补并网振荡场景及关键影响因素研究[J]. 高电压技术, 2024, 50(2): 670-685.

董存, 王铮, 白捷予, 等. 光伏发电功率超短期预测方法综述[J]. 高电压技术, 2023, 49(7): 2938-2951.

XUE J K, SHEN B.Dung beetle optimizer: a new meta-heuristic algorithm for global optimization[J]. The journal of supercomputing, 2023, 79(7): 7305-7336.

席磊, 彭典名, 曹伟, 等. 数据驱动算法的电力信息物理系统FDIA定位检测[J]. 中国电机工程学报, 2025, 45(18): 7110-7122.

葛晓琳, 章国耀, 符杨, 等. 基于改进鲸鱼算法的含风电电力系统动态安全预防控制策略[J]. 中国电机工程学报, 2025, 45(1): 98-110.

MIRJALILI S, MIRJALILI S M, LEWIS A.Grey wolf optimizer[J]. Advances in engineering software, 2014, 69: 46-61.

MIRJALILI S, LEWIS A.The whale optimization algorithm[J]. 2016, 95(C): 51-67.

赵如意, 王晓辉, 郑碧煌, 等. 基于特征优化和混合改进灰狼算法优化BiLSTM网络的短期光伏功率预测[J]. 电网技术, 2025, 49(1): 209-222.

刘杰, 从兰美, 夏远洋, 等. 基于DBO-VMD和IWOA-BILSTM神经网络组合模型的短期电力负荷预测[J]. 电力系统保护与控制, 2024, 52(8): 123-133.

KUNDUR P.Power system stability and control[M]. New York: McGraw-Hill, 1994.

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