基于电力系统受扰后频率最低点预测的一次调频优化研究

张国斌; 沈烨昱; 霍红岩; 郭瑞君; 牛玉广; 柳双翠

doi:10.19912/j.0254-0096.tynxb.2023-2099

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (5) : 89-98. DOI: 10.19912/j.0254-0096.tynxb.2023-2099

基于电力系统受扰后频率最低点预测的一次调频优化研究

张国斌¹, 沈烨昱², 霍红岩¹, 郭瑞君¹, 牛玉广², 柳双翠³

作者信息 +

RESEARCH ON OPTIMIZATION OF PRIMARY FREQUENCY REGULATION BASED ON FREQUENCY NADIR PREDICTZON AFTER POWER SYSTEM DISTURBANCES

Zhang Guobin¹, Shen Yeyu², Huo Hongyan¹, Guo Ruijun², Niu Yuguang¹, Liu Shuangcui³

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摘要

为了提升新型电力系统下的频率稳定性,提出一种基于电力系统受扰后频率最低点预测的一次调频优化方法。首先为了准确预测电力系统受到扰动后的频率变化特征,分析频率偏差的产生机理,选取切机等功率不平衡事件的相关影响变量,结合极限学习机（ELM）,建立基于ELM神经网络的电力系统频率最低点预测模型,并采用蜣螂算法（DBO）对ELM优化输入权值和隐含层阈值,降低ELM随机生成参数的不稳定性。然后设计基于预测信号的一次调频优化策略。在IEEE 39节点上开展仿真试验,结果显示DBO-ELM算法在预测频率最低点时具有更快的计算速度、更强的泛化能力以及更高的预测精度,所提的一次调频优化策略能有效提升频率稳定性,可为电力系统大频差扰动提供解决方案。

Abstract

To improve the frequency stability of the new power system, this paper proposes a primary frequency regulation optimization method based on predicting the frequency nadir after system disturbances. Firstly, to accurately predict the frequency variation characteristics of the power system after disturbance, the mechanism of frequency deviation was analyzed, and relevant influencing variables of power imbalance events such as generator tripping, were selected. Combined with Extreme Learning Machine （ELM）, an ELM neural network-based frequency nadir prediction model was established, where the Dung Beetle Algorithm（DBO） was employed to optimize the input weights and hidden layer thresholds of the ELM, reducing the instability due to randomly generated parameters. Then, a primary frequency regulation optimization strategy based on predicted signals was designed. Simulation experiments were conducted on IEEE 39-bus system, and the results showed that the DBO-ELM algorithm has faster calculation speed, stronger generalization ability, and higher prediction accuracy when predicting the lowest point of frequency. The proposed primary frequency regulation optimization strategy effectively improved frequency stability and provided solutions for large frequency deviation disturbances in power systems.

导出引用

张国斌, 沈烨昱, 霍红岩, 郭瑞君, 牛玉广, 柳双翠. 基于电力系统受扰后频率最低点预测的一次调频优化研究[J]. 太阳能学报. 2025, 46(5): 89-98 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2099

Zhang Guobin, Shen Yeyu, Huo Hongyan, Guo Ruijun, Niu Yuguang, Liu Shuangcui. RESEARCH ON OPTIMIZATION OF PRIMARY FREQUENCY REGULATION BASED ON FREQUENCY NADIR PREDICTZON AFTER POWER SYSTEM DISTURBANCES[J]. Acta Energiae Solaris Sinica. 2025, 46(5): 89-98 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2099

中图分类号： TM712

参考文献

[1] 张君黎, 徐政. 考虑RoCoF约束的新能源电力系统惯量分区配置方法[J]. 太阳能学报, 2023, 44(9): 18-28.
ZHANG J L, XU Z.Regional inertia configuration method of renewable energy power system considering RoCoF constraint[J]. Acta energiae solaris sinica, 2023, 44(9): 18-28.
[2] 盛四清, 占志刚, 吴林林, 等. 考虑频率二次跌落的风电机组调频控制研究[J]. 太阳能学报, 2023, 44(8): 485-491.
SHENG S Q, ZHAN Z G, WU L L, et al.Research on frequency regulation control of wind turbines considering secondary frequency drop[J]. Acta energiae solaris sinica, 2023, 44(8): 485-491.
[3] 李铁成, 闫鹏, 胡雪凯, 等. 光伏高占比系统中储能辅助调频控制策略研究[J]. 太阳能学报, 2023, 44(8): 282-291.
LI T C, YAN P, HU X K, et al.Research on energy storage assisted frequency modulation control strategy in photovoltaic high duty cycle system[J]. Acta energiae solaris sinica, 2023, 44(8): 282-291.
[4] 于会群, 戚明鑫, 彭道刚, 等. 储能-火电联合一次调频的双层控制策略[J]. 热能动力工程, 2023, 38(6): 48-57.
YU H Q, QI M X, PENG D G, et al.Double-layer control strategy of combined primary frequency regulation for battery energy storage system and thermal power unit[J]. Journal of engineering for thermal energy and power, 2023, 38(6): 48-57.
[5] 殷建华, 张国斌, 贾斌, 等. 基于电网小频差考核的供热机组一次调频优化[J]. 内蒙古电力技术, 2022, 40(6): 78-82.
YIN J H, ZHANG G B, JIA B, et al.Optimization of heating unit primary frequency regulation based on small frequency deviation assessment of power grid[J]. Inner Mongolia electric power, 2022, 40(6): 78-82.
[6] 汪梦军, 郭剑波, 马士聪, 等. 新能源电力系统暂态频率稳定分析与调频控制方法综述[J]. 中国电机工程学报, 2023, 43(5): 1672-1694.
WANG M J, GUO J B, MA S C, et al.Review of transient frequency stability analysis and frequency regulation control methods for renewable power systems[J]. Proceedings of the CSEE, 2023, 43(5): 1672-1694.
[7] 谢开贵, 赵宇生, 胡博, 等. 考虑风电主动参与频率控制的电力系统运行可靠性评估[J]. 电网技术, 2023, 47(1): 41-54.
XIE K G, ZHAO Y S, HU B, et al.Operational reliability assessment of power system considering active participation of wind power in frequency control[J]. Power system technology, 2023, 47(1): 41-54.
[8] 方秋实, 于继来, 郭钰锋. 基于频率轨迹信息的电力系统等值惯性时间系数评估方法[J]. 电网技术, 2023, 47(2): 435-448.
FANG Q S, YU J L, GUO Y F.Evaluation of equivalent inertia time coefficient for power system based on frequency trajectory[J]. Power system technology, 2023, 47(2): 435-448.
[9] 罗启珩, 王晓茹, 刘金强, 等. 基于调速系统等值模型的电力系统发生扰动后最低频率预测[J]. 电力自动化设备, 2019, 39(10): 163-167.
LUO Q H, WANG X R, LIU J Q, et al.Minimum frequency prediction of power system after disturbance based on equivalent model of governor system[J]. Electric power automation equipment, 2019, 39(10): 163-167.
[10] LIU J Q, WANG X R, LIN J T, et al.A hybrid equivalent model for prediction of power system frequency response[C]//2018 IEEE Power & Energy Society General Meeting (PESGM), Portland, OR, USA, 2018: 1-5.
[11] 王琦, 李峰, 汤奕, 等. 基于物理-数据融合模型的电网暂态频率特征在线预测方法[J]. 电力系统自动化, 2018, 42(19): 1-9.
WANG Q, LI F, TANG Y, et al.On-line prediction method of transient frequency characteristics for power grid based on physical-statistical model[J]. Automation of electric power systems, 2018, 42(19): 1-9.
[12] 张英敏, 彭泽峰, 彭乔, 等. 预测新能源接入电网受扰后频率最低点的通用ASF模型[J]. 电网技术, 2023, 47(5): 1788-1799.
ZHANG Y M, PENG Z F, PENG Q, et al.Generic ASF model of new-energy-integrated power grid for frequency nadir estimation under disturbance[J]. Power system technology, 2023, 47(5): 1788-1799.
[13] 仉怡超, 闻达, 王晓茹, 等. 基于深度置信网络的电力系统扰动后频率曲线预测[J]. 中国电机工程学报, 2019, 39(17): 5095-5104, 5290.
ZHANG Y C, WEN D, WANG X R, et al.A method of frequency curve prediction based on deep belief network of post-disturbance power system[J]. Proceedings of the CSEE, 2019, 39(17): 5095-5104, 5290.
[14] WU Y K.Frequency stability for an island power system: developing an intelligent preventive-corrective control mechanism for an offshore location[J]. IEEE industry applications magazine, 2017, 23(2): 74-87.
[15] 薄其滨, 王晓茹, 刘克天. 基于v-SVR的电力系统扰动后最低频率预测[J]. 电力自动化设备, 2015, 35(7): 83-88.
BO Q B, WANG X R, LIU K T.Minimum frequency prediction based on v-SVR for post-disturbance power system[J]. Electric power automation equipment, 2015, 35(7): 83-88.
[16] XU Y, DAI Y Y, DONG Z Y, et al.Extreme learning machine-based predictor for real-time frequency stability assessment of electric power systems[J]. Neural computing and applications, 2013, 22(3): 501-508.
[17] 陆文安, 朱清晓, 李兆伟, 等. 基于卷积神经网络的新型电力系统频率特性预测方法[J]. 上海交通大学学报, 2024, 58(10): 1500-1512.
LU W A, ZHU Q X, LI Z W, et al.A prediction method of new power system frequency characteristics based on convolutional neural network[J]. Journal of Shanghai Jiao Tong University, 2024, 58(10): 1500-1512.
[18] 胡益, 王晓茹, 滕予非, 等. 基于多层支持向量机的交直流电网频率稳定控制方法[J]. 中国电机工程学报, 2019, 39(14): 4104-4118.
HU Y, WANG X R, TENG Y F, et al.Frequency stability control method of AC/DC power system based on multi-layer support vector machine[J]. Proceedings of the CSEE, 2019, 39(14): 4104-4118.
[19] 胡亚平, 聂涌泉, 何宇斌, 等. 基于ELM预测模型的高比例新能源电网改进频率控制策略[J]. 电网与清洁能源, 2022, 38(7): 98-106.
HU Y P, NIE Y Q, HE Y B, et al.Improved frequency control strategy for power grid with high proportion of renewable energy based on ELM prediction model[J]. Power system and clean energy, 2022, 38(7): 98-106.
[20] 汪梦军, 郭剑波, 马士聪, 等. 新能源电力系统暂态频率稳定分析与调频控制方法综述[J]. 中国电机工程学报, 2023, 43(5): 1672-1694.
WANG M J, GUO J B, MA S C, et al.Review of transient frequency stability analysis and frequency regulation control methods for renewable power systems[J]. Proceedings of the CSEE, 2023, 43(5): 1672-1694.
[21] 谢开贵, 赵宇生, 胡博, 等. 考虑风电主动参与频率控制的电力系统运行可靠性评估[J]. 电网技术, 2023, 47(1): 41-54.
XIE K G, ZHAO Y S, HU B, et al.Operational reliability assessment of power system considering active participation of wind power in frequency control[J]. Power system technology, 2023, 47(1): 41-54.
[22] 易佩, 景志滨, 徐飞, 等. 考虑频率安全约束的电力系统临界惯量计算[J]. 清华大学学报(自然科学版), 2022, 62(10): 1721-1729.
YI P, JING Z B, XU F, et al.Calculation of the critical inertia of apower system considering frequency security constraints[J]. Journal of Tsinghua University (science and technology), 2022, 62(10): 1721-1729.
[23] 李冠争, 李斌, 王帅, 等. 基于特征选择和随机森林的电力系统受扰后动态频率预测[J]. 电网技术, 2021, 45(7): 2492-2502.
LI G Z, LI B, WANG S, et al.Dynamic frequency prediction of power system post-disturbance based on feature selection and random forest[J]. Power system technology, 2021, 45(7): 2492-2502.
[24] 林进钿. 基于深度学习的电力系统扰动后动态频率特征预测[D]. 成都: 西南交通大学, 2019.
LIN J D.Prediction of dynamic frequency characteristics of power system after disturbance based on deep learning[D]. Chengdu: Southwest Jiaotong University, 2019.
[25] 仉怡超. 基于深度学习的电力系统扰动后动态频率预测[D]. 成都: 西南交通大学, 2020.
ZHANG Y C.Dynamic frequency prediction of power system after disturbance based on deep learning[D]. Chengdu: Southwest Jiaotong University, 2020.
[26] 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.
[27] 盛举, 贾庆岩, 孙建军, 等. 基于多尺度形态学滤波的火电机组一次调频控制方法[J]. 电力自动化设备, 2022, 42(2): 194-200.
SHENG J, JIA Q Y, SUN J J, et al.Primary frequency regulation control method of thermal power unit based on multi-scale morphological filter[J]. Electric power automation equipment, 2022, 42(2): 194-200.
[28] 张小科, 王子杰, 夏大伟, 等. 一种面向深度调峰运行火电机组的一次调频能力建模新方法[J]. 电网技术, 2022, 46(12): 4947-4953.
ZHANG X K, WANG Z J, XIA D W, et al.New modeling for primary frequency regulation capability of thermal power units under deep peak regulation[J]. Power system technology, 2022, 46(12): 4947-4953.