基于PEF-PinSVM的含可再生能源并网电力系统电压暂态稳定裕度评估

李汐, 高阳, 马斌, 于春雨, 韩冬军, 周强

太阳能学报 ›› 2024, Vol. 45 ›› Issue (8) : 240-248.

PDF(1139 KB)
欢迎访问《太阳能学报》官方网站,今天是
PDF(1139 KB)
太阳能学报 ›› 2024, Vol. 45 ›› Issue (8) : 240-248. DOI: 10.19912/j.0254-0096.tynxb.2023-0642

基于PEF-PinSVM的含可再生能源并网电力系统电压暂态稳定裕度评估

  • 李汐1, 高阳1, 马斌1, 于春雨2, 韩冬军3, 周强3
作者信息 +

VOLTAGE TRANSIENT STABILITY MARGIN ASSESSMENT OF GRID CONNECTED POWER SYSTEMS CONTAINING RENEWABLE ENERGY BASED ON PEF-PinSVM

  • Li Xi1, Gao Yang1, Ma Bin1, Yu Chunyu2, Han Dongjun3, Zhou Qiang3
Author information +
文章历史 +

摘要

为精准掌握含可再生能源并网电力系统电压暂态稳定性,提出一种改进弹球损失支持向量机的评估方法。首先,结合系统特征与投影能量函数指标相关理论建立电力系统暂态稳定裕度的原始特征集,随后借助最大相关最小冗余特征选择法,筛选出衡量并网电力系统电压暂态稳定裕度的特征子集,以表征可再生能源并网对电力系统电压暂态稳定裕度的影响;其次,采用分位数方法将系统划分为稳定类与不稳定类,并将电压暂态稳定裕度分类问题转换为在PEF-PinSVM中寻找最优分位数距离问题,减小可再生能源并网波动性与不确定性对系统电压暂态稳定裕度评估的影响;最后,以IEEE-39节点系统进行算例仿真,发现含可再生能源并网电力系统电压暂态稳定裕度评估准确率会伴随可再生能源渗透率的增加而提升。

Abstract

To accurately grasp the transient stability of voltage in grid connected power systems containing renewable energy, this paper proposes an improved evaluation method based on the support vector machine for ball loss. Firstly, based on the theory of system characteristics and projection energy function indicators, establish the original feature set of power system transient stability margin; Subsequently, using the maximum correlation minimum redundancy feature selection method, a subset of features that measure the transient stability margin of the grid connected power system voltage is selected to characterize the impact of renewable energy grid connection on the transient stability margin of the power system voltage; Thirdly, the quantile method is used to divide the system into stable and unstable classes, and the classification problem of voltage transient stability margin is transformed into the problem of finding the optimal quantile distance in PEF PinSVM, reducing the impact of renewable energy grid connection volatility and uncertainty on the evaluation of system voltage transient stability margin; Finally, using the IEEE39 node system as an example simulation, it is found that the accuracy of voltage transient stability margin assessment in grid connected power systems containing renewable energy is imporved with the increase of renewable energy penetration rate.

关键词

暂态稳定 / 可再生能源 / 电力系统 / 支持向量机 / 电网

Key words

transient stability / renewable energy / electric power system / support vector machine / power grid

引用本文

导出引用
李汐, 高阳, 马斌, 于春雨, 韩冬军, 周强. 基于PEF-PinSVM的含可再生能源并网电力系统电压暂态稳定裕度评估[J]. 太阳能学报. 2024, 45(8): 240-248 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0642
Li Xi, Gao Yang, Ma Bin, Yu Chunyu, Han Dongjun, Zhou Qiang. VOLTAGE TRANSIENT STABILITY MARGIN ASSESSMENT OF GRID CONNECTED POWER SYSTEMS CONTAINING RENEWABLE ENERGY BASED ON PEF-PinSVM[J]. Acta Energiae Solaris Sinica. 2024, 45(8): 240-248 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0642
中图分类号: TM712   

参考文献

[1] 卢纯. 开启我国能源体系重大变革和清洁可再生能源创新发展新时代:深刻理解碳达峰、碳中和目标的重大历史意义[J]. 人民论坛·学术前沿, 2021, 10(14): 28-41.
LU C.Opening a new era of major changes in China’s energy system and innovative development of clean and renewable energy—deeply understanding the great historical significance of the targets of carbon peak and carbon neutralization[J]. Frontiers, 2021,10(14): 28-41.
[2] 李竹, 宋莉, 于松泰, 等. 促进可再生能源市场化的省内中长期运行策略研究[J]. 太阳能学报, 2023, 44(2):317-325.
LI Z, SONG L, YU S T, et al.Research on medium and long-term operation strategy of promoting marketization of renewable energy in province[J]. Acta energiae solaris sinica, 2023, 44(2): 317-325.
[3] 刘信彤, 辛业春, 王长江, 等. 基于Smo-PinSVM的含新能源电力系统暂态稳定评估[J]. 太阳能学报, 2021, 42(5): 98-104.
LIU X T, XIN Y C, WANG C J, et al.Transient stability assessment in bulk power grid with renewable energy using Smo-PinSVM[J]. Acta energiae solaris sinica, 2021, 42(5): 98-104.
[4] 史法顺, 吴俊勇, 吴昊衍, 等. 基于深度学习的电力系统暂态功角与暂态电压稳定裕度一体化评估[J]. 电网技术, 2023, 47(2): 731-740.
SHI F S, WU J Y, WU H Y, et al.Integrated evaluation of power system transient power angle and transient voltage stability margin based on deep learning[J]. Power system technology, 2023, 47(2): 731-740.
[5] 王强, 刘炼, 陈浩. 基于关系探索和KTBoost的暂态稳定裕度评估[J]. 电力系统及其自动化学报, 2022, 34(8): 35-43.
WANG Q, LIU L, CHEN H.Transient stability margin assessment based on relationship exploration and KTBoost[J]. Proceedings of the CSU-EPSA, 2022, 34(8): 35-43.
[6] 肖繁, 王涛, 饶渝泽, 等. 基于梯度提升决策树静态电压稳定裕度评估[J]. 电测与仪表, 2020, 57(20): 39-45.
XIAO F, WANG T, RAO Y Z, et al.Static voltage stability margin evolution based on gradient boosting regression tree[J]. Electrical measurement & instrumentation, 2020, 57(20): 39-45.
[7] 宓登凯, 王彤, 相禹维, 等. 基于Elastic Net的暂态稳定裕度在线评估[J]. 电网技术, 2020, 44(1): 19-26.
MI D K, WANG T, XIANG Y W, et al.Elastic Net based online assessment of power system transient stability margin[J]. Power system technology, 2020, 44(1): 19-26.
[8] 梁辰, 刘道伟, 焦彦军. 基于改进动态阻抗法的电网静态电压稳定裕度快速评估[J]. 电力系统保护与控制, 2017, 45(24): 44-49.
LIANG C, LIU D W, JIAO Y J.Rapid evaluation of power system static voltage stability margin based on improved dynamic impedance[J]. Power system protection and control, 2017, 45(24): 44-49.
[9] 孔祥超, 尹星月, 张述铭, 等. 基于ANN模型的在线电压稳定裕度评估[J]. 电测与仪表, 2015, 52(5): 20-24.
KONG X C, YIN X Y, ZHANG S M, et al.Online evaluating of voltage stability margin based on artificial neural network[J]. Electrical measurement & instrumentation, 2015, 52(5): 20-24.
[10] HUANG X L, SHI L, SUYKENS J A K. Support vector machine classifier with pinball loss[J]. IEEE transactions on pattern analysis and machine intelligence, 2014, 36(5): 984-997.
[11] JUMUTC V, HUANG X L, SUYKENS J A K. Fixed-size Pegasos for hinge and pinball loss SVM[C]//The 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX, USA, 2013: 1-7.
[12] 潘雄, 张龙, 黄家栋, 等. 基于Sobol序列和混合Copula的含风电和光伏电力系统暂态稳定分析[J]. 太阳能学报, 2015, 36(7): 1622-1631.
PAN X, ZHANG L, HUANG J D, et al.Transient stability analysis of power system integrated with wind farms and photovoltaic plants based on Sobol sequence and mixed-Copula[J]. Acta energiae solaris sinica, 2015, 36(7): 1622-1631.
[13] 刘俊, 孙惠文, 吴柳, 等. 电力系统暂态稳定性评估综述[J]. 智慧电力, 2019, 47(12): 44-53, 122.
LIU J, SUN H W, WU L, et al.Overview of transient stability assessment of power systems[J]. Smart power, 2019, 47(12): 44-53, 122.
[14] 蔡霁霖, 郝丽丽, 张柯琪. 含可再生能源电力系统可靠性评估的非参数重要性分层抽样法[J]. 电力系统自动化, 2022, 46(7): 104-115.
CAI J L, HAO L L, ZHANG K Q.Non-parametric stratified importance sampling method for reliability evaluation of power system with renewable energy[J]. Automation of electric power systems, 2022, 46(7): 104-115.
[15] 叶家豪, 魏霞, 黄德启, 等. 基于灰色关联分析的BSO-ELM-AdaBoost风电功率短期预测[J]. 太阳能学报, 2022, 43(3): 426-432.
YE J H, WEI X, HUANG D Q, et al.Short-term forecast of wind power based on BSO-ELM-AdaBoost with grey correlation analysis[J]. Acta energiae solaris sinica, 2022, 43(3): 426-432.

基金

国网青海省电力公司科技项目(52280719003P)

PDF(1139 KB)

Accesses

Citation

Detail

段落导航
相关文章

/