上海交通大学学报

上海交通大学学报 >

2024 , Vol. 58 >Issue 1: 40 - 49

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2022.370

新型电力系统与综合能源

基于PMU梯度动态偏差的新型电力系统快速稳定性

展开
  • 1.北京建筑大学 机电与车辆工程学院,北京 100044
    2.北京市建筑安全检测工程技术研究中心, 北京100044
    3.清华大学 电力系统及发电设备安全控制和仿真国家重点实验室,北京 100084
于 淼(1982-),博士,副教授,现主要从事电力系统辨识与控制研究.

收稿日期: 2022-09-23

  修回日期: 2022-12-08

  录用日期: 2022-12-30

  网络出版日期: 2023-04-27

基金资助

清华大学电力系统及大型发电设备安全控制与仿真国家重点实验室基金资助项目(SKLD20M17);北京市高等教育学会项目(YB2021131);北京建筑大学金字塔人才培养工程项目(JDYC20200324);北京建筑大学研究生创新项目(PG2022132);国家级大学生创新创业训练计划项目(202110016052);国家自然科学基金委青年科学基金资助项目(51407201)

收起

Fast Stability of New Power System Based on a PMU Gradient Dynamic Deviation Method

Expand
  • 1. School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
    2. Beijing Engineering Research Center of Monitoring for Construction Safety, Beijing 100044, China
    3. State Key Laboratory of Control and Simulation of Power System and Generation Equipment, Tsinghua University, Beijing 100084, China

Received date: 2022-09-23

  Revised date: 2022-12-08

  Accepted date: 2022-12-30

  Online published: 2023-04-27

Fold

摘要

在能源转型和科技进步双重推动下,高比例可再生能源和高比例电力电子设备正成为电力系统发展重要趋势和关键特征.新型电力系统动态行为随之发生重大变化,传统小干扰稳定性分析方法满足使用需要,在应对运行工况快速变化场合时尚存在亟需解决问题.提出基于同步相量测量装置(PMU)数据梯度动态偏差李雅普诺夫直接分析法对新型电力系统小干扰稳定性进行分析,利用PMU数据矩阵降维得到低维矩阵,代入含双馈异步风力发电机组的电力系统矩阵模型,求解李雅普诺夫方程式得到对角矩阵,通过判定矩阵正定性对系统稳定性作出判断.利用求解后得到的对角矩阵计算相应状态变量动态偏差值,对相应状态变量曲线使用梯度下降法迭代计算该曲线极值点数值,时间加权计算整个振荡过程时间加权动态偏差量,为阻尼稳定控制器配置位置提供指导.利用含风力发电新英格兰10机39节点系统仿真验证方法可以达到改善系统小干扰稳定性以及对新型电力系统快速稳定性分析的有效性.

关键词： 同步相量测量装置数据; 新型电力系统; 稳定性分析; 李雅普诺夫直接法; 梯度动态偏差

本文引用格式

于淼, 胡敬轩, 张寿志, 魏静静, 孙建群, 吴屹潇 . 基于PMU梯度动态偏差的新型电力系统快速稳定性[J]. 上海交通大学学报, 2024 , 58(1) : 40 -49 . DOI: 10.16183/j.cnki.jsjtu.2022.370

Abstract

The high proportion of renewable energy and power electronic equipment is emerging as a significant trend and key characteristic of the power system development driven by the dual promotion of the energy transformation and scientific technological advancement. Major modifications have been made to the dynamic behavior of the new power system. The traditional small signal stability analysis approach is difficult to apply, and there are still urgent issues to be resolved for the quick change of operating conditions. In this paper, a Lyapunov direct analysis method of gradient dynamic deviation based on phasor measurement unit (PMU) data is proposed to analyze the small signal stability of the new power system. First, the PMU data matrix is used to reduce the dimension to obtain the low dimension matrix, which is substituted into the power system matrix model with a doubly-fed induction generator (DFIG). The diagonal matrix is obtained by solving the Lyapunov equation, and the positive definiteness of the matrix is determined to judge the system stability. Then, the dynamic deviation of corresponding state variable is calculated by solving the obtained diagonal matrix. The gradient descent method is applied to the corresponding state variable curve to iterate the extreme point value of curve. The time-weighted dynamic deviation of the whole oscillation process is calculated by time weighting, which provides guidance for the subsequent configuration position of damping stability controller, i.e., power system stabilizer (PSS). The method can improve the small interference stability of the system. The effectiveness of the fast stability analysis of the new power system is verified by simulations of the new England 10-machine 39-bus system with DFIG.

Key words： phasor measurement unit (PMU) data; new power system; stability analysis; Lyapunov direct method; gradient dynamic deviation

参考文献

[1] QUINT R, DANGELMAIER L, GREEN I, et al. Transformation of the grid: The impact of distributed energy resources on bulk power systems[J]. IEEE Power and Energy Magazine, 2019, 17(6): 35-45.
[2] BLONDEL V D, TSITSIKLIS J N. A survey of computational complexity results in systems and control[J]. Automatica, 2000, 36(9): 1249-1274.
[3] LEW D, BARTLETT D, GROOM A, et al. Secrets of successful integration: Operating experience with high levels of variable, inverter-based generation[J]. IEEE Power and Energy Magazine, 2019, 17(6): 24-34.
[4] 肖先勇, 郑子萱. “双碳”目标下新能源为主体的新型电力系统: 贡献、关键技术与挑战[J]. 工程科学与技术, 2022, 54(1): 47-59.
[4] XIAO Xianyong, ZHENG Zixuan. New power systems dominated by renewable energy towards the goal of emission peak & carbon neutrality: Contribution, key techniques, and challenges[J]. Advanced Engineering Sciences, 2022, 54(1): 47-59.
[5] 侯验秋, 丁一, 包铭磊, 等. 电-气耦合视角下德州大停电事故分析及对我国新型电力系统发展启示[J]. 中国电机工程学报, 2022, 42(21): 7764-7775.
[5] HOU Yanqiu, DING Yi, BAO Minglei, et al. Analysis of texas blackout from the perspective of electricity-gas coupling and its enlightenment to the development of China’s new power system[J]. Proceedings of the CSEE, 2022, 42(21): 7764-7775.
[6] 黄强, 郭怿, 江建华, 等. “双碳”目标下中国清洁电力发展路径[J]. 上海交通大学学报, 2021, 55(12): 1499-1509.
[6] HUANG Qiang, GUO Yi, JIANG Jianhua, et al. Development pathway of China’s clean electricity under carbon peaking and carbon neutrality goals[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1499-1509.
[7] 谢小荣, 贺静波, 毛航银, 等. “双高”电力系统稳定性的新问题及分类探讨[J]. 中国电机工程学报, 2021, 41(2): 461-475.
[7] XIE Xiaorong, HE Jingbo, MAO Hangyin, et al. New issues and classification of power system stability with high shares of renewables and power electronics[J]. Proceedings of the CSEE, 2021, 41(2): 461-475.
[8] 杨鹏, 刘锋, 姜齐荣, 等. “双高”电力系统大扰动稳定性: 问题、挑战与展望[J]. 清华大学学报(自然科学版), 2021, 61(5): 403-414.
[8] YANG Peng, LIU Feng, JIANG Qirong, et al. Large-disturbance stability of power systems with high penetration of renewables and inverters: Phenomena, challenges, and perspectives[J]. Journal of Tsinghua University (Science and Technology), 2021, 61(5): 403-414.
[9] 马宁宁, 谢小荣, 唐健, 等. “双高”电力系统宽频振荡广域监测与预警系统[J]. 清华大学学报(自然科学版), 2021, 61(5): 457-464.
[9] MA Ningning, XIE Xiaorong, TANG Jian, et al. Wide-area measurement and early warning system for wide-band oscillations in “double-high” power systems[J]. Journal of Tsinghua University (Science and Technology), 2021, 61(5): 457-464.
[10] 侯凯元, 闵勇, 张瑞琪. 电力系统暂态稳定域边界局部近似方法的研究[J]. 中国电机工程学报, 2004, 24(1): 1-5.
[10] HOU Kaiyuan, MIN Yong, ZHANG Ruiqi. Local approximation of transient stability boundary of the power systems[J]. Proceedings of the CSEE, 2004, 24(1): 1-5.
[11] 吴小珊, 李崇涛, 曾柯寒, 等. 基于矩阵束的电力系统小干扰稳定性分析模型及其特征分析方法[J]. 南方电网技术, 2020, 14(7): 39-45.
[11] WU Xiaoshan, LI Chongtao, ZENG Kehan, et al. Small signal stability analysis model of power system and its eigen-analysis method based on matrix pencil[J]. Southern Power System Technology, 2020, 14(7): 39-45.
[12] 易铭, 黄云辉, 朱当, 等. 基于弱电网的光伏储能系统小干扰稳定性分析[J]. 智慧电力, 2021, 49(2): 69-75.
[12] YI Ming, HUANG Yunhui, ZHU Dang, et al. Small signal stability analysis of photovoltaic energy storage system under weak grid[J]. Smart Power, 2021, 49(2): 69-75.
[13] 孙华东, 方诗卉, 徐式蕴, 等. 基于Nyquist阵列理论的风电并网系统小扰动稳定分析及控制[J]. 中国电机工程学报, 2020, 40(10): 3124-3134.
[13] SUN Huadong, FANG Shihui, XU Shiyun, et al. Power system small signal stability analysis and control based on nyquist array theory[J]. Proceedings of the CSEE, 2020, 40(10): 3124-3134.
[14] 高振, 雷为民, 赵峰, 等. 基于Lyapunov直接法的虚拟同步发电机并网暂态稳定性分析[J]. 浙江电力, 2019, 38(10): 52-60.
[14] GAO Zhen, LEI Weimin, ZHAO Feng, et al. Transient stability analysis on virtual synchronous generators integration based on Lyapunov’s direct method[J]. Zhejiang Electric Power, 2019, 38(10): 52-60.
[15] 袁小明, 何维. 动态过程的幅频调制统一本质与系统稳定问题分类及新能源发电构网能力创新[J]. 电源学报, 2021, 19(6): 1-9.
[15] YUAN Xiaoming, HE Wei. Amplitude/frequency as prerequisites of operation and thus classification of stability problems and capability opportunities for new generations[J]. Journal of Power Supply, 2021, 19(6): 1-9.
[16] 刘宪林, 丁超杰, 王子琦, 等. 电力系统小扰动稳定的直接法分析[J]. 电力自动化设备, 2011, 31(7): 1-4.
[16] LIU Xianlin, DING Chaojie, WANG Ziqi, et al. Direct method to analyze small signal stability of electric power systems[J]. Electric Power Automation Equipment, 2011, 31(7): 1-4.
[17] 宫泽旭, 艾力西尔·亚尔买买提, 辛焕海, 等. 新能源电力系统并网设备小扰动稳定分析(一): 机理模型与稳定判据适用性[J]. 中国电机工程学报, 2022, 42(12): 4405-4419.
[17] GONG Zexu, YAERMAIMAITI Ailixier, XIN Huan-hai, et al. Small signal stability analysis of equipment in renewable energy power system(Part I): Mechanism model and adaptation of stability criterion[J]. Proceedings of the CSEE, 2022, 42(12): 4405-4419.
[18] 贾继灏, 马丽丽, 刘宪林. 基于等值单机无穷大系统的多机系统PSS设计[J]. 电力系统保护与控制, 2009, 37(23): 70-74.
[18] JIA Jihao, MA Lili, LIU Xianlin. PSS design in a multi-machine system based on equivalent single machine infinite bus power system[J]. Power System Protection and Control, 2009, 37(23): 70-74.
[19] 邵昊舒, 蔡旭. 大型风电机组惯量控制研究现状与展望[J]. 上海交通大学学报, 2018, 52(10): 1166-1177.
[19] SHAO Haoshu, CAI Xu. Research status and prospect of inertia control for large scale wind turbines[J]. Journal of Shanghai Jiao Tong University, 2018, 52(10): 1166-1177.
[20] 徐晋, 汪可友, 李国杰, 等. 随机扰动下的电力系统强迫振荡分析[J]. 上海交通大学学报, 2017, 51(5): 563-569.
[20] XU Jin, WANG Keyou, LI Guojie, et al. Analysis of forced power oscillation under stochastic disturbance[J]. Journal of Shanghai Jiao Tong University, 2017, 51(5): 563-569.
[21] 伊恩·古德费洛, 约书亚·本吉奥, 亚伦·库维尔. 深度学习[M]. 北京: 人民邮电出版社, 2017.
[21] IAN Goodfellow, YOSHUA Bengio, AARON Courville. Deep learning[M]. Beijing: Posts & Telecom Press, 2017.
[22] DEHGHANI M, NIKRAVESH S K Y. State-space model parameter identification in large-scale power systems[J]. IEEE Transactions on Power Systems, 2008, 23(3): 1449-1457.
[23] 倪以信, 陈寿孙, 张宝霖. 动态电力系统的理论和分析[M]. 北京: 清华大学出版社, 2002.
[23] NI Yixin, CHEN Shousun, ZHANG Baolin. Theory and analysis of dynamic power system dynamics[M]. Beijing: Tsinghua University Press, 2002.
[24] 贾鹤鸣, 李瑶, 孙康健. 基于遗传乌燕鸥算法的同步优化特征选择[J]. 自动化学报, 2022, 48(6): 1601-1615.
[24] JIA Heming, LI Yao, SUN Kangjian. Simultaneous feature selection optimization based on hybrid sooty tern optimization algorithm and genetic algorithm[J]. Acta Automatica Sinica, 2022, 48(6): 1601-1615.
[25] 任斌, 程良伦. 李雅普诺夫稳定性理论中V函数的构造研究[J]. 自动化与仪器仪表, 2009(2): 8-10.
[25] REN Bin, CHENG Lianglun. The structure of V function in Lyapunov’s stableness theory[J]. Automation & Instrumentation, 2009(2): 8-10.
[26] YU M, LI J L, ZHANG S Z. Probabilistic stability of small disturbance in wind power system based on a variational Bayes and Lyapunov theory using PMU data[J]. IET Generation, Transmission & Distribution, 2022, 16(23): 4818-4829.
[27] 和萍. 风电并网对电力系统稳定性的影响与控制[M]. 北京: 科学出版社, 2019.
[27] HE Ping. Influence and control of wind power grid connection on power system stability[M]. Beijing: Science Press, 2019.
Options
文章导航

/

〈 〉