Over‑limit short‑circuit current evaluation strategy for power grid with high penetration of renewable energy by combining data‑driven and model‑driven method
CSTR:
Author:
Affiliation:

(1.Economic and Technical Research Institute,State Grid Hubei Electric Power Co.,Ltd., Wuhan 430077, China; 2.College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China; 3.State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

Clc Number:

TM713

  • Article
  • | |
  • Metrics
  • |
  • Reference [21]
  • | | | |
  • Comments
    Abstract:

    The problem of over?limit short?circuit current in power grid containing renewable energy is becoming increasingly serious. Because of its faster and larger state changes, offline over?limit short?circuit current analysis may not be able to cover all the over?limit scenarios. Therefore, online analysis is quite necessary. Considering that the mainstream physical model calculation method can hardly meet the online calculation speed demand, faster calculation is of great significance. Therefore, this paper proposes a strategy of combining data?driven and model?driven method for over?limit short?circuit current evaluation for power grid with high penetration of renewable energy. Firstly, based on the analysis of the main factors affecting the short?circuit current, in order to improve the calculation speed, the original dimension is reduced to consider only the influence of load. Then, the optimal power flow and random simulation methods are combined to generate a large set of samples, and the data?driven model is obtained through machine learning algorithm training. On this basis, the error analysis and threshold setting of the model are carried out by using false positive rate and false negative rate. Then, the data?driven model is used to screen over?limit short?circuit current scenarios; Finally, the theoretical physical model proposed in the latest research is used to verify the short?circuit current scenario after preliminary screening with high accuracy. It is verified on the IEEE 39 bus model with photovoltaic power supply. The simulation results show that this strategy can effectively improve the verification speed without omitting the over?limit short?circuit current scenarios.

    Reference
    [1] 魏繁荣,马啸,林湘宁,等.应对不可预知型短路电流超标的主动响应型站域保护策略[J].中国电机工程学报,2018,38(2):484?496. WEI Fanrong,MA Xiao,LIN Xiangning,et al.Strategy on the active response substation integrated protection for dealing with the unpredictable excessive short?circuit current[J].Proceedings of the CSEE,2018,38(2):484?496.
    [2] 杨振纲,李力,李扬絮,等.广东电网短路电流超标问题及对策[J].南方电网技术,2011,5(5):90?93. YANG Zhengang,LI Li,LI Yangxu,et al.The issue of short?circuit current being out of limitation in guangdong power grid and related countermeasures[J].Southern Power System Technology,2011,5(5):90?93.
    [3] 傅旭,李想,王笑飞.新能源发电接入对电网短路电流的影响研究[J].分布式能源,2018,3(1):58?63. FU Xu,LI Xiang,WANG Xiaofei.Short circuit current of electric power system with new energy power generation[J].Distributed Energy,2018,3(1):58?63.
    [4] 南东亮,王维庆,彭寅章,等.故障限流器在新能源并网中最佳安装位置和参数配置研究[J].太阳能学报,2022,43(1):307?312. NAN Dongliang,WANG Weiqing,PENG Yinzhang,et al.Research on optimum installation position and parameter configuration of fault current limiter in new energy grid[J].Acta Energiae Solaris Sinica,2022,43(1):307?312.
    [5] 郑超铭,陈义宣,李玲芳.大规模新能源接入对云南电网短路电流影响分析[J].云南电力技术,2021,49(4):21?23+28. ZHENG Chaoming,CHEN Yixuan,Li Lingfang,et al.Analysis of the impact of large?scale new energy integration on the short?circuit current of yunnan power grid[J].Yunnan Electric Power,2021,49(4):21?23+28.
    [6] 贾科,侯来运,毕天姝,等.基于故障域局部迭代的工程实用化新能源短路电流计算[J].电力系统自动化,2021,45(13):151?158. JIA Ke,HOU Laiyun,BI Tianshu,et al.Practical short?circuit current engineering calculation for renewable energy based on local iteration in fault area[J].Automation of Electric Power Systems,2021,45(13):151?158.
    [7] 匡晓云,方煜,关红兵,等.适用于含新能源逆变电源网络的全时域短路电流计算方法[J].电力自动化设备,2020,40(5):113?122. KUANG Xiaoyun,FANG Yu,GUAN Hongbing,et al.Full?time domain short circuit current calculation method suitable for power network with inverter?interfaced renewable energy source[J].Electric Power Automation Equipment,2020,40(5):113?122.
    [8] 刘慧媛,肖繁,张哲,等.新能源电源接入不平衡配电网的短路计算方法[J].电力系统自动化,2019,43(21):177?186. LIU Huiyuan,XIAO Fan,ZHANG Zhe,et al.Short?circuit calculation method for unbalanced distribution network with integration of renewable energy[J].Automation of Electric Power Systems,2019,43(21):177?186.
    [9] 乔黎伟,王静怡,郭炜,等.基于随机森林算法的中短期用电量预测[J].电力科学与技术学报,2020,35(2):150?156. QIAO Liwei,WANG Jingyi,GUO Wei,et al.Medium and short term electricity demand prediction based on random forests algorithm.Journal of Electric Power Science and Technology,2020,35(2):150?156.
    [10] 赵洋,王瀚墨,康丽,等.基于时间卷积网络的短期电力负荷预测[J].电工技术学报,2022,37(5):1242?1251. ZHAO Yang,WANG Hanmo,KANG Li,et al.Temporal convolution network?based short?term electrical load forecasting[J].Transactions of China Electrotechnical Society,2022,37(5):1242?1251.
    [11] 厉仄平,伍冲翀,熊来红,等.基于机器学习和雷电感应电压的输电线路雷击定位方法[J].高压电器,2022,58(12):109?116. LI Zeping,WU Chongchong,XIONG Laihong,et al.Lightning location method for transmission lines based on machine learning and lightning induced voltage[J].High Voltage Apparatus,2022,58(12):109?116.
    [12] 周楠,张平,郑征,等.基于机器学习的电力通信网带宽分配算法[J].电网与清洁能源,2021,37(5):67?73. ZHOU Nan, ZHANG Ping, ZHENG Zheng,et al.Bandwidth allocation algorithm for power communication network based on machine learning[J]. Power System and Clean Energy,2021,37(5):67?73.
    [13] YANG L T,YANG H G.Analysis of different neural networks and a new architecture for short?term load forecasting[J].Energies,2019, 12(8):1433?1455.
    [14] LIU T X,JIN Y,GAO Y Y.A new hybrid approach for short?term electric load forecasting applying support vector machine with ensemble empirical mode decomposition and whale optimization[J]. Energies,2019,12(8):1520?1539.
    [15] 王子晗,高红均,高艺文,等.基于深度强化学习的城市配电网多级动态重构优化运行方法[J].电力系统保护与控制,2022,50(24):60?70. WANG Zihan,GAO Hongjun,GAO Yiwen,et al.Multi?level dynamic reconfiguration and operation optimization method for an urbandistribution network based on deep reinforcement learning[J].Power System Protection and Control,2022,50(24):60?70.
    [16] 任柯政,徐泰山,郭瑾程,等.数据驱动的新型电力系统断面极限估算方法[J].智慧电力,2022,50(11):9?16. REN Kezheng,XU Taishan,GUO Jincheng,et al.A new data?driven method for estimating the section limit of power system[J].Smart Power,2022,50(11):9?16.
    [17] 郑翔,王慧芳,姜宽,等.机理与数据融合驱动的含IIDG配电网短路电流计算方法[J].电力自动化设备,2021,41(1):41?48. ZHENG Xiang,WANG Huifang,JIANG Kuan,et al.Calculation method of short circuit current in distribution network with IIDG driven by mechanism and data fusion[J].Electric Power Automation Equipment,2021,41(1):41?48.
    [18] 李峰,王琦,胡健雄,等.数据与知识联合驱动方法研究进展及其在电力系统中应用展望[J].中国电机工程学报,2021,41(13):4377?4390. LI Feng,WANG Qi,HU Jianxiong,et al.Combined data?driven and knowledge?driven methodology research advances and its applied prospect in power systems[J].Proceedings of the CSEE,2021,41(13):4377?4390.
    [19] WANG Q,LI F,TANG Y,et al.Integrating model?driven and data?driven methods for power system frequency stability assessment and control[J].IEEE Transactions on Power Systems,2019,34(6):4557?4568.
    [20] 张涵,王程,毕天姝.融合物理与数据知识的电力系统扰动后频率在线快速计算方法[J].电网技术,2022,46(11):4325?4335. ZHANG Han,WANG Cheng,BI Tianshu.On?line fast frequency calculation method after power system disturbance based on fusion of physics and data knowledge[J].Power System Technology,2022,46(11):4325?4335.
    [21] ZIMMERMAN R D,MURILLO?SANCHEZ C E,THOMAS R J.MATPOWER:steady?state operations, planning and analysis tools for power systems research and education[J].IEEE Transactions on Power Systems,2011,26(1):12?19.
    Related
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation

熊 志,章谋成,姚 伟,乔 立,赵红生,刘 巨,王 博.数据—物理融合驱动的含新能源电网短路电流超标评估策略[J].电力科学与技术学报英文版,2023,38(4):24-34. XIONG Zhi, ZHANG Moucheng, YAO Wei, QIAO Li, ZHAO Hongsheng, LIU Ju, WANG Bo. Over‑limit short‑circuit current evaluation strategy for power grid with high penetration of renewable energy by combining data‑driven and model‑driven method[J]. Journal of Electric Power Science and Technology,2023,38(4):24-34.

Copy
Share
Article Metrics
  • Abstract:275
  • PDF: 1307
  • HTML: 0
  • Cited by: 0
History
  • Online: November 06,2023
Article QR Code