Identification Method for Control Parameters of Doubly-Fed Induction Generator Based on LSTM Neural Network

doi:10.11930/j.issn.1004-9649.202208091

Abstract

Abstract: Since it is difficult to obtain highly accurate control parameters of the electromagnetic model of a doubly-fed induction generator (DFIG) under transient conditions, a high precision identification method for the control parameters of DFIG based on long short-term memory (LSTM) neural network was proposed. Firstly, the RT-LAB hardware-in-the-loop (HIL) simulation platform was used to measure and obtain the HIL data of the DFIG controller, and the identification model of DFIG was built in the Plecs platform. Secondly, the Person correlation coefficient method was used to extract highly correlated features and train the neural network. Finally, the proposed LSTM neural network was used to identify the control parameters of DFIG and compare them with the measured data. As a result, the feasibility, effectiveness, and practicability of the proposed method were verified. The results show that compared with the traditional identification methods, the proposed parameter identification method based on LSTM neural network can effectively improve the identification accuracy of the control parameters of the electromagnetic model of DFIGs under transient conditions.

Key words: doubly-fed induction generator, hard-in-the-loop, parameter identification, long short-term memory network, Person correlation coefficient method

XUE Fei, LI Hongqiang, LI Xutao, XU Hengshan. Identification Method for Control Parameters of Doubly-Fed Induction Generator Based on LSTM Neural Network[J]. Electric Power, 2023, 56(6): 31-39.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202208091

https://www.electricpower.com.cn/EN/Y2023/V56/I6/31

References

[1] 张兴, 孙艳霞, 李丽娜, 等. 风电机组电磁暂态建模及验证[J]. 中国电力, 2020, 53(7): 106–112
ZHANG Xing, SUN Yanxia, LI Lina, et al. Electromagnetic transient modelling and verifying of wind turbine generator[J]. Electric Power, 2020, 53(7): 106–112
[2] JIN Y Q, LU C J, JU P, et al. Probabilistic preassessment method of parameter identification accuracy with an application to identify the drive train parameters of DFIG[J]. IEEE Transactions on Power Systems, 2020, 35(3): 1769–1782.
[3] 李振垚, 摆世彬, 方若水, 等. 基于广域测量系统的双馈风力发电机组在线参数辨识方法[J]. 南方电网技术, 2021, 15(5): 89–97
LI Zhenyao, BAI Shibin, FANG Ruoshui, et al. Online parameter identification method of doubly fed induction generator based on wide area measurement system[J]. Southern Power System Technology, 2021, 15(5): 89–97
[4] 陆友文, 崔昊, 陈佳宁, 等. 基于RA-CNN和同步相量的风电场次/超同步振荡参数智能辨识方法[J]. 中国电力, 2023, 56(4): 46–55, 67
LU Youwen, CUI Hao, CHEN Jianing, et al. Intelligent identification method of wind farm sub-synchronous/super-synchronous oscillation parameters based on RA-CNN and synchrophasor[J]. Electric Power, 2023, 56(4): 46–55, 67
[5] 张辉, 蒲天骄, 王刚, 等. 基于实测频率轨迹的机组调速器参数辨识[J]. 电力系统保护与控制, 2014, 42(1): 24–33
ZHANG Hui, PU Tianjiao, WANG Gang, et al. Governor parameter identification of generation system based on measured frequency curve[J]. Power System Protection and Control, 2014, 42(1): 24–33
[6] 乔腾, 张益铭, 曹一家, 等. 基于概率可靠性评估的永磁直驱风机低电压穿越控制模型参数辨识[J]. 中国电力, 2021, 54(12): 102–111
QIAO Teng, ZHANG Yiming, CAO Yijia, et al. Parameter identification of low voltage ride-through control model for permanent magnet direct-drive wind turbine based on probabilistic reliability assessment[J]. Electric Power, 2021, 54(12): 102–111
[7] GAUTAM D, VITTAL V, HARBOUR T. Impact of increased penetration of DFIG-based wind turbine generators on transient and small signal stability of power systems[J]. IEEE Transactions on Power Systems, 2009, 24(3): 1426–1434.
[8] 张仰飞, 袁越, 陈小虎, 等. 风力机参数的可辨识分析[J]. 电力系统自动化, 2009, 33(6): 86–89
ZHANG Yangfei, YUAN Yue, CHEN Xiaohu, et al. Analysis on wind turbine parameters identifiability[J]. Automation of Electric Power Systems, 2009, 33(6): 86–89
[9] 沈欣炜, 郑竞宏, 朱守真, 等. 光伏并网逆变器控制参数的dq轴解耦辨识策略[J]. 电力系统自动化, 2014, 38(4): 38–43
SHEN Xinwei, ZHENG Jinghong, ZHU Shouzhen, et al. A dq axis decoupling parameter identification strategy for grid-connected inverter controller of photovoltaic generation system[J]. Automation of Electric Power Systems, 2014, 38(4): 38–43
[10] 孙黎霞, 林雪, 金宇清, 等. 基于粒子群优化算法的并网光伏发电单元建模[J]. 电网技术, 2015, 39(5): 1213–1218
SUN Lixia, LIN Xue, JIN Yuqing, et al. Modeling of grid-connected photovoltaic generation unit based on particle swarm optimization algorithm[J]. Power System Technology, 2015, 39(5): 1213–1218
[11] 金宇清, 鞠平, 潘学萍, 等. 光伏逆变器控制参数的分步辨识方法[J]. 电网技术, 2015, 39(3): 594–600
JIN Yuqing, JU Ping, PAN Xueping, et al. A stepwise method to identify controller parameters of photovoltaic inverter[J]. Power System Technology, 2015, 39(3): 594–600
[12] 潘学萍, 鞠平, 温荣超, 等. 解耦辨识双馈风电机组转子侧控制器参数的频域方法[J]. 电力系统自动化, 2015, 39(20): 19–25, 108
PAN Xueping, JU Ping, WEN Rongchao, et al. Decoupling estimation of parameters in rotor side controller of DFIG-based wind turbine by frequency domain method[J]. Automation of Electric Power Systems, 2015, 39(20): 19–25, 108
[13] 许饶琪, 彭晓涛, 秦世耀, 等. 基于M序列的双馈风机变流器参数辨识方法研究[J]. 电网技术, 2022, 46(2): 578–586
XU Raoqi, PENG Xiaotao, QIN Shiyao, et al. Parameter identification of doubly-fed induction generator converter based on M-sequence[J]. Power System Technology, 2022, 46(2): 578–586
[14] 秦继朔, 贾科, 孔繁哲, 等. 基于寻优算法的永磁风机并网逆变器故障穿越控制参数分步辨识[J]. 中国电机工程学报, 2021, 41(增刊1): 59–69
QIN Jishuo, JIA Ke, KONG Fanzhe, et al. Stepwise parameter identification of fault ride-through control parameters of PMSG grid-connected inverter based on optimization algorithm[J]. Proceedings of the CSEE, 2021, 41(S1): 59–69
[15] 郭建军, 韩钤钰, 董佳琦, 等. 基于SSA-PSO-LSTM模型的羊舍相对湿度预测技术[J]. 农业机械学报, 2022, 53(9): 365–373, 398
GUO Jianjun, HAN Qianyu, DONG Jiaqi, et al. Prediction of sheep house humidity based on SSA PSO LSTM model[J]. Transactions of the Chinese Society for Agricultural Machinery, 2022, 53(9): 365–373, 398
[16] 付敏, 盖凯凯, 林悦铭. 带滤波电路的双馈风力发电系统网侧控制策略[J]. 控制工程, 2017, 24(2): 396–401
FU Min, GAI Kaikai, LIN Yueming. Research on the control strategy of grid side of doubly-fed wind power system with filter circuit[J]. Control Engineering of China, 2017, 24(2): 396–401
[17] GÜNTHER K, SOURKOUNIS C. Investigation of virtual synchronous machine control for the grid-side converter of wind turbines with permanently excited synchronous generator[C]//IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society. Lisbon, Portugal. IEEE, 2019: 2395–2401.
[18] 李温鹏, 周平. 高炉铁水质量鲁棒正则化随机权神经网络建模[J]. 自动化学报, 2020, 46(4): 721–733
LI Wenpeng, ZHOU Ping. Robust regularized RVFLNs modeling of molten iron quality in blast furnace ironmaking[J]. Acta Automatica Sinica, 2020, 46(4): 721–733
[19] 陈世群, 高伟, 陈孝琪, 等. 一种基于极限学习机和皮尔逊相关系数的光伏阵列故障快速诊断方法[J]. 电气技术, 2021, 22(10): 57–64
CHEN Shiqun, GAO Wei, CHEN Xiaoqi, et al. A fast fault diagnosis method for photovoltaic array via extreme learning machine and Pearson’s correlation coefficient[J]. Electrical Engineering, 2021, 22(10): 57–64
[20] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735–1780.