Parameter Identification of Low Voltage Ride-Through Control Model for Permanent Magnet Direct-Drive Wind Turbine Based on Probabilistic Reliability Assessment

doi:10.11930/j.issn.1004-9649.202012112

Abstract

Abstract: The accurate parameter acquisition is helpful to improve the simulation accuracy of the low voltage ride-through control model for permanent magnet direct-drive wind turbines. The parameters of a complex model are numerous and cannot be fully obtained through tests, and the inaccuracy of its non-target parameters will affect the accuracy of target parameter identification. Therefore, the sensitivity analysis is used to evaluate the difficulty of parameter acquisition, providing a basis for the identification order selection. The non-target parameters are randomly set within the empirical range, and the probabilistic reliability assessment method is employed to decide observed variables from them for the accurate identification of target parameters. A stepwise identification strategy that takes into account both the priority order for identification and the selection of corresponding observed variables is adopted for the target parameters, and the identification results with high probabilistic reliability are obtained as the final parameter values. In this way, the influence of inaccurate settings of non-target parameters is eliminated. The proposed method is applied to identify the target parameters of the low voltage ride-through control model for permanent magnet direct-drive wind turbines, and the results have been verified by measured data.

Key words: permanent magnet direct-drive wind turbine, parameter identification, sensitivity analysis, probabilistic reliability assessment, low voltage ride-through control model

QIAO Teng, ZHANG Yiming, CAO Yijia, WANG Li, YUAN Qing. 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.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202012112

https://www.electricpower.com.cn/EN/Y2021/V54/I12/102

References

[1] PAN C Y, XI Y H, HU Y. Grey-box parameter identification for drive-train system of large-scale wind turbine[C]//2019 Chinese Control Conference (CCC). Guangzhou, China. IEEE, 2019.
[2] 侍乔明, 付立军, 李海英, 等. 直驱永磁风力发电机组的频率响应简化模型及其应用[J]. 智慧电力, 2020, 48(1): 42–48,90
SHI Qiaoming, FU Lijun, LI Haiying, et al. Simplified frequency response model of direct-driven wind turbine with permanent-magnet synchronous generator and its application[J]. Smart Power, 2020, 48(1): 42–48,90
[3] 贾锋, 蔡旭, 李征, 等. 风电机组精细化建模及硬件在环实时联合仿真[J]. 中国电机工程学报, 2017, 37(4): 1239–1251
JIA Feng, CAI Xu, LI Zheng, et al. Refined modeling of wind energy conversion systems and real-time co-simulation with hardware-in-loop[J]. Proceedings of the CSEE, 2017, 37(4): 1239–1251
[4] 张剑, 何怡刚. 基于轨迹灵敏度分析的永磁直驱风电场等值模型参数辨识[J]. 电工技术学报, 2020, 35(15): 3303–3313
ZHANG Jian, HE Yigang. Parameters identification of equivalent model of permanent magnet synchronous generator wind farm based on analysis of trajectory sensitivity[J]. Transactions of China Electrotechnical Society, 2020, 35(15): 3303–3313
[5] 张新宇, 赵义松, 苑经纬, 等. 考虑网络损耗的直流近区风电场集电参数等值建模[J]. 电力科学与技术学报, 2020, 35(6): 117–123
ZHANG Xinyu, ZHAO Yisong, YUAN Jingwei, et al. Research on equivalent modeling of collecting parameters of DC near-field wind farms considering network losses[J]. Journal of Electric Power Science and Technology, 2020, 35(6): 117–123
[6] ANG K M, LIM W H, ISA N A M, et al. A constrained multi-swarm particle swarm optimization without velocity for constrained optimization problems[J]. Expert Systems With Applications, 2020, 140: 112882.
[7] 肖曦, 许青松, 王雅婷, 等. 基于遗传算法的内埋式永磁同步电机参数辨识方法[J]. 电工技术学报, 2014, 29(3): 21–26
XIAO Xi, XU Qingsong, WANG Yating, et al. Parameter identification of interior permanent magnet synchronous motors based on genetic algorithm[J]. Transactions of China Electrotechnical Society, 2014, 29(3): 21–26
[8] SANDRE-HERNANDEZ O, MORALES-CAPORAL R, RANGEL-MAGDALENO J, et al. Parameter identification of PMSMs using experimental measurements and a PSO algorithm[J]. IEEE Transactions on Instrumentation and Measurement, 2015, 64(8): 2146–2154.
[9] 田德, 高洋, 闫肖蒙, 等. 基于差分进化算法的风电机组变桨参数寻优[J]. 太阳能学报, 2019, 40(8): 2154–2161
TIAN De, GAO Yang, YAN Xiaomeng, et al. Optimization of pitch controller parameters for wind turbine based on differential evolution algorithm[J]. Acta Energiae Solaris Sinica, 2019, 40(8): 2154–2161
[10] 曹斌, 刘文焯, 原帅, 等. 基于低电压穿越试验的光伏发电系统建模研究[J]. 电力系统保护与控制, 2020, 48(18): 146–155
CAO Bin, LIU Wenzhuo, YUAN Shuai, et al. Modeling of photovoltaic power system based on low voltage ride-through test[J]. Power System Protection and Control, 2020, 48(18): 146–155
[11] ZHOU Y H, ZHAO L, LEE W J. Robustness analysis of dynamic equivalent model of DFIG wind farm for stability study[J]. 2018 IEEE/IAS 54th Industrial and Commercial Power Systems Technical Conference (I&CPS), 2018: 1–9.
[12] 潘学萍, 殷紫吟, 鞠平, 等. 基于短路电流辨识双馈感应发电机的模型参数[J]. 电力自动化设备, 2017, 37(11): 27–31
PAN Xueping, YIN Ziyin, JU Ping, et al. Model parameter identification of DFIG based on short circuit current[J]. Electric Power Automation Equipment, 2017, 37(11): 27–31
[13] 童宁, 林湘宁, 李正天, 等. 对称电压跌落条件下双馈风电机群馈出电流与线路电流保护的交互影响[J]. 中国电机工程学报, 2014, 34(22): 3806–3814
TONG Ning, LIN Xiangning, LI Zhengtian, et al. Study for reciprocal effect between overcurrent protection and doubly-fed induction generator feeding current under the condition of symmetrical voltage sag[J]. Proceedings of the CSEE, 2014, 34(22): 3806–3814
[14] 黄桦, 潘学萍, 李佳伟, 等. 直驱永磁风电机组LVRT模型参数的整定方法与实测验证[J]. 电力自动化设备, 2019, 39(4): 155–160
HUANG Hua, PAN Xueping, LI Jiawei, et al. Parameter setting method and measurement verification of LVRT model for direct drive permanent magnet wind turbine generator[J]. Electric Power Automation Equipment, 2019, 39(4): 155–160
[15] 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: 1–13. http://kns.cnki.net/kcms/detail/11.2107.TM.20201112.1424.003.html.
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: 1–13. http://kns.cnki.net/kcms/detail/11.2107.TM.20201112.1424.003.html.
[16] 张伟骏, 陈文龙, 黄霆, 等. 基于轨迹灵敏度的同步发电机参数辨识[J]. 中国电力, 2019, 52(9): 102–109
ZHANG Weijun, CHEN Wenlong, HUANG Ting, et al. Parameter identification method of synchronous generator based on trajectory sensitivity[J]. Electric Power, 2019, 52(9): 102–109
[17] 刘涤尘, 王力, 赵洁, 等. 压水堆核电机组动态模型参数评价[J]. 电力自动化设备, 2018, 38(10): 39–45
LIU Dichen, WANG Li, ZHAO Jie, et al. Parameter evaluation of pressurized water reactor nuclear power unit's dynamic model[J]. Electric Power Automation Equipment, 2018, 38(10): 39–45
[18] 刘德顺, 岳文辉, 杜小平. 不确定性分析与稳健设计的研究进展[J]. 中国机械工程, 2006, 17(17): 1834–1841
LIU Deshun, YUE Wenhui, DU Xiaoping. Study on uncertainty analysis and robust design: a review[J]. China Mechanical Engineering, 2006, 17(17): 1834–1841
[19] 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.
[20] 李腾, 葛少云, 梁文举, 等. 考虑环境效益的含分布式电源配电网概率可靠性评估[J]. 电力系统及其自动化学报, 2019, 31(1): 117–123,131
LI Teng, GE Shaoyun, LIANG Wenju, et al. Probabilistic reliability evaluation on distribution network with distributed generations considering environmental benefit[J]. Proceedings of the CSU-EPSA, 2019, 31(1): 117–123,131
[21] 孙大卫, 吴林林, 刘辉, 等. 弱电网直驱风机低电压穿越特性及其对机端暂态电压的影响[J]. 中国电机工程学报: 1–11. https://doi.org/10.13334/j.0258-8013.pcsee.201078.
SUN Dawei, WU Linlin, LIU Hui, et al. The effect of low voltage ride through characteristics on pmsg terminal transient voltage in weakly-synchronized gird[J]. Proceedings of the CSEE: 1–11. https://doi.org/10.13334/j.0258-8013.pcsee.201078.
[22] LIU F, ZHOU Y, DUAN S X, et al. Parameter design of a two-current-loop controller used in a grid-connected inverter system with LCL filter[J]. IEEE Transactions on Industrial Electronics, 2009, 56(11): 4483–4491.
[23] WANG L, QIAO T, ZHAO B, et al. Modeling and parameter optimization of grid-connected photovoltaic systems considering the low voltage ride-through control[J]. Energies, 2020, 13(15): 3972.
[24] 郭亚舟. 基于PSD-BPA的风电并网模型及实证应用研究[D]. 上海: 上海交通大学, 2018.
GUO Yazhou. Modelling and application principles of wind power integration based on PSD-BPA[D]. Shanghai: Shanghai Jiaotong University, 2018.
[25] BEIGI A M, MAROOSI A. Parameter identification for solar cells and module using a hybrid firefly and pattern search algorithms[J]. Solar Energy, 2018, 171: 435–446.
[26] 王齐, 陈娟, 李全善, 等. 改进生物地理学优化算法及其在PID控制器参数中的优化[J]. 南京理工大学学报, 2017, 41(4): 519–525
WANG Qi, CHEN Juan, LI Quanshan, et al. PID parameter optimization based on improved biogeography-based optimization algorithm[J]. Journal of Nanjing University of Science and Technology, 2017, 41(4): 519–525