一种基于数字孪生的灰盒单相PWM整流器建模与健康参数监测方法

doi:10.19595/j.cnki.1000-6753.tces.232062

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (4892 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Since single-phase, two-level PWM rectifiers are widely used and undertake important functions, their working conditions are complex, and the operating environment is variable. Therefore, the demand for their reliability in engineering application sites has gradually increased, and the problem of accurate monitoring and evaluation of reliability urgently needs to be solved. Non-invasive monitoring is a better choice to reduce the impact on the original system during system status monitoring. Considering the situation of unknown system control parameters in practical applications, this paper proposes a digital twin gray-box model for single-phase, two-level PWM rectifiers. This model can simulate the external voltage and current characteristics of actual rectification systems, achieving non-invasive monitoring of the control parameters of the system and key parameters of the main circuit.
Firstly, a digital twin gray-box model of single-phase, two-level PWM rectifiers has been established. A mathematical model of the main circuit is established. Combined with the mathematical model of the corresponding closed-loop control system and the sampling part in the actual system, a discretized mathematical model of the closed-loop rectification system can be obtained using the fourth-order Runge-Kutta method for discretization. Based on the actual rectification circuit's external voltage and current data, an improved particle swarm optimization algorithm is used to iterate the discrete mathematical model. The parameters in iterations include system control parameters and health status characteristic parameters. When the external characteristics of the discrete mathematical model approximate the actual circuit, the final digital twin gray-box model has been obtained.
Secondly, system control parameters and health status characteristic parameters are monitored. Due to the unknown control parameters, the control parameters are introduced as the measured values during the algorithm iteration when establishing the digital twin gray-box model. Based on the external characteristic data of the actual system, the control parameters can be well monitored, making the digital twin model similar to the actual system. Meanwhile, key parameters of the main circuit are the focus of monitoring, i.e., the equivalent inductance L and resistance R of the AC-side inductor, the capacitance C of the DC-side supporting capacitor, and the saturation voltage drop of IGBT.
Finally, the digital twin gray-box model and parameter monitoring method have been validated under different parameters and operating conditions. The results indicate that the established model can simulate the actual system's dynamic and static operating states. At the same time, the control parameters and key parameters are effectively monitored, the errors are less than 5%, and the changing trend of parameters can be identified. In addition, the influence of different intelligent optimization algorithms on monitoring performance is explored, considering algorithm complexity, average iterations, and local optimal escaping ability. The superiority of the particle swarm optimization algorithm under the application conditions has been demonstrated.

Key words： Single-phase PWM rectifier digital twin gray-box model condition monitoring IGBT

Received: 08 December 2023

PACS:

TM461

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Zhang Sihui
	Song Wensheng
	Tang Tao
	Zou Yuchao
	Zhang Zhiwei

Cite this article:

Zhang Sihui,Song Wensheng,Tang Tao等. A Gray-Box Single-Phase PWM Rectifier Modeling and Health Parameter Monitoring Method Based on Digital Twins[J]. Transactions of China Electrotechnical Society, 2025, 40(2): 463-476.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.232062 OR https://dgjsxb.ces-transaction.com/EN/Y2025/V40/I2/463

[1] 晏益朋, 余城洋, 熊露婧, 等. 一种基于离散时域模型的单相PWM整流器控制参数多目标优化设计方法[J]. 电工技术学报, 2024, 39(1): 206-216.
Yan Yipeng, Yu Chengyang, Xiong Lujing, et al.A multi-objective controller parameter design optimi- zation method of single-phase PWM rectifier with discrete-time domain model[J]. Transactions of China Electrotechnical Society, 2024, 39(1): 206-216.
[2] 周雪松, 胡一凡, 马幼捷, 等. 动车组静调试验中直流母线电压振荡分析与抑制[J]. 电工技术学报, 2022, 37(14): 3701-3712.
Zhou Xuesong, Hu Yifan, Ma Youjie, et al.Analysis and suppression of voltage oscillation for DC bus in static adjustment test of EMU[J]. Transactions of China Electrotechnical Society, 2022, 37(14): 3701-3712.
[3] 彭林, 马磊, 刘浩然, 等. 单相PWM整流器H_∞ 混合灵敏度电流控制[J]. 中国电机工程学报, 2020, 40(14): 4580-4589.
Peng Lin, Ma Lei, Liu Haoran, et al.H_∞ mixed sensitivity current control for single-phase PWM rectifier[J]. Proceedings of the CSEE, 2020, 40(14): 4580-4589.
[4] 郝亮亮, 陈建林, 段贤稳, 等. 核电多相无刷励磁系统中旋转整流器不同开路故障模式的特征分析及诊断[J]. 电工技术学报, 2023, 38(18): 4932-4946.
Hao Liangliang, Chen Jianlin, Duan Xianwen, et al.Analysis and diagnosis of different open-circuit fault modes of rotating rectifier in multi-phase brushless excitation system at nuclear power plant[J]. Transa- ctions of China Electrotechnical Society, 2023, 38(18): 4932-4946.
[5] 梁郑秋, 郝亮亮, 周艳真, 等. 基于卷积神经网络的核电多相无刷励磁系统旋转整流器故障诊断[J]. 电工技术学报, 2023, 38(20): 5458-5472.
Liang Zhengqiu, Hao Liangliang, Zhou Yanzhen, et al.Fault diagnosis of rotating rectifier in nuclear multi-phase brushless excitation system based on convolutional neural network[J]. Transactions of China Electrotechnical Society, 2023, 38(20): 5458-5472.
[6] 罗丹, 陈民铀, 赖伟, 等. 基于Haar小波变换重构开关序列的MMC子模块电容值在线监测方法[J]. 电工技术学报, 2022, 37(20): 5278-5289.
Luo Dan, Chen Minyou, Lai Wei, et al.Online monitoring method for sub-module capacitance in modular multilevel converter based on Haar wavelet transform reconstruction switch sequence[J]. Transa- ctions of China Electrotechnical Society, 2022, 37(20): 5278-5289.
[7] 王奇维, 李斌兴, 潘冠丞, 等. 基于转子位置误差解耦阻抗建模的永磁同步电机电感在线辨识方法[J/OL]. 电工技术学报, 2024, DOI: 10.19595/j.cnki. 1000-6753.tces.231339.
Wang Qiwei, Li Binxing, Pan Guancheng, et al.Impedance model based online inductance identi- fication method of permanent magnet synchronous motor decoupled from rotor position error[J/OL]. Transactions of China Electrotechnical Society, 2024, DOI: 10.19595/j.cnki.1000-6753.tces.231339.
[8] Yang Yanyong, Zhang Pinjia.In situ junction temperature monitoring and bond wire detecting method based on IGBT and FWD on-state voltage drops[J]. IEEE Transactions on Industry Applications, 2022, 58(1): 576-587.
[9] 蔡富裕, 郭文勇, 赵闯, 等. 基于参数在线辨识的电流源型变流器状态监测及自适应控制[J]. 电力系统自动化, 2019, 43(18): 67-74, 119.
Cai Fuyu, Guo Wenyong, Zhao Chuang, et al.State monitoring and adaptive control of current source converter based on online identification of para- meters[J]. Automation of Electric Power Systems, 2019, 43(18): 67-74, 119.
[10] Pirsto V, Kukkola J, Mahafugur Rahman F M, et al. Real-time identification of LCL filters employed with grid converters[J]. IEEE Transactions on Industry Applications, 2020, 56(5): 5158-5169.
[11] 张敏, 潘庭龙. 基于多新息随机梯度算法的网侧变流器参数辨识方法研究[J]. 可再生能源, 2016, 34(4): 511-517.
Zhang Min, Pan Tinglong.Parameter identification of grid-side converter based on MISG[J]. Renewable Energy Resources, 2016, 34(4): 511-517.
[12] Wang Quanjie, Wang Wei, Shuai Zhikang, et al.Condition monitoring for resonant capacitors in LLC resonant converter[C]//2024 IEEE 10th International Power Electronics and Motion Control Conference (IPEMC2024-ECCE Asia), Chengdu, China, 2024: 3545-3550.
[13] Mihai S, Yaqoob M, Hung D V, et al.Digital twins: a survey on enabling technologies, challenges, trends and future prospects[J]. IEEE Communications Surveys & Tutorials, 2022, 24(4): 2255-2291.
[14] Song Wensheng, Zou Yuchao, Ma Chenwei, et al.Digital twin modeling method of three-phase inverter-driven PMSM systems for parameter esti- mation[J]. IEEE Transactions on Power Electronics, 2024, 39(2): 2360-2371.
[15] Arraño-Vargas F, Konstantinou G.Modular design and real-time simulators toward power system digital twins implementation[J]. IEEE Transactions on Industrial Informatics, 2023, 19(1): 52-61.
[16] Peng Yingzhou, Zhao Shuai, Wang Huai.A digital twin based estimation method for health indicators of DC-DC converters[J]. IEEE Transactions on Power Electronics, 2021, 36(2): 2105-2118.
[17] Sharida A, Kamal N F, Alnuweiri H, et al.Digital- twin-based diagnosis and tolerant control of T-type three-level rectifiers[J]. IEEE Open Journal of the Industrial Electronics Society, 2023, 4: 230-241.
[18] Zhang Sihui, Song Wensheng, Cao Hu, et al.A digital-twin-based health status monitoring method for single-phase PWM rectifiers[J]. IEEE Transa- ctions on Power Electronics, 2023, 38(11): 14075-14087.
[19] Zhao Zhaoyang, Hu Haitao, He Zhengyou, et al.A transient-modeling-based grey-box method for online monitoring of DC-link capacitors[J]. IEEE Transa- ctions on Power Electronics, 2023, 38(11): 14547-14562.
[20] 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.
[21] Wang Lu, Qin Zian, Bauer P.A gradient-descent optimization assisted gray-box impedance modeling of EV chargers[J]. IEEE Transactions on Power Electronics, 2023, 38(7): 8866-8879.
[22] 严干贵, 贾希浩, 王玉鹏, 等. 动态响应误差驱动的风机并网变流器控制参数辨识方法[J]. 东北电力大学学报, 2023, 43(3): 1-7.
Yan Gangui, Jia Xihao, Wang Yupeng, et al.Control parameter identification method of wind turbine grid-connected converter driven by dynamic response error[J]. Journal of Northeast Electric Power University, 2023, 43(3): 1-7.
[23] 徐恒山, 朱士豪, 黄永章, 等. 基于GRU-IPSO算法的双馈风机控制参数辨识[J/OL]. 华北电力大学学报(自然科学版), 1-10 [2024-03-27]. http://kns.cnki.net/kcms/detail/13.1212.TM.20230913.1807.002.html.
Xu Hengshan, Zhu Shihao, Huang Yongzhang, et al. Control parameter identification of for doubly fed induction generator based on GRU-IPSO algo- rithm[J/OL]. Journal of North China Electric Power University (Natural Science Edition), 1-10 [2024- 03-27]. http://kns.cnki.net/kcms/detail/13.1212.TM.20230913.1807.002.html.
[24] Du Mingxing, Kong Qingyi, Ouyang Ziwei, et al.Strategy for diagnosing the aging of an IGBT module by ON-state voltage separation[J]. IEEE Transactions on Electron Devices, 2019, 66(11): 4858-4864.
[25] Eberhart R, Kennedy J.A new optimizer using particle swarm theory[C]//MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 1995: 39-43.