Experimental Identification of a Coupled-Circuit Model for the Digital Twin of a Wound-Rotor Induction Machine

被引:1
作者
Aboubi, Fatma Zohra [1 ]
Maiga, Abdrahamane [1 ]
Cros, Jerome [1 ]
Kamwa, Innocent [1 ]
机构
[1] Laval Univ, Dept Elect & Comp Engn, Lab Elect Engn Power Elect & Ind Control LEEPEIC, 1065 Ave Med, Quebec City, PQ G1V 0A6, Canada
关键词
electrical machines; identification; coupled-circuit model; field calculation; digital twin; SIMULATION;
D O I
10.3390/en17081948
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The development of monitoring and diagnostic methods for electrical machines requires the use of transient models capable of operating in real time and producing signal signatures with high precision. In this context, coupled-circuit models offer numerous advantages due to their speed of execution and accuracy. They have been successfully employed to create real-time digital twins of electrical machines. The main challenge of this modeling method lies in the preparation of the model, which involves numerous preliminary calculations and takes time to identify all its parameters. This is particularly due to the variation in inductances based on the rotor position. To determine these inductance values with great precision, the classical approach involves using finite-element field calculation software. However, the computation time quickly becomes an issue due to the large number of values to calculate and simulations to perform. This article introduces an innovative experimental approach to identify a coupled-circuit model and develop a digital twin of a wound-rotor induction machine. This method relies solely on simple electrical measurements and tests conducted at extremely low rotation speeds (1 rpm) to obtain inductance variations as a function of the rotor position. By employing this technique, the need for analytical models or finite-element field calculation simulations, which typically require precise knowledge of the machine's geometry and materials, is circumvented. The measurement processing employs optimization methods to extract the inductances as a function of the rotor position, which are then used as input data for the coupled-circuit model. The final parameters are specific to each machine and replicate all its manufacturing imperfections such as eccentricity and geometric or winding defects. This experimental identification method significantly enhances the model's accuracy and reduces the usually required preliminary calculation time in a finite-element-based identification process.
引用
收藏
页数:25
相关论文
共 17 条
  • [1] Real-Time Hardware-in-the-Loop Simulation of Permanent-Magnet Synchronous Motor Drives Under Stator Faults
    Alvarez-Gonzalez, Fernando
    Griffo, Antonio
    Sen, Bhaskar
    Wang, Jiabin
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2017, 64 (09) : 6960 - 6969
  • [2] Benninger M., 2022, 2022 International Conference on Electrical Machines (ICEM), P1307, DOI 10.1109/ICEM51905.2022.9910708
  • [3] Real-Time Digital Twin of a Wound Rotor Induction Machine Based on Finite Element Method
    Bouzid, Sami
    Viarouge, Philippe
    Cros, Jerome
    [J]. ENERGIES, 2020, 13 (20)
  • [4] Ebrahimi A, 2019, PROC IEEE INT SYMP, P1059, DOI [10.1109/isie.2019.8781529, 10.1109/ISIE.2019.8781529]
  • [5] High Accuracy Modeling of Permanent Magnet Synchronous Motors Using Finite Element Analysis
    Elsherbiny, Hanaa
    Szamel, Laszlo
    Ahmed, Mohamed Kamal
    Elwany, Mahmoud A.
    [J]. MATHEMATICS, 2022, 10 (20)
  • [6] Damper Currents Simulation of Large Hydro-Generator Using the Combination of FEM and Coupled Circuits Models
    Gbegbe, Anicette Zihewo
    Rouached, Bouali
    Cros, Jerome
    Bergeron, Maxim
    Viarouge, Philippe
    [J]. IEEE TRANSACTIONS ON ENERGY CONVERSION, 2017, 32 (04) : 1273 - 1283
  • [7] Houdouin G, 2003, IEEE IEMDC'03: IEEE INTERNATIONAL ELECTRIC MACHINES AND DRIVES CONFERENCE, VOLS 1-3, P297, DOI 10.1109/IEMDC.2003.1211279
  • [8] A fault prediction framework for Doubly-fed induction generator under time-varying operating conditions driven by digital twin
    Ma, Junyan
    Yuan, Yiping
    Chen, Pan
    [J]. IET ELECTRIC POWER APPLICATIONS, 2023, 17 (04) : 499 - 521
  • [9] Mathault J., 2014, Electrimacs, V61, P19
  • [10] Partial Inductance Model of Induction Machines for Fault Diagnosis
    Pineda-Sanchez, Manuel
    Puche-Panadero, Ruben
    Martinez-Roman, Javier
    Sapena-Bano, Angel
    Riera-Guasp, Martin
    Perez-Cruz, Juan
    [J]. SENSORS, 2018, 18 (07)