Parameter identification of a lumped parameter thermal model for a permanent magnet synchronous machine

Guemo, Gilles Guedia; Chantrenne, Patrice; Jac, Julien

doi:10.1109/iemdc.2013.6556329

Cited by 13 publications

(8 citation statements)

References 2 publications

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“…The use of reduced-order LPTN models would require estimations of many parameters that are not well known or possible to calculate using analytical equations [9,10]. Thus, the identification of the parameters through empirical-based tuning is a crucial step in these studies [11][12][13]. The parameter identification procedure can be stated as an optimization problem, in which the values are varied until the used LPTN model gives the same results as the experimental ones.…”

Section: Background and Related Workmentioning

confidence: 99%

Synergizing Transfer Learning and Multi-Agent Systems for Thermal Parametrization in Induction Traction Motors

Mehboob,

Fattouh,

Sahoo

2024

Preprint

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This paper presents an innovative multi-agent, data-driven reinforcement learning (RL) approach to develop and utilize the thermal equivalent network model that represents the motor's thermal dynamics. A multi-agent reinforcement learning is designed and trained to adjust the model parameters using data from several motor driving cycles. To ensure the incoming driving cycle matches the historical data before employing the pre-trained RL agents, offline statistical analysis and clustering techniques are developed and used. Numerical simulations highlighted the RL agent's ability to develop strategies that effectively address the variability of driving cycles. The proposed RL framework showed its capability to accurately reflect the motor's thermal behavior under various driving conditions.

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Section: Background and Related Workmentioning

confidence: 99%

Synergizing Transfer Learning and Multi-Agent Systems for Thermal Parametrization in Induction Traction Motors

Mehboob,

Fattouh,

Sahoo

2024

Preprint

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“…This study focuses on the determination of interface resistors and the convection heat transfer coefficients required for the LPTN. Gauss-Newton, Levenberg-Marquardt, and genetic algorithms have been employed in [63] to identify ten thermal parameters, including four convective heat transfer coefficients in different regions, four contact conductance between the regions, conductivity coefficient in winding, and thermal resistance of the ball-bearing. A calibration technique for the generic thermal model of electrical machines has been proposed in [64].…”

Section: Lumped Parameter Thermal Networkmentioning

confidence: 99%

An overview of thermal modelling techniques for permanent magnet machines

Khalesidoost

Faiz

Mazaheri‐Tehrani

2022

IET Science Measure & Tech

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Permanent magnet (PM) machines have gained increasing attention from the industry because of their potential merits over conventional electrical machines. High‐temperature level in electrical machines is a major factor that can adversely impact the performance and health conditions of these machines. These effects need to be minimized to make them competitive with currently operational machines in the industry. It can be done by performing a thorough thermal analysis of the prototype during the design process and proper online thermal monitoring of the machine during the operation stage. Thermal modelling is a powerful tool for achieving such goals in both cases. This paper aims to review the state‐of‐the‐art of thermal modelling techniques and provide a detailed comparison of them in PM machines. The fundamental concepts of heat transfer and sources of heat in PM machines are first discussed. Then, different thermal modelling techniques, including thermal subdomain models (TSMs), lumped parameter thermal networks (LPTNs), finite element models (FEMs), computational fluid dynamics (CFD), reduced order models (ROMs), and hybrid thermal models (HTMs) are introduced and compared. Finally, the utilization of these models in the thermal design, control, and monitoring of PM machines is studied.

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“…Hence, all errors in the determination of total and winding losses directly add up to an error in the iron loss values. In the above backdrop, empirically-based parametrization is a crucial step in these studies [9][10][11][12][13][14][15]. The parameter identification procedure can be stated as an optimization problem in which the values are varied until the used LPTN model gives the same results as the experimental ones.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…Hence, to improve computational efficiency, the dependence of the state matrix on the phase current is approximated with polynomial approximation. In the work presented by the authors [11], the parameter identification is conducted by using two determinist methods, the Gauss-Newton method and the Levenberg-Marquardt method, and one stochastic method, the Genetic Algorithm method. The results show good accuracy in predicting the temperature; however, the authors concluded that stochastic methods are more precise for a real-time model operation.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Synergizing Transfer Learning and Multi-Agent Systems for Thermal Parametrization in Induction Traction Motors

Mehboob,

Fattouh,

Sahoo

2024

Applied Sciences

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Maintaining optimal temperatures in the critical parts of an induction traction motor is crucial for railway propulsion systems. A reduced-order lumped-parameter thermal network (LPTN) model enables computably inexpensive, accurate temperature estimation; however, it requires empirically based parameter estimation exercises. The calibration process is typically performed in labs in a controlled experimental setting, which is associated with a lot of supervised human efforts. However, the exploration of machine learning (ML) techniques in varied domains has enabled the model parameterization in the drive system outside the laboratory settings. This paper presents an innovative use of a multi-agent reinforcement learning (MARL) approach for the parametrization of an LPTN model. First, a set of reinforcement learning agents are trained to estimate the optimized thermal parameters using the simulated data in several driving cycles (DCs). The selection of a reinforcement learning agent and the level of neurons in the RL model is made based on variability of the driving cycle data. Furthermore, transfer learning is performed on a new driving cycle data collected on the measurement setup. Statistical analysis and clustering techniques are proposed for the selection of an RL agent that has been pre-trained on the historical data. It is established that by synergizing within reinforcement learning techniques, it is possible to refine and adjust the RL learning models to effectively capture the complexities of thermal dynamics. The proposed MARL framework shows its capability to accurately reflect the motor’s thermal behavior under various driving conditions. The transfer learning usage in the proposed approach could yield significant improvement in the accuracy of temperature prediction in the new driving cycles data. This approach is proposed with the aim of developing more adaptive and efficient thermal management strategies for railway propulsion systems.

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Parameter identification of a lumped parameter thermal model for a permanent magnet synchronous machine

Cited by 13 publications

References 2 publications

Synergizing Transfer Learning and Multi-Agent Systems for Thermal Parametrization in Induction Traction Motors

Synergizing Transfer Learning and Multi-Agent Systems for Thermal Parametrization in Induction Traction Motors

An overview of thermal modelling techniques for permanent magnet machines

Synergizing Transfer Learning and Multi-Agent Systems for Thermal Parametrization in Induction Traction Motors

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