Reinforcement Learning-Based Online Estimation of the Inductances of the Permanent Magnet Synchronous Machines

IF 5.9 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Minh Xuan Bui;Hai-Nguyen Nguyen;Inam Nutkani;Nuwantha Fernando;Rukmi Dutta;M. Faz Rahman
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Abstract

This article proposes a novel method to estimate the d- and q-axis inductances of the permanent magnet synchronous machine (PMSM) based on reinforcement learning (RL) algorithms. The main challenges in online inductance estimation are the nonlinearity of the inverter and the complex dependence of machine inductances on the operating d- and q-axis currents. A feedforward neural network (FNN) was developed and trained to compensate for the nonlinearity of the inverter in the measured d- and q-axis voltages. The twin-delayed deep deterministic (TD3) policy gradient RL-based estimator was developed and trained to identify the machine inductances in real time using the compensated d- and q-axis voltages, the measured d- and q-axis currents, and the rotor speed, allowing high flexibility to adapt to variations in the system during operation by retraining with updated data. The proposed technique was experimentally validated and demonstrated high estimation accuracy under critical operating conditions, including full load variation, field weakening, and speed changes. The simulation and experimental results also showed that the proposed approach outperforms the conventional recursive least squares (RLS) and Adaline neural network (ANN) methods, achieving over 10% improvement in estimation accuracy and enhanced robustness against the inverter’s nonlinearity.
基于强化学习的永磁同步电机电感在线估计
提出了一种基于强化学习(RL)算法的永磁同步电机d轴和q轴电感估计方法。在线电感估计的主要挑战是逆变器的非线性和机器电感对工作d轴和q轴电流的复杂依赖。开发并训练了前馈神经网络(FNN)来补偿逆变器在测量的d轴和q轴电压中的非线性。开发并训练了基于双延迟深度确定性(TD3)策略梯度rl的估计器,使用补偿的d轴和q轴电压、测量的d轴和q轴电流以及转子转速实时识别机器电感,通过更新数据进行再训练,可以高度灵活地适应系统在运行过程中的变化。实验验证了该方法在满载变化、磁场减弱和转速变化等关键工况下具有较高的估计精度。仿真和实验结果表明,该方法优于传统的递推最小二乘(RLS)和Adaline神经网络(ANN)方法,估计精度提高10%以上,增强了对逆变器非线性的鲁棒性。
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来源期刊
IEEE Transactions on Instrumentation and Measurement
IEEE Transactions on Instrumentation and Measurement 工程技术-工程:电子与电气
CiteScore
9.00
自引率
23.20%
发文量
1294
审稿时长
3.9 months
期刊介绍: Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.
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