Sensorless Adaptive control of VSI-Fed Induction Motor Drive with Optimized MLP-Neural Network

Q3 Engineering
S. Arya, Mahesh Pudari
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引用次数: 0

Abstract

ABSTRACT Multilayer-Perceptron Neural Network (MLP-NN)-based sensorless speed control of adaptive Indirect Field-Oriented Control (IFOC) strategy is implemented for online parameter estimation of Induction Motor Drive (IMD) fed from Common mode voltage injection Space vector PWM (CVSVPWM) based Voltage Source Inverter. Harris Hawks Optimization (HHO) is implemented in this work, to train the MLP-NN model by choosing the optimal weight and biases for the estimation of accurate parameters and speed of IMD. The objective of optimal MLP-NN is to improve the IMD reliability and response fast during dynamic operation. The model performances are evaluated by employing statistical metrics of MSE, RMSE, MAE, MAPE, and R for training and testing. These are reported for testing to be 0.000602064, 0.0245, 0.4015, 0.25474, and 0.9997 which indicates the best-fitted prediction model and proves the minimized error. The results reveal that an optimized MLP-NN accomplishes promising performance in estimating the parameters and speed with the least errors such as rs is 3.82%, rr is 4.19%, ls is 0.41%, lr is 0.72%, lm is 0.21%, and strongly tracking of reference speed. In addition, HHO is also employed to evolve the gains of the PI-controller in adaptive-IFOC for generation of reference signals by reducing the computational effort.
基于优化mlp神经网络的VSI-Fed感应电机无传感器自适应控制
摘要针对共模电压注入空间矢量PWM (CVSVPWM)电压源逆变器的异步电机驱动(IMD)参数在线估计问题,实现了基于多层感知器神经网络(MLP-NN)的自适应间接场定向控制(IFOC)策略的无传感器速度控制。本文采用Harris Hawks Optimization (HHO)方法,通过选择最优的权值和偏置来训练MLP-NN模型,以估计IMD的准确参数和速度。最优MLP-NN的目标是在动态运行时提高IMD的可靠性和快速响应。采用统计指标MSE、RMSE、MAE、MAPE和R进行训练和测试,评估模型的性能。测试结果为0.000602064、0.0245、0.4015、0.25474和0.9997,表明预测模型拟合最佳,证明误差最小。结果表明,优化后的MLP-NN在估计参数和速度方面取得了良好的效果,误差最小,rs为3.82%,rr为4.19%,ls为0.41%,lr为0.72%,lm为0.21%,并且对参考速度具有较强的跟踪能力。此外,HHO还用于通过减少计算量来进化自适应ifoc中pi控制器的增益,以产生参考信号。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Australian Journal of Electrical and Electronics Engineering
Australian Journal of Electrical and Electronics Engineering Engineering-Electrical and Electronic Engineering
CiteScore
2.30
自引率
0.00%
发文量
46
期刊介绍: Engineers Australia journal and conference papers.
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