1. Department of Electrical and Electronics Engineering, National Institute of Technology Puducherry, Karaikal 609605, India 2. Department of Electrical and Electronics Engineering, Pondicherry Engineering College, Puducherry 605014, India
This paper presents a new neural network based model reference adaptive system (MRAS) to solve low speed problems for estimating rotor resistance in vector control of induction motor (IM). The MRAS using rotor flux as the state variable with a two layer online trained neural network rotor flux estimator as the adaptive model (FLUX-MRAS) for rotor resistance estimation is popularly used in vector control. In this scheme, the reference model used is the flux estimator using voltage model equations. The voltage model encounters major drawbacks at low speeds, namely, integrator drift and stator resistance variation problems. These lead to a significant error in the estimation of rotor resistance at low speed. To address these problems, an offline trained NN with data incorporating stator resistance variation is proposed to estimate flux, and used instead of the voltage model. The offline trained NN, modeled using the cascade neural network, is used as a reference model instead of the voltage model to form a new scheme named as “NN-FLUX-MRAS.” The NN-FLUX-MRAS uses two neural networks, namely, offline trained NN as the reference model and online trained NN as the adaptive model. The performance of the novel NN-FLUX-MRAS is compared with the FLUX-MRAS for low speed problems in terms of integral square error (ISE), integral time square error (ITSE), integral absolute error (IAE) and integral time absolute error (ITAE). The proposed NN-FLUX-MRAS is shown to overcome the low speed problems in Matlab simulation.
收稿日期: 2015-10-08
出版日期: 2016-11-17
Corresponding Author(s):
Venkadesan ARUNACHALAM
引用本文:
. [J]. Frontiers in Energy, 2016, 10(4): 382-392.
Venkadesan ARUNACHALAM,Himavathi SRINIVASAN,A. MUTHURAMALINGAM. A novel NN based rotor flux MRAS to overcome low speed problems for rotor resistance estimation in vector controlled IM drives. Front. Energy, 2016, 10(4): 382-392.
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