Open-loop neuro-fuzzy speed estimator applied to vector and scalar induction motor drives |
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| Affiliation: | 1. Industrial Engineering Department, Centro Universitário da FEI, Brazil;2. Electrical Engineering Department, University of São Paulo, Brazil;3. Electrical Engineering Department, University of São Paulo, São Carlos School of Engineering, Brazil;1. Data Mining and Optimisation Research Group, Centre for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia;2. Faculty of Information & Communication Technologies, Swinburne University of Technology, Victoria 3122, Australia;1. Computer Science and Communications Research Unit, University of Luxembourg, Luxembourg;2. Laboratoire d’Informatique Fondamentale de Lille, University of Lille 1, France;1. Department of Applied Mathematics, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong, China;2. School of Electronics and Computer Science, University of Southampton Malaysia Campus, Nusajaya, Johor, Malaysia;3. Department of Electrical and Computer Engineering, Curtin University, WA, Australia;1. Gwangju Institute of Science and Technology, South Korea;2. International Islamic University, Islamabad, Pakistan;3. National University of Computer and Emerging Sciences, Islamabad, Pakistan;4. College of Computer and Information Sciences, Al-Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia |
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| Abstract: | Scalar and vector drives have been the cornerstones of control of industrial motors for decades. In both the elimination of mechanical speed sensor consists in a trend of modern drives. This work proposes the development of an adaptive neuro-fuzzy inference system (ANFIS) angular rotor speed estimator applied to vector and scalar drives. A multi-frequency training of ANFIS is proposed, initially for a V/f scheme and after that a vector drive with magnetizing flux oriented control is proposed. In the literature ANFIS has been commonly proposed as a speed controller in substitution of the classical PI controller of the speed control loop. This paper investigates the ANFIS as an open-loop speed estimator instead. The subtractive clustering technique was used as strategy for generating the membership functions for all the incoming signal inputs of ANFIS. This provided a better analysis of the training data set improving the comprehension of the estimator. Additionally the subtractive cluster technique allowed the training with experimental data corrupted by noise improving the estimator robustness. Simulations to evaluate the performance of the estimator considering the V/f and vector drive system were realized using the Matlab/Simulink® software. Finally experimental results are presented to validate the ANFIS open loop estimator. |
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| Keywords: | Neuro-fuzzy Estimation Induction motor ANFIS |
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