中国电气工程学报（英文） ›› 2021, Vol. 7 ›› Issue (2): 70-82.doi: 10.23919/CJEE.2021.000017

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  • 收稿日期:2020-07-23 修回日期:2020-10-03 接受日期:2020-10-29 出版日期:2021-06-25 发布日期:2021-07-08

Adaptive Neuro Fuzzy Inference System Based Decoupled Control for Neutral Point Clamped Multi Level Inverter Fed Induction Motor Drive

Giribabu Dyanamina*, Sanjay Kumar Kakodia   

  1. Department of Electrical Engineering, Maulana Azad National Institute of Technology, Bhopal, M.P 462003, India
  • Received:2020-07-23 Revised:2020-10-03 Accepted:2020-10-29 Online:2021-06-25 Published:2021-07-08
  • Contact: * E-mail: dgiribabu208@gmail.com
  • About author:Giribabu Dyanamina received a B.Tech. degree in electrical and electronics engineering from Jawaharlal Nehru Technological University (JNTU), Hyderabad, Telangana, India in 2006, and his M.T. degree in power electronics from JNTU in 2008, and Ph.D. degree in electrical engineering in the Indian Institute of Technology Roorkee (IITR), Uttarakhand, India. In 2013, he joined the National Institute of Technology Kurukshetra (NIT-KKR), Haryana, India, as an assistant professor in the Electrical Engineering Department. In 2019, he joined the Maulana Azad National Institute of Technology Bhopal (MANIT-B), India, the Electrical Engineering Department, where he works as an assistant professor. He is a senior IEEE member. His research interests include sensorless speed control of electric drives, multi-level inverters, AI, and renewable energy sources.
    Sanjay Kumar Kakodia received a B.Tech. degree in electrical engineering from Rajasthan Technical University (RTU) Kota, Rajasthan, India, in 2016. He received his M.Tech. degree in power electronics and drives from the National Institute of Technology, Kurukshetra (NIT-KKR), India in 2019. He was worked as an assistant professor in MITRC Alwar, Rajasthan, India. He is currently research scholar in the Electrical Engineering Department, Maulana Azad National Institute of Technology Bhopal (MANIT-B), India. His research interests include sensorless speed control of IM drives and multilevel inverters.

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Abstract: The presence of an integrator in a reference model of a rotor flux-based model reference adaptive system (RF-MRAS) and non-linearity of the inverter in the output voltage degrade the speed response of the sensorless operation of the electric drive system in terms of DC drift, initial value issues, and inaccurate voltage acquisition. To improve the speed response, a compensating voltage component is supplemented by an amending integrator. The compensating voltage is a coalition of drift and offset voltages, and reduces DC drift and initial value issues. During low-speed operation, inaccurate voltage acquisition distorts the stator voltage critically, and it becomes considerable when the stator voltage of the machine is low. Implementing a three-level neutral point clamped inverter in speed-sensorless decoupled control of an induction motor improves the performance of the drive with superior quality of inverter output voltage. Further, the performance of the induction motor drive is improved by replacing the proportional-integral (PI) controller in the adaption mechanism of RF-MRAS with an adaptive neuro-fuzzy inference system (ANFIS) controller. A prototype model of the three-level neutral point clamped inverter (3L-NPC)-fed induction motor drive is fabricated in a laboratory, and its performance for a RF-MRAS, modified RFMRAS, and modified RFMRAS using ANFIS are compared using different benchmark tests.

Key words: Induction motor drive, vector control, neutral point clamped inverter, model reference adaptive system, ANFIS