Open AccessImprovised predictive torque control strategy for an open end winding induction motor drive fed with four‐level inversion using normalised weighted sum model
Author(s) -
Kunisetti Venkata Praveen Kumar,
Kodumur Meesala Ravi Eswar,
Thippiripati Vinay Kumar
Publication year - 2018
Publication title -
iet power electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.637
H-Index - 77
eISSN - 1755-4543
pISSN - 1755-4535
DOI - 10.1049/iet-pel.2017.0594
Subject(s) - control theory (sociology) , weighting , torque ripple , direct torque control , induction motor , torque , inverter , computer science , voltage , engineering , control (management) , artificial intelligence , physics , electrical engineering , thermodynamics , acoustics
Open end winding induction motor (OEWIM) drives are better alternate for multi‐level inverter fed induction motor drives. OEWIM drives can be used in industries and electric vehicles but they entail ripple‐free torque. Predictive torque control (PTC) strategy offers high dynamic performance and lesser ripple in torque, flux when compared with direct torque control. Classical PTC involves high switching frequencies and empirical methods to select weighting factors. The selection and tuning of weighting factors are cumbersome. In this article, a new normalised weighted sum model (WSM) based PTC of four‐level inverter fed OEWIM is introduced to curtail torque, flux ripples, switching frequency and enhance the selection of weighting factors. The proposed algorithm uses multi‐objective cost function and the optimisation of cost function is performed by using normalised WSM. The normalisation of individual cost function simplifies the selection of weighting factors to select optimal voltage vector. As a result, the proposed PTC offers all the features of classical PTC and overcomes the difficulties involved in classical PTC. Simulation and experimental studies are performed on dual inverter fed OEWIM with four‐level inversion. The effectiveness of proposed algorithm is verified by comparing proposed PTC algorithm with classical PTC algorithm.