Investigation and analysis of high performance green energy induction motor drive with intelligent estimator

• Published in: Renewable energy Vol. 87; pp. 965 - 976
• Main Authors: Chitra, A., Himavathi, S.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2016
• Subjects: algorithms; Back propagation; IFOC; learning; MPPT; Neural networks; renewable energy sources; Rotor resistance estimators; solar collectors; topology; Back propagation; MPPT; IFOC; Neural networks; Rotor resistance estimators; PV
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2015.07.084

This paper attempts to enhance the performance of a green energy induction motor drive. The electronic power converters become indispensable part of the renewable energy systems (RES). The solar photovoltaic (PV) system is efficiently operated with artificial neural network (ANN) based maximum power point tracking (MPPT) algorithm. The inverter topologies for the green drive scheme are analyzed. To improve the drive performance a reduced switch multilevel inverter (RSMLI) is employed. As indirect field oriented control (IFOC) is used, the drive demands on-line estimation of rotor resistance. A neural learning model reference adaptive scheme (NL-MRAS) based rotor resistance estimator is found to exhibit good dynamic performance. This work also investigates the performance of the green drive with an intelligent estimator. The performance enhancement of the green energy drive obtained by ANN based MPPT for the PV system, a reduced switch MLI and an intelligent estimator is presented. [Display omitted] •PV system with IC and ANN based MPPT is developed and the results are presented.•The RSMLI fed vector controlled induction motor drive is designed in MATLAB.•A new modified neural learning algorithm is proposed for the neural based Rr estimator.•The enhanced performance of the green drive with and without Rr estimator is presented.