Loss minimization DTC electric motor drive system based on adaptive ANN strategy

• Main Authors: Sim, Sy Yi, Mulyo Utomo, Wahyu, Hwang, Goh Hui, Kai, Chien Siong, Lim, Alvin John Meng Siang, Zambri, Nor Aira, Buswig, Yonis M. Y., Law, Kah Haw, Sim, Gia Yi
• Format: Article
• Published: Institute of Advanced Engineering and Science 2020
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.uthm.edu.my/6399/; https://dx.doi.org/10.11591/ijpeds.v11.i2.pp618-624

Electric motor drive systems (EMDS) have been recognized as one of the most promising motor systems recently due to their low energy consumption and reduced emissions. With only some exceptions, EMDS are the main source for the provision of mechanical energy in industry and accounts for about 60% of global industrial electricity consumption. Large energy efficiency potentials have been identified in EMDS with very short payback time and high-cost effectiveness. Typical, during operation at rated mode, the motor drive able to hold its good efficiencies. However, a motor usually operates out from rated mode in many applications, especially while under light load, it reduced the motor’s efficiency severely. Hence, it is necessary that a conventional drive system to embed with loss minimization strategy to optimize the drive system efficiency over all operation range. Conventionally, the flux value is keeping constantly over the range of operation, where it should be highlighted that for any operating point, the losses could be minimize with the proper adjustment of the flux level to a suitable value at that point. Hence, with the intention to generate an adaptive flux level corresponding to any operating point, especially at light load condition, an online learning Artificial Neural Network (ANN) controller was proposed in this study, to minimize the system losses. The entire proposed strategic drive system would be verified under the MATLAB/Simulink software environment. It is expected that with the proposed online learning Artificial Neural Network controller efficiency optimization algorithm can achieve better energy saving compared with traditional blended strategies.