E3S Web Conf.
Volume 87, 20191st International Conference on Sustainable Energy and Future Electric Transportation (SeFet 2019)
|Number of page(s)
|22 February 2019
Artificial Neural Networks based SPWM technique for speed control of Permanent Magnet Synchronous Motor
1 Professor, EEE Department, GRIET, Hyderabad, India
2 Research Scholar, EEE Department, TKR Engineering College, Hyderabad
* Corresponding author: email@example.com
The advancement of industry apparatuses for some methods with specific tasks to control the working of a few actuators on the field. Among these actuators, Permanent magnet synchronous motor drives are a mainly all-inclusive machine. Proficient utilization of hesitance torque, generally effectiveness, minor misfortunes and smaller size of the motor are the principle attractions of PMSM when contrasted and different drivers. Precise and rapid torque reaction is one of the parameters to determine differentiating arrangements in the ongoing past. The field-situated power perceived the likely and vigorous answer to accomplish these prerequisites to empower the figuring of streams and voltages in different parts of the inverter and motor under transient and consistent conditions. The primary objective of this paper is to investigate Artificial Neural Network based control of speed for PMSM in both open and closed loop under no-load and loaded condition. A shut circle control framework with ANN procedure in the speed circle intended to work in steady torque and transition debilitating districts. MATLAB reproduction performed in the wake of preparing the neural system (directed learning), results for reference control applications are adequate and appropriate in the process business. Speed control in shut circle at different stacking conditions talked about in detail.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.