Condition Monitoring and Feature Extraction of Stator Current Phasors for Enhanced Fault Diagnosis in AC Drive 
Jyothi R1, Tejas Holla2, Umesh NS3, Uma Rao K4, Jayapal R5
1Jyothi R*, Department of Electrical and Electronics Engineering, RV College of Engineering, Bangalore (Karnataka), India.
2Tejas Holla, Department of Electrical and Electronics Engineering, RV College of Engineering, Bangalore (Karnataka), India.
3Umesh NS, Department of Electrical and Electronics Engineering, RV College of Engineering, Bangalore (Karnataka), India.
4K Uma Rao, Department of Electrical and Electronics Engineering, R.V. College of Engineering, Bangalore (Karnataka), India.
5Jayapal R, Department of Electrical and Electronics Engineering, RV College of Engineering, Bangalore (Karnataka), India.
Manuscript received on October 11, 2021. | Revised Manuscript received on October 16, 2021. | Manuscript published on October 30, 2021. | PP: 174-180 | Volume-11 Issue-1, October 2021. | Retrieval Number: 100.1/ijeat.A31731011121 | DOI: 10.35940/ijeat.A3173.1011121
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: AC drives are employed mainly in process plants for various applications. In most industrial applications, Induction motor drives are preferred as they are robust, reliable, and efficient. Process industries have seen a paradigm shift from manual control to automatic control. Advancements in power electronics technology have led to smooth control of the induction motor using variable frequency drives over an entire speed range. Variable Frequency Drives (VFD) comprises of Voltage source inverter and a three phase squirrel cage induction motor. Various electric faults that are incipient in the VFD cause an abrupt change in circuit parameters resulting in insulation damage, reduced efficiency, and leading to catastrophic failure of the entire system. Hence, continuous monitoring of the system parameters such as stator current, speed, and the vibration of the machine is essential to diagnose incipient faults in the system. AI techniques have been effectively used in the fault diagnosis of electrical systems. In the proposed work, simulation results of machine learning-based fault diagnosis techniques are presented. Real-time IoT-based condition monitoring of the Variable Frequency Drive is also implemented for enhanced fault diagnosis of various incipient electrical faults in AC drives. The experimental results obtained are validated with the simulation data.
Keywords: Internet of things (IoT), Condition Monitoring, Machine Learning, Voltage Source Inverter, Fault Diagnosis.
Scope of the Article: Internet of Things