Analysis of artificial intelligence in industrial drives and development of fault deterrent novel machine learning prediction algorithm. (3rd April 2022)

• Record Type: Journal Article
• Authors: Vishnu Murthy, K.; Ashok Kumar, L.
• Is Part Of: Automatika. Volume 63:Number 2(2022)
• Page Start: 349
• Page End: 364
• Publication Date: 2022-04-03
• Subjects: Artificial intelligence -- machine learning -- inverter drives -- power quality -- voltage sag; Automatic control -- Periodicals; 629.805
• DOI: 10.1080/00051144.2022.2039988
• Languages: English
• ISSNs: 0005-1144

Abstract

Abstract : Industrial sectors rely on electrical inverter drives to power their various load segments. Because the majority of their load is nonlinear, their drive system behaviour is unpredictable. Manufacturers continue to invest much in research and development to ensure that the device can resist any disturbances caused by the power system or load-side changes. The literature in this field of study depicts numerous effects caused by harmonics, a sudden inrush of currents, power interruption in all phases, leakage current effects and torque control of the system, among others. These and numerous other effects have been discovered as a result of research, and the inverter drive has been enhanced to a more advanced device than its earlier version. Despite these measures, inverter drives continue to operate poorly and frequently fail throughout the warranty term. This failure analysis is used as the basis for this research work, which presents a method for forecasting faulty sections using power system parameters. The said parameters were obtained by field-test dataset analysis in industrial premises. The prediction parameter is established by the examination of field research test data. The same data are used to train the machine learning system for future pre-emptive action. When exposed to live data feeds, the algorithm may forecast the future and suggest the same. Thus, when comparing the current status of the device to the planned study effort, the latter provides an advantage in terms of safeguarding the device and avoiding a brief period of total shutdown. As a result, the machine learning model was trained using the tested dataset and employed for prediction purposes; as a result, it provides a more accurate prediction, which benefits end consumers rather than improving the power system's grid-side difficulties.