@ARTICLE{K._Vishnu_Murthy_Power_2022,
 author={K., Vishnu Murthy and L., Ashok Kumar},
 volume={70},
 number={3},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 pages={e141180},
 howpublished={online},
 year={2022},
 abstract={Since electrical drives have become an integral element of any industrial sector, power quality difficulties have been well expected, and delivering genuine quality of the same has proven to be a difficult challenge. Since power quality relies on load side non-linearity and high semiconductor technology consumption, it is a serious concern. The efficiency of the drive segment employed in the sector is increasingly becoming a topic of discussion in today’s market. Numerous reviews of available literature have found problems with the load side as well as with electrical drive proficiency, as a result of the issues listed above. A high level of power quality vulnerability is simply too much. Even the most advanced technology has its limits when it comes to drive operation. Research on the grid-side quality issues of electrical drives is the focus of this article. After field testing of grid power quality, each parametric analysis is performed to identify crucial parameters that can cause industrial drives to fail. Based on this discovery, a machine learning strategy was developed and an artificial intelligence technique was proposed to administer the fault deterrent prediction algorithm. An accurate forecast of anomalous behavior on the grid side ensures safe and dependable grid operation such that shutdown or failure probability is minimized to a greater extent by the results. Additional information gleaned from historical data will prove useful to equipment manufacturers in the future, providing a solution to this problem.},
 type={Article},
 title={Power quality analysis in electrical drives and a case study of artificial intelligence prediction algorithm for fault deterrent electrical drives},
 URL={http://www.journals.pan.pl/Content/123208/PDF-MASTER/2615_BPASTS_2022_70_3.pdf},
 doi={10.24425/bpasts.2022.141180},
 keywords={voltage sag/swell, voltage imbalance, machine learning, inverter drives, artificial intelligence},
}