Signal-piloted processing and machine learning based efficient power quality disturbances recognition.

ABSTRACT: Significant losses can occur for various smart grid stake holders due to the Power Quality Disturbances (PQDs). Therefore, it is necessary to correctly recognize and timely mitigate the PQDs. In this context, an emerging trend is the development of machine learning assisted PQDs management. Based on the conventional processing theory, the existing PQDs identification is time-invariant. It can result in a huge amount of unnecessary information being collected, processed, and transmitted. Consequently, needless processing activities, power consumption and latency can occur. In this paper, a novel combination of signal-piloted acquisition, adaptive-rate segmentation and time-domain features extraction with machine learning tools is suggested. The signal-piloted acquisition and processing brin

SUBMITTER: Mian Qaisar S 

PROVIDER: S-EPMC8162588 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature