12/20/2023 0 Comments Signal processing machine learning![]() ![]() The project is funded by the Effat University, Jeddah, KSA under the grant No. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the manuscript and its Supporting Information files.įunding: S.M.Q secures funding for this project. Received: ApAccepted: Published: May 28, 2021Ĭopyright: © 2021 Saeed Mian Qaisar. PLoS ONE 16(5):Įditor: Thippa Reddy Gadekallu, Vellore Institute of Technology: VIT University, INDIA Results show that the system attains a 6.75-fold reduction in the collected information and the processing load and secures the 98.05% accuracy of classification.Ĭitation: Mian Qaisar S (2021) Signal-piloted processing and machine learning based efficient power quality disturbances recognition. The applicability of the suggested approach is studied for automated recognition of the power signal’s major voltage and transient disturbances. It permits to avoid any biasness of findings. Multiple metrics are used to test the success of classification. A comparison is made among the k-Nearest Neighbor, Naïve Bayes, Artificial Neural Network and Support Vector Machine. The classification is accomplished by using robust machine learning algorithms. Additionally, a reduced computational cost and latency of classifier is promised. Therefore, a remarkable reduction can be secured in the data storage, processing and transmission requirement towards the post classifier. The signal-piloted acquisition and processing brings real-time compression. In this paper, a novel combination of signal-piloted acquisition, adaptive-rate segmentation and time-domain features extraction with machine learning tools is suggested. Consequently, needless processing activities, power consumption and latency can occur. It can result in a huge amount of unnecessary information being collected, processed, and transmitted. ![]() Based on the conventional processing theory, the existing PQDs identification is time-invariant. In this context, an emerging trend is the development of machine learning assisted PQDs management. Therefore, it is necessary to correctly recognize and timely mitigate the PQDs. Significant losses can occur for various smart grid stake holders due to the Power Quality Disturbances (PQDs). ![]()
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