Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification

Abstract

Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification. SN Computer Science. 3. 10.1007/s42979-021-00904-1. Power Quality Disturbances (PQDs) commonly occur in large scale power systems and networks that rise critical issues. Therefore, an automated recognition and mitigation of PQDs is necessary. In this scenario, an efficient adaptive rate technique is proposed for viable features mining and recognition of PQDs. Event-driven A/D converters (EDADCs) are used to acquire PQD signals. To accurately segment the sampled signal, an appealing approach is used. A time-domain evaluation is carried out in the next phase to investigate the characteristics of these fragments. The mature machine learning algorithms are employed to carry out the classification. Compared to conventional counterparts, the findings indicate a decrease of 13.26 times in collected information. An average maximum identification precision of 99.33% is achieved by the proposed method. Compared to predecessors, this confirms the considerable performance of the processing and power usage of the engineered solution while achieving high recognition accuracy.

Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification. SN Computer Science. 3. 10.1007/s42979-021-00904-1. Power Quality Disturbances (PQDs) commonly occur in large scale power systems and networks that rise critical issues. Therefore, an automated recognition and mitigation of PQDs is necessary. In this scenario, an efficient adaptive rate technique is proposed for viable features mining and recognition of PQDs. Event-driven A/D converters (EDADCs) are used to acquire PQD signals. To accurately segment the sampled signal, an appealing approach is used. A time-domain evaluation is carried out in the next phase to investigate the characteristics of these fragments. The mature machine learning algorithms are employed to carry out the classification. Compared to conventional counterparts, the findings indicate a decrease of 13.26 times in collected information. An average maximum identification precision of 99.33% is achieved by the proposed method. Compared to predecessors, this confirms the considerable performance of the processing and power usage of the engineered solution while achieving high recognition accuracy.

Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification. SN Computer Science. 3. 10.1007/s42979-021-00904-1. Power Quality Disturbances (PQDs) commonly occur in large scale power systems and networks that rise critical issues. Therefore, an automated recognition and mitigation of PQDs is necessary. In this scenario, an efficient adaptive rate technique is proposed for viable features mining and recognition of PQDs. Event-driven A/D converters (EDADCs) are used to acquire PQD signals. To accurately segment the sampled signal, an appealing approach is used. A time-domain evaluation is carried out in the next phase to investigate the characteristics of these fragments. The mature machine learning algorithms are employed to carry out the classification. Compared to conventional counterparts, the findings indicate a decrease of 13.26 times in collected information. An average maximum identification precision of 99.33% is achieved by the proposed method. Compared to predecessors, this confirms the considerable performance of the processing and power usage of the engineered solution while achieving high recognition accuracy.

Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification. SN Computer Science. 3. 10.1007/s42979-021-00904-1. Power Quality Disturbances (PQDs) commonly occur in large scale power systems and networks that rise critical issues. Therefore, an automated recognition and mitigation of PQDs is necessary. In this scenario, an efficient adaptive rate technique is proposed for viable features mining and recognition of PQDs. Event-driven A/D converters (EDADCs) are used to acquire PQD signals. To accurately segment the sampled signal, an appealing approach is used. A time-domain evaluation is carried out in the next phase to investigate the characteristics of these fragments. The mature machine learning algorithms are employed to carry out the classification. Compared to conventional counterparts, the findings indicate a decrease of 13.26 times in collected information. An average maximum identification precision of 99.33% is achieved by the proposed method. Compared to predecessors, this confirms the considerable performance of the processing and power usage of the engineered solution while achieving high recognition accuracy.

Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification. SN Computer Science. 3. 10.1007/s42979-021-00904-1. Power Quality Disturbances (PQDs) commonly occur in large scale power systems and networks that rise critical issues. Therefore, an automated recognition and mitigation of PQDs is necessary. In this scenario, an efficient adaptive rate technique is proposed for viable features mining and recognition of PQDs. Event-driven A/D converters (EDADCs) are used to acquire PQD signals. To accurately segment the sampled signal, an appealing approach is used. A time-domain evaluation is carried out in the next phase to investigate the characteristics of these fragments. The mature machine learning algorithms are employed to carry out the classification. Compared to conventional counterparts, the findings indicate a decrease of 13.26 times in collected information. An average maximum identification precision of 99.33% is achieved by the proposed method. Compared to predecessors, this confirms the considerable performance of the processing and power usage of the engineered solution while achieving high recognition accuracy.

Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification. SN Computer Science. 3. 10.1007/s42979-021-00904-1. Power Quality Disturbances (PQDs) commonly occur in large scale power systems and networks that rise critical issues. Therefore, an automated recognition and mitigation of PQDs is necessary. In this scenario, an efficient adaptive rate technique is proposed for viable features mining and recognition of PQDs. Event-driven A/D converters (EDADCs) are used to acquire PQD signals. To accurately segment the sampled signal, an appealing approach is used. A time-domain evaluation is carried out in the next phase to investigate the characteristics of these fragments. The mature machine learning algorithms are employed to carry out the classification. Compared to conventional counterparts, the findings indicate a decrease of 13.26 times in collected information. An average maximum identification precision of 99.33% is achieved by the proposed method. Compared to predecessors, this confirms the considerable performance of the processing and power usage of the engineered solution while achieving high recognition accuracy.

Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification. SN Computer Science. 3. 10.1007/s42979-021-00904-1. Power Quality Disturbances (PQDs) commonly occur in large scale power systems and networks that rise critical issues. Therefore, an automated recognition and mitigation of PQDs is necessary. In this scenario, an efficient adaptive rate technique is proposed for viable features mining and recognition of PQDs. Event-driven A/D converters (EDADCs) are used to acquire PQD signals. To accurately segment the sampled signal, an appealing approach is used. A time-domain evaluation is carried out in the next phase to investigate the characteristics of these fragments. The mature machine learning algorithms are employed to carry out the classification. Compared to conventional counterparts, the findings indicate a decrease of 13.26 times in collected information. An average maximum identification precision of 99.33% is achieved by the proposed method. Compared to predecessors, this confirms the considerable performance of the processing and power usage of the engineered solution while achieving high recognition accuracy.

Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification. SN Computer Science. 3. 10.1007/s42979-021-00904-1. Power Quality Disturbances (PQDs) commonly occur in large scale power systems and networks that rise critical issues. Therefore, an automated recognition and mitigation of PQDs is necessary. In this scenario, an efficient adaptive rate technique is proposed for viable features mining and recognition of PQDs. Event-driven A/D converters (EDADCs) are used to acquire PQD signals. To accurately segment the sampled signal, an appealing approach is used. A time-domain evaluation is carried out in the next phase to investigate the characteristics of these fragments. The mature machine learning algorithms are employed to carry out the classification. Compared to conventional counterparts, the findings indicate a decrease of 13.26 times in collected information. An average maximum identification precision of 99.33% is achieved by the proposed method. Compared to predecessors, this confirms the considerable performance of the processing and power usage of the engineered solution while achieving high recognition accuracy.

Power Quality Disturbances (PQDs) commonly occur in large scale power systems and networks that rise critical issues. Therefore, an automated recognition and mitigation of PQDs is necessary. In this scenario, an efficient adaptive rate technique is proposed for viable features mining and recognition of PQDs. Event-driven A/D converters (EDADCs) are used to acquire PQD signals. To accurately segment the sampled signal, an appealing approach is used. A time-domain evaluation is carried out in the next phase to investigate the characteristics of these fragments. The mature machine learning algorithms are employed to carry out the classification. Compared to conventional counterparts, the findings indicate a decrease of 13.26 times in collected information. An average maximum identification precision of 99.33% is achieved by the proposed method. Compared to predecessors, this confirms the considerable performance of the processing and power usage of the engineered solution while achieving high recognition accuracy.