Real-time system for automatic detection and classification of single and multiple power quality disturbances

•Hardware implementation through FPGA and 400 MHz Real-time processor.•Classification of twenty disturbance classes (fourteen multiple ones).•Implementation of a simple detector allows sending only disturbance data to the classifier.•Real-time operation, allowing application in electrical power syst...

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Published inMeasurement : journal of the International Measurement Confederation Vol. 128; pp. 276 - 283
Main Authors Ribeiro, Eduardo G., Mendes, Thais M., Dias, Guilherme L., Faria, Euler R.S., Viana, Felipe M., Barbosa, Bruno H.G., Ferreira, Danton D.
Format Journal Article
LanguageEnglish
Published London Elsevier Ltd 01.11.2018
Elsevier Science Ltd
Subjects
Classification
Disturbances
Electrical measurement
Electricity distribution
Energy resources
Field programmable gate arrays
Higher-order statistics
LabVIEW
Microprocessors
Neuro-tree
Real time
Smart grid
Waveform analysis
Real-Time
Higher-order statistics
Neuro-tree
LabVIEW
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ISSN0263-2241
1873-412X
DOI10.1016/j.measurement.2018.06.059

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Summary:•Hardware implementation through FPGA and 400 MHz Real-time processor.•Classification of twenty disturbance classes (fourteen multiple ones).•Implementation of a simple detector allows sending only disturbance data to the classifier.•Real-time operation, allowing application in electrical power systems monitoring.•LabVIEW interface which shows acquired waveforms, reports and disturbances. It is known that the quality of power has been the subject of several researches aiming to provide relevant information to users of electrical systems that are becoming increasingly smart. This study presents an approach for single and multiple power quality disturbance detection and classification using multidimensional analysis, higher-order statistics and a neuro-tree based classifier. The system was implemented in an FPGA (Field Programmable Gate Array), a real-time processor and a remote computer, with LabVIEW interface. This implementation enables real-time execution and its application to monitor smart grids. It is able to detect deviations in the measured voltage waveform from the nominal one and classify 20 classes of single and multiple disturbances with a global efficiency upper to 97%.
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ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2018.06.059