Efficient On-Line Detection Scheme of Voltage Events Using Quadrature Method

Short-duration RMS voltage variations, namely sag, swell and interruption, are characterized by the variations of RMS voltage values and time durations. In this paper, the quadrature method for calculating RMS voltage is used to efficiently detect these power quality events. For comparison, the same power quality events are detected using conventional RMS calculation methods. Different voltage events are simulated and the results of the quadrature and conventional methods are compared. An experimental real-time monitoring system for voltage events based on the LabVIEW platform is built, and several voltage events are tested and evaluated by the developed setup using quadrature and conventional methods. Both simulation and experimental results validates the superiority of the quadrature method for most of the considered scenarios in terms of accuracy and robustness. 

[1]  N. S. Srivatchan,et al.  Half Cycle Discrete Transformation for Voltage Sag Improvement in an Islanded Microgrid using Dynamic Voltage Restorer , 2018 .

[2]  Carmine Landi,et al.  Survey on Voltage Dip Measurements in Standard Framework , 2014, IEEE Transactions on Instrumentation and Measurement.

[3]  Nelson Kagan,et al.  Influence of RMS variation measurement protocols on electrical system performance indices for voltage sags and swells , 2000, Ninth International Conference on Harmonics and Quality of Power. Proceedings (Cat. No.00EX441).

[4]  E. Perez,et al.  Limitations in the Use of R.M.S. Value in Power Quality Analysis , 2006, 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings.

[5]  J. R. Macedo,et al.  Practical Aspects of Performance Tests on Power Quality Analyzers , 2012 .

[6]  J. Howard,et al.  Quadrature sampling phase detection , 1994 .

[7]  Ferhat Ucar,et al.  Power Quality Event Detection Using a Fast Extreme Learning Machine , 2018 .

[8]  S. Wetterlin A Method of Using Quadrature Sampling to Measure Phase and Magnitude , 2008 .

[9]  Gerald T. Heydt,et al.  On the use of RMS values in power quality assessment , 2003 .

[10]  E. Feilat,et al.  Detection and Classification of Voltage Variations Using Combined Envelope-Neural Network Based Approach , 2017 .

[11]  M. A. Abido,et al.  Implementation of quadrature based RMS calculation on real-time power monitoring systems , 2013, 2013 IEEE Power and Energy Conference at Illinois (PECI).

[12]  Özgül Salor-Durna,et al.  Assessment of RMS computation in terms of power quality event detection , 2015, 2015 23nd Signal Processing and Communications Applications Conference (SIU).

[13]  Antonio Moschitta,et al.  Performance comparison of advanced techniques for voltage dip detection , 2011, 2011 IEEE International Instrumentation and Measurement Technology Conference.

[14]  M. A. Abido,et al.  Characterization of short-duration voltage events , 2012, 2012 IEEE International Conference on Power and Energy (PECon).

[15]  Pankaj V. Gautam,et al.  Detection, characterization and classification of short duration voltage events using DWT and fuzzy logic , 2017, 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA).

[16]  M.H.J. Bollen,et al.  Automatic classification of power system events using RMS voltage measurements , 2002, IEEE Power Engineering Society Summer Meeting,.

[17]  Math Bollen,et al.  Understanding Power Quality Problems: Voltage Sags and Interruptions , 1999 .