Investigation of power quality categorisation and simulating it's impact on sensitive electronic equipment

With an increasing usage of sensitive electronic equipment power quality has become a major concern now. One critical aspect of power quality studies is the ability to perform automatic power quality data analysis and categorization. The objective of this paper is to present a technique based on fuzzy logic to categorize power quality events and to simulate the impact of power quality on sensitive equipment. Inherent features are extracted from recorded waveforms using Fourier and wavelet analyses and fed into a fuzzy expert system. The categorization technique has been implemented using the Fourier, Wavelet and fuzzy logic toolboxes in MATLAB and tested with real power quality measured data. The impact of power quality on the operation of sensitive equipment has been illustrated through simulations in MATLAB SIMULINK. Such study is essential to predict the performance of modern loads and also to be able to explain why a specific load fails during a power quality event. The findings are reported in detail in this paper.

[1]  M. M. Morcos,et al.  Artificial Intelligence and Advanced Mathematical Tools for Power Quality Applications: A Survey , 2001, IEEE Power Engineering Review.

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

[3]  Martin Greiner,et al.  Wavelets , 2018, Complex..

[4]  D.D. Sabin,et al.  Probing power quality data , 1994, IEEE Computer Applications in Power.

[5]  Paulo F. Ribeiro,et al.  Power electronics, power quality and modern analytical tools: the impact on electrical engineering education , 1994, Proceedings of 1994 IEEE Frontiers in Education Conference - FIE '94.

[6]  A. Al-Rawi,et al.  Wavelets and power system transient analysis , 1998, IMTC/98 Conference Proceedings. IEEE Instrumentation and Measurement Technology Conference. Where Instrumentation is Going (Cat. No.98CH36222).

[7]  Edward J. Powers,et al.  Power quality disturbance waveform recognition using wavelet-based neural classifier. II. Application , 2000 .

[8]  M. Kezunovic,et al.  A Novel Software Implementation Concept for Power Quality Study , 2001, IEEE Power Engineering Review.

[9]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[10]  J. Yen,et al.  Fuzzy Logic: Intelligence, Control, and Information , 1998 .

[11]  J. W. Resende,et al.  Identification of power quality disturbances using the MATLAB wavelet transform toolbox , 2001 .

[12]  Zhao Yang Dong,et al.  Power Quality Investigation with Wavelet Techniques , 2002 .

[13]  A. Mansoor,et al.  Effects of voltage sags on AC motor drives , 1997, 1997 IEEE Annual Textile, Fiber and Film Industry Technical Conference.

[14]  Zhao Yang Dong,et al.  Enhancing neural network electricity load forecast with wavelet techniques , 2002 .

[15]  Edward J. Powers,et al.  Power quality disturbance waveform recognition using wavelet-based neural classifier. I. Theoretical foundation , 2000 .

[16]  H. Wayne Beaty,et al.  Electrical Power Systems Quality , 1995 .