Integrating the S-PQDA software tool in the utility power quality management system

This paper presents a smart power quality data a analyzer (S-PQDA) or power quality diagnosis software (PQDS) tool that performs power quality (PQ) diagnosis on the PQ disturbance data recorded by an online PQ monitoring system. The software tool enables power utility engineers to perform automatic PQ disturbance detection, classification and diagnosis of the disturbances. The PQDS also assists the power utility engineers in identifying the existence of incipient faults due to partial discharges in the cable compartment. The overall accuracy of the software in performing PQ diagnosis is 96.4%.

[1]  Bijaya Ketan Panigrahi,et al.  Detection and classification of power quality disturbances using S-transform and modular neural network , 2008 .

[2]  T. Lobos,et al.  Automated classification of power-quality disturbances using SVM and RBF networks , 2006, IEEE Transactions on Power Delivery.

[3]  Dusmanta Kumar Mohanta,et al.  Power System Disturbance Recognition Using Wavelet and S-Transform Techniques , 2004 .

[4]  W. E. Brumsickle,et al.  Operational experience with a nationwide power quality and reliability monitoring system , 2003, 38th IAS Annual Meeting on Conference Record of the Industry Applications Conference, 2003..

[5]  Math Bollen,et al.  The influence of motor re-acceleration on voltage sags , 1994, Proceedings of 1994 IEEE Industry Applications Society Annual Meeting.

[6]  H. He,et al.  A self-organizing learning array system for power quality classification based on wavelet transform , 2006, IEEE Transactions on Power Delivery.

[7]  Huang Weili,et al.  Application of dynamic time-frequency analysis for power quality event classification and recognition , 2009, 2009 Chinese Control and Decision Conference.

[8]  N. Ertugrul,et al.  A comparative study on effective signal processing tools for power quality monitoring , 2005, 2005 European Conference on Power Electronics and Applications.

[9]  I.W.C. Lee,et al.  An S-transform based neural pattern classifier for non-stationary signals , 2002, 6th International Conference on Signal Processing, 2002..

[10]  Bijaya K. Panigrahi,et al.  Hybrid signal processing and machine intelligence techniques for detection, quantification and classification of power quality disturbances , 2009, Eng. Appl. Artif. Intell..

[11]  G. Panda,et al.  Power Quality Analysis Using S-Transform , 2002, IEEE Power Engineering Review.

[12]  G. Yang,et al.  A new method for classification of power quality , 2008, 2008 3rd IEEE Conference on Industrial Electronics and Applications.

[13]  Azah Mohamed,et al.  Identification of Multiple Power Quality Disturbances using S-Transform and Rule Based Classification Technique , 2009 .