论文信息 - Application of computational intelligence for diagnosing Power Quality disturbances

Application of computational intelligence for diagnosing Power Quality disturbances

This paper presents the application of signal processing and artificial intelligence techniques for performing automated power quality (PQ) diagnosis. This new diagnosis system is named as the Power Quality Diagnostic System or PQDS. The PQDS is developed using the S-Transform (ST) and the Support Vector Regression (SVR) techniques. The PQDS has been successfully implemented in Malaysia and has assisted the power utility's engineers in verifying the types, sources and causes of the recorded PQ disturbances by the online power quality monitoring system (PQMS). The PQDS gave perfect (100%) accuracy in diagnosing voltage sags.

A. Mohamed | M. F. Faisal

[1] Lalu Mansinha,et al. Localization of the complex spectrum: the S transform , 1996, IEEE Trans. Signal Process..

[2] S. Mishra,et al. Detection and Classification of Power Quality Disturbances Using S-Transform and Probabilistic Neural Network , 2008, IEEE Transactions on Power Delivery.

[3] P.K. Dash,et al. Multiresolution S-transform-based fuzzy recognition system for power quality events , 2004, IEEE Transactions on Power Delivery.

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

[5] 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..

[6] N. Ertugrul,et al. Investigation of Effective Automatic Recognition Systems of Power-Quality Events , 2007, IEEE Transactions on Power Delivery.

[7] 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..

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

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