Multiple Chaos Synchronization System for Power Quality Classification in a Power System

This document proposes multiple chaos synchronization (CS) systems for power quality (PQ) disturbances classification in a power system. Chen-Lee based CS systems use multiple detectors to track the dynamic errors between the normal signal and the disturbance signal, including power harmonics, voltage fluctuation phenomena, and voltage interruptions. Multiple detectors are used to monitor the dynamic errors between the master system and the slave system and are used to construct the feature patterns from time-domain signals. The maximum likelihood method (MLM), as a classifier, performs a comparison of the patterns of the features in the database. The proposed method can adapt itself without the need for adjustment of parameters or iterative computation. For a sample power system, the test results showed accurate discrimination, good robustness, and faster processing time for the detection of PQ disturbances.

[1]  S. J. Huang,et al.  FPGA Realization of Wavelet Transform for Detection of Electric Power System Disturbances , 2002, IEEE Power Engineering Review.

[2]  Lin Huang,et al.  Dichotomy of nonlinear systems: Application to chaos control of nonlinear electronic circuit ✩ , 2006 .

[3]  Hsien-Keng Chen,et al.  Generation of hyperchaos from the Chen–Lee system via sinusoidal perturbation , 2008 .

[4]  Donghua Zhou,et al.  A sliding mode observer based secure communication scheme , 2005 .

[5]  Philip de Chazal,et al.  Automatic classification of heartbeats using ECG morphology and heartbeat interval features , 2004, IEEE Transactions on Biomedical Engineering.

[6]  Hsien-Keng Chen,et al.  Alternative implementation of the chaotic Chen–Lee system , 2009 .

[7]  T. Liao,et al.  Chaotic synchronization via adaptive sliding mode observers subject to input nonlinearity , 2005 .

[8]  Chun-Fei Hsu,et al.  Design of Adaptive Wavelet Neural Control System for Chaos Synchronization with Uncertainties , 2008 .

[9]  Euntai Kim,et al.  Model reference adaptive synchronization of T–S fuzzy discrete chaotic systems using output tracking control , 2007 .

[10]  Andrew W. Senior,et al.  A Combination Fingerprint Classifier , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Her-Terng Yau,et al.  Fuzzy Sliding Mode Control for a Class of Chaos Synchronization with Uncertainties , 2006 .

[12]  Whei-Min Lin,et al.  Detection and Classification of Multiple Power-Quality Disturbances With Wavelet Multiclass SVM , 2008, IEEE Transactions on Power Delivery.

[13]  Juhn-Horng Chen,et al.  Controlling chaos and chaotification in the Chen–Lee system by multiple time delays , 2008 .

[14]  Juhn-Horng Chen,et al.  Synchronization and anti-synchronization coexist in Chen–Lee chaotic systems , 2009 .

[15]  Chao-Lin Kuo,et al.  Chaos Synchronization-Based Detector for Power-Quality Disturbances Classification in a Power System , 2011, IEEE Transactions on Power Delivery.

[16]  Chia-Hung Lin,et al.  Power quality detection with classification enhancible wavelet-probabilistic network in a power system , 2005 .

[17]  Alan C. Bovik,et al.  Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images , 2005, IEEE Transactions on Medical Imaging.

[18]  Pasquale Daponte,et al.  Wavelet network-based detection and classification of transients , 2001, IEEE Trans. Instrum. Meas..

[19]  P B Persson,et al.  Complexity and "chaos" in blood pressure after baroreceptor denervation of conscious dogs. , 1995, The American journal of physiology.

[20]  Jian-Liung Chen,et al.  Combining Biometric Fractal Pattern and Particle Swarm Optimization-Based Classifier for Fingerprint Recognition , 2010 .

[21]  Chao-Lin Kuo Design of an Adaptive Fuzzy Sliding-Mode Controller for Chaos Synchronization , 2007 .

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