Title :
Recognition of Multiple PQ Disturbances Using Wavelet-based Neural Networks - Part 2: Implementation and Applications
Author :
Cheng-Long Chuang ; Yen-Ling Lu ; Tsong-Liang Huang ; Ying-Tung Hsiao ; Joe-Air Jiang
Author_Institution :
Graduate Inst. of Bio-Ind. Mechatronics Eng., Nat. Taiwan Univ., Taipei
Abstract :
For part I see ibid., p.Z001593-8 (2005). This work proposes and implements a novel classifier integrated with the wavelet transform and dynamic structural neural network for recognizing multiple power quality disturbances in a measured waveform. The classifier has been tested under different PQ events such as with single disturbance, dual disturbances and multiple disturbances. The experimental results show that the proposed classifier can achieve high accuracy rate more than 97% under various test cases
Keywords :
neural nets; pattern classification; power engineering computing; power supply quality; power system faults; power system measurement; wavelet transforms; dynamic structural neural network; multiple power quality disturbances; pattern classifier; pattern recognition; wavelet transform; Amplitude estimation; Artificial neural networks; Feature extraction; Graphical user interfaces; Neural networks; Power quality; Testing; Voltage fluctuations; Wavelet analysis; Wavelet transforms; Power quality; neural networks; pattern recognition; wavelet transform;
Conference_Titel :
Transmission and Distribution Conference and Exhibition: Asia and Pacific, 2005 IEEE/PES
Conference_Location :
Dalian
Print_ISBN :
0-7803-9114-4
DOI :
10.1109/TDC.2005.1546957