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Pattern recognition applications for power system disturbance classification
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DOI：10.1109/TPWRD.2002.1022786.png)
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Abstract
En 中文
This paper presents an automated online disturbance classification technique. This technique is based on wavelet multiresolution analysis and pattern recognition techniques. The wavelet-multiresolution transform is introduced as a powerful tool for feature extraction in order to classify different disturbances. Minimum Euclidean distance, k-nearest neighbor, and neural network classifiers are used to evaluate the efficiency of the extracted features.
Keywords:
k-nearest neighbor
minimum Euclidean distance
multiresolution signal decomposition
neural network recognition techniques
power quality
wavelet analysis
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3.7
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9.1K
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2.2W
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