Power quality disturbance identification using morphological pattern spectrum and probabilistic neural network
作者： Z. M. Chen , M. S. Li , T. Y. Ji , Q. H. Wu
DOI: 10.1109/PESGM.2015.7285920
关键词: Classifier (UML) 、 Harmonics 、 Feature vector 、 Engineering 、 Morphological pattern 、 Feature extraction 、 Artificial neural network 、 Swell 、 Artificial intelligence 、 Probabilistic neural network 、 Pattern recognition
摘要: This paper proposes a method for identification of power quality (PQ) disturbances using morphological pattern spectrum (MPS) and probabilistic neural network (PNN). The PQ disturbance signals are decomposed by three-order MPS to extract number features which used identification. These compose feature vector train PNN classifier. trained is employed classify signals. proposed tested 760 with additive noise, including sag, swell, interruption, harmonics, notching, oscillatory fluctuation, simulated according the IEEE 1159-2009 standard. results demonstrate that extracted effective classifies high accuracy rate.
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