Sparse Linear Models Applied to Power Quality Disturbance Classification

详细信息    查看全文

• 关键词：Disturbances classification ; Overcomplete representation ; Power quality ; Sparse linear models ; Statistical signal processing
• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：10125
• 期：1
• 页码：521-529
• 丛书名：Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
• ISBN：978-3-319-52277-7
• 卷排序：10125

文摘

The automatic recognition of Power Quality (PQ) disturbances can be seen as a pattern recognition problem, in which different types of waveform distortion are differentiated based on their features. Similar to other quasi-stationary signals, PQ disturbances can be decomposed into time-frequency dependent components by using time-frequency or time-scale dictionaries. Short-time Fourier, Wavelets, and Stockwell transforms are some of the most common dictionaries used in the PQ community. Previous works about PQ disturbance classification have been restricted to the use of one of the above dictionaries. Taking advantage of the theory behind sparse linear models (SLMs), we introduce a sparse method for PQ representation, starting from overcomplete dictionaries. We apply Group Lasso. We employ different types of time-frequency dictionaries to characterize PQ disturbances and evaluate their performance under different pattern recognition algorithms. We show that SLMs promote the sparse basis selection improving the classification accuracy.