Abstract
Using Phase Space Reconstruction (PSR) and Support Vector Machines (SVMs), a novel approach for power disturbance classification is presented. The types of concerned disturbances include voltage sags, voltage swells, voltage interruptions, impulsive transients, harmonics and flickers. PSR is applied for disturbance feature extraction. Based on PSR, power disturbance trajectories are constructed and then converted into binary images through encoding. Four distinctive features are proposed as the inputs of SVM classifier. Simulation results show that the classification method is effective and it requires less training samples.
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Li, Z., Wu, W. (2007). Phase Space Reconstruction Based Classification of Power Disturbances Using Support Vector Machines. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_73
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DOI: https://doi.org/10.1007/978-3-540-71701-0_73
Publisher Name: Springer, Berlin, Heidelberg
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