Assessing the performance of a modified S-transform with probabilistic neural network, support vector machine and nearest neighbour classifiers for single and multiple power quality disturbances identification-Reference-Cited by-同舟云学术

Assessing the performance of a modified S-transform with probabilistic neural network, support vector machine and nearest neighbour classifiers for single and multiple power quality disturbances identification

Published:2017-07-06 Issue:4 Volume:31 Page:1041-1060
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Shamachurn Heman

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s00521-017-3136-z.pdf

Reference31 articles.

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3. Khokhar S, Mohd Zin AAB, Mokhtar ASB, Pesaran M (2015) A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances. Renew Sustain Energy Rev 51:1650–1663

4. Zhang S, Li P, Zhang L, Li H, Jiang W, Hu Y (2016) Modified S transform and ELM algorithms and their applications in power quality analysis. Neurocomputing 185:231–241

5. Reddy MV, Sodhi R (2016) A rule-based S-transform and AdaBoost based approach for power quality assessment. Electr Power Syst Res 134:66–79

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