{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T16:02:30Z","timestamp":1773676950907,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2015,8,12]],"date-time":"2015-08-12T00:00:00Z","timestamp":1439337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Aiming at the combined power quality +disturbance recognition, an automated recognition method based on wavelet packet entropy (WPE) and modified incomplete  S-transform (MIST) is proposed in this paper. By combining wavelet packet Tsallis singular entropy, energy entropy and MIST, a 13-dimension vector of different power quality (PQ) disturbances including single disturbances and combined disturbances is extracted. Then, a ruled decision tree is designed to recognize the combined disturbances. The proposed method is tested and evaluated using a large number of simulated PQ disturbances and some real-life signals, which include voltage sag, swell, interruption, oscillation transient, impulsive transient, harmonics, voltage fluctuation and their combinations. In addition, the comparison of the proposed recognition approach with some existing techniques is made. The experimental results show that the proposed method can effectively recognize the single and combined PQ disturbances.<\/jats:p>","DOI":"10.3390\/e17085811","type":"journal-article","created":{"date-parts":[[2015,8,12]],"date-time":"2015-08-12T11:38:10Z","timestamp":1439379490000},"page":"5811-5828","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Combined Power Quality Disturbances Recognition Using Wavelet Packet Entropies and S-Transform"],"prefix":"10.3390","volume":"17","author":[{"given":"Zhigang","family":"Liu","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2473","DOI":"10.1109\/TIE.2013.2272276","article-title":"Detection and classification of single and combined power quality disturbances using neural networks","volume":"61","year":"2014","journal-title":"IEEE Trans. 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