Open AccessPower‐quality disturbance recognition based on time‐frequency analysis and decision tree
Author(s) -
Zhong Tie,
Zhang Shuo,
Cai Guowei,
Huang Nantian
Publication year - 2018
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.5780
Subject(s) - decision tree , classifier (uml) , pattern recognition (psychology) , computer science , artificial intelligence , disturbance (geology) , power quality , decision tree learning , noise (video) , data mining , machine learning , engineering , paleontology , voltage , electrical engineering , image (mathematics) , biology
The quality of electric power has become an important issue for electric utility companies and their customers. With the extensive application of micro‐grid technologies, power quality (PQ) disturbances are more likely to affect users; thus, research on the recognition of PQ disturbances has attracted increased attention. This study presents a novel PQ disturbance‐recognition algorithm, based on time‐frequency (TF) analysis and a decision tree (DT) classifier. The proposed method requires fewer feature statistics compared to the S‐transform‐based approach for PQ disturbance identification. In this study, feature statistics extracted using TF analysis are trained by a DT classifier to perform the automatic classification of PQ disturbances. As the proposed methodology can efficiently identify PQ disturbances, the performance of the DT classifier can be ensured. In addition, the influence of noise is investigated, and 12 types of noisy disturbance, with signal‐to‐noise ratios of 30–50 dB, are considered for the classification problem. Finally, the proposed method is compared with other popular proposed disturbance‐recognition algorithms in terms of detection accuracy. The experimental results reveal that the proposed method can effectively detect and classify different PQ disturbances.