Power-quality disturbance recognition based on time-frequency analysis and decision tree

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 attract...

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Bibliographic Details
Published in:IET generation, transmission & distribution Vol. 12; no. 18; pp. 4153 - 4162
Main Authors: Zhong, Tie, Zhang, Shuo, Cai, Guowei, Huang, Nantian
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 16.10.2018
Subjects:
automatic PQ disturbance classification
decision tree classifier
decision trees
distributed power generation
DT classifier
electric power quality
feature statistics
microgrid technologies
pattern classification
power supply quality
power‐quality disturbance recognition
PQ disturbance‐recognition algorithm
Research Article
signal‐to‐noise ratios
statistical analysis
TF analysis
time‐frequency analysis
utility companies
pattern classification
electric power quality
decision tree classifier
distributed power generation
automatic PQ disturbance classification
microgrid technologies
power supply quality
utility companies
time-frequency analysis
PQ disturbance-recognition algorithm
DT classifier
decision trees
TF analysis
power-quality disturbance recognition
feature statistics
statistical analysis
signal-to-noise ratios
ISSN:1751-8687, 1751-8695
Online Access:Get full text
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Summary: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.
ISSN:1751-8687
1751-8695
DOI:10.1049/iet-gtd.2018.5780