Detection and Classification of Power Quality Disturbances Using Double Resolution S-Transform and DAG-SVMs

The accurate detection and classification of power quality (PQ) disturbances in power systems is a key step to determine the causes of these events before any proper countermeasure could be taken. This paper presents a new algorithm for detection and classification of PQ disturbances based on the co...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement Vol. 65; no. 10; pp. 2302 - 2312
Main Authors: Li, Jianmin, Teng, Zhaosheng, Tang, Qiu, Song, Junhao
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Artificial neural networks
Digital signal processors
Disturbance classification
Feature extraction
Harmonic analysis
power quality (PQ)
Power system harmonics
S-transform (ST)
Signal processing algorithms
support vector machines (SVMs)
time–frequency analysis
White noise
ISSN:0018-9456, 1557-9662
Online Access:Get full text
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Summary:The accurate detection and classification of power quality (PQ) disturbances in power systems is a key step to determine the causes of these events before any proper countermeasure could be taken. This paper presents a new algorithm for detection and classification of PQ disturbances based on the combination of double-resolution S-transform (DRST) and directed acyclic graph support vector machines (DAG-SVMs). The proposed method first employs DRST for an effective feature extraction from power signals. Then, the DAG-SVMs are used to predict the classes of PQ disturbances. The DRST not only has better time-frequency localization and stronger robustness but also reduces the computational complexity without losing the useful information of the original signal in comparison with the traditional S-transform. Through the combined use of DRST and DAG-SVMs, the algorithm can be easily implemented in embedded real-time applications. Finally, the implementation of the proposed algorithm in a digital signal processor + advanced reduced instruction set computing machine-based hardware test platform is introduced. The effectiveness of the proposed method is demonstrated by means of computer simulations and practical experiments with single and combined PQ disturbances.
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2016.2578518