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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on instrumentation and measurement Ročník 65; číslo 10; s. 2302 - 2312
Hlavní autoři: Li, Jianmin, Teng, Zhaosheng, Tang, Qiu, Song, Junhao
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.10.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2016.2578518