Classification of power quality disturbances using S-transform based artificial neural networks

Kaewarsa, Suriya

doi:10.1109/icicisys.2009.5357780

Cited by 11 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the accuracy of C18-C20 reaches 100% in all conditions, even the worst is 95% for classifying the multiple disturbance of C17. Thus, the accuracy is less affected with more disturbances combined relative to other methods [26,34,[36][37][38][39][40], where the classification accuracy is reduced greatly with more disturbances combined. Maybe this is because that the true signals can be estimated from the original distorted PQ signals by the KF-ML with denoising and that the DBN architecture can explore multi-layer nonlinear characteristics for various disturbances.…”

Section: Classificationmentioning

confidence: 94%

“…To evaluate the superiority of the proposed method, the accuracy comparison with other classification approaches in [36][37][38][39][40] is made and presented in Table 6.…”

Section: Classificationmentioning

confidence: 99%

“…As seen from Table 6, in cases of weak noise, such as 40 dB and no noise, the accuracy of the proposed method is higher than the hybrid method based on Hilbert Huang Transform and RVM [36], or almost as high as the others. However, at 30 and 20 dB noise levels, the proposed method is far better than others, such as S‐transform based ANN [37], which shows that the proposed classification method is highly immune to noise and it could be used in high‐noise environments.…”

Section: Simulation and Analysismentioning

confidence: 99%

See 2 more Smart Citations

Type identification and time location of multiple power quality disturbances based on KF‐ML‐aided DBN

Yang

Chen

Tang

et al. 2021

IET Generation Trans & Dist

View full text Add to dashboard Cite

Type identification and time location of power quality disturbances (PQDs) is the key to adopting corresponding measures to suppress disturbances. More complex multiple disturbances caused by the overlapping of different micro-grids make it a challenging task. The paper proposes a hybrid approach combing KF-ML (Kalman filter based on maximum likelihood) with deep belief network (DBN) for dealing with PQDs. To be specific, the KF-ML is firstly applied to reduce noise from the original distorted signal, and the innovation sequence obtained by KF-ML can be used to locate starting-ending times of PQDs. Then, the DBN, which fuses feature extraction and classification into a single block, is capable of recognizing the type of PQDs accurately. To verify the effectiveness of the proposed method, 20 classes of PQDs with noise interference are tested, and experiment results show that the detection time of the proposed method is very close to the set time, and the absolute error of time location is less than 0.3 ms. The average classification accuracy at different noise levels reaches about 95%, and is very high even with more disturbances combined. Thus, the proposed method is immune to noise and less affected with more disturbances combined relative to other methods.1 PQDs obviously emerged [3].Multiple power quality disturbances (MPQDs), which are composed of several single disturbances with different type, dif-This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

show abstract

Section: Classificationmentioning

confidence: 94%

“…To evaluate the superiority of the proposed method, the accuracy comparison with other classification approaches in [36][37][38][39][40] is made and presented in Table 6.…”

Section: Classificationmentioning

confidence: 99%

Section: Simulation and Analysismentioning

confidence: 99%

See 1 more Smart Citation

Type identification and time location of multiple power quality disturbances based on KF‐ML‐aided DBN

Yang

Chen

Tang

et al. 2021

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…S-transform (ST) was also introduced recently in [4][5][6] as an effective technique for PQ disturbances signal processing. It is method for the feature extraction and also detection of PQ disturbances.…”

Section: Windowing Technique and Continuous S-transform (Cst)mentioning

confidence: 99%

ANOVA Based Feature Analysis and Selection in Power Quality Disturbances Identification

Daud

Abidin

Singh

2015

AMM

View full text Add to dashboard Cite

This study was conducted in order to identify the different types of PQD based on a new approach the Analysis Of Variance (ANOVA). ANOVA is used as feature selection for the Power Quality Disturbances (PQD) parameters. The datum of PQD from the PSCAD/EMTDC® simulation has been validated before feature extraction analysis can be commenced. The obtained datum is then analyzed by using cycle windowing technique based on Continuous S-Transform (CST) to extract the features and its characteristics. Moreover, the study focuses an important issue concerning the identification of PQD selection and detection. The feature and characteristics of four types of signal such as Sag, Swell, Transient and sinusoidal normal signal are obtained. The outcome of the analysis shows that a new approach ANOVA have a different result in term of identification of PQD.

show abstract

“…The inverse of the width and frequency has a directly proportional relationship and the result is intuitive and not easy to be affected by noises [3][4][5] . The result of S-transform is a plural time-frequency matrix which contains distribution information such as amplitude, frequency and phase; the matrix is suitable for the signal feature extraction of the transient power quality disturbance signals [6][7][8] .…”

Section: Introductionmentioning

confidence: 99%

Classification of transient power quality disturbance based on S-transform and fuzzy KNN

Men¹,

Jia²,

Hao³

et al. 2015

Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015

View full text Add to dashboard Cite

Abstract. This paper proposed a new method to classify transient power quality disturbances using S-transform and K-Nearest Neighbor classifier. First, S-transform was applied to perform time-frequency analysis on the power quality disturbance signals, and the S-transform mode time-frequency matrix was achieved from the S-transform, and we got 8 valid features extracted from the matrix, then the feature vectors who consisted of the former features were input into the Fuzzy KNN classifier to perform the disturbing signals intelligent classification, then completed the intelligent classification for six types of transient power quality disturbances signals. The result showed that the structure of the proposed method was simple, the number of the needed features was little and it was doing well in classifying these types of transient power quality disturbances.

show abstract

Classification of power quality disturbances using S-transform based artificial neural networks

Cited by 11 publications

References 12 publications

Type identification and time location of multiple power quality disturbances based on KF‐ML‐aided DBN

Type identification and time location of multiple power quality disturbances based on KF‐ML‐aided DBN

ANOVA Based Feature Analysis and Selection in Power Quality Disturbances Identification

Classification of transient power quality disturbance based on S-transform and fuzzy KNN

Contact Info

Product

Resources

About