Web-based on mobile phone for automatic classification of power quality disturbance using the S-transform and support vector machines

Wenda, Alex; Hussain, Aini; Samad, Salina Abdul; Mohamed, Ramizi; Hannan, M. A.

doi:10.1109/tencon.2010.5686118

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…The flow chart of the proposed method is shown in Figure 1. In this study, three feature vectors extracted from the S-transform introduced in [15] were applied as inputs and classification stage with ANFIS implementation will be carried out using MATLAB software.…”

Section: Methodsmentioning

confidence: 99%

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Fault disturbances classification analysis using adaptive neuro-fuzzy inferences system

Shahbudin

Kassim

Mohamad

et al. 2019

IJEECS

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This paper affords the use of neuro-fuzzy technique called the Adaptive Network–based Fuzzy Inference System (ANFIS) to highlight its ability to perform fault disturbances classiﬁcation tasks using extracted features based on S-transforms methods. The ANFIS model with a ﬁve-layered architecture was trained using extracted features to classify signal data comprising various faults disturbances, namely, voltage sag, swell, impulsive, interruption, notch, and pure signal. Results obtained showed that the ANFIS model is very suitable and can generate excellent classification results provided that the right type and number of Membership Functions (MFs) are used in the classification task.

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Section: Methodsmentioning

confidence: 99%

“…According to [15], the first step in implementing the feature extraction is to map the distorted data signal to S-transform domain. For non-stationary signal interference and noise, using the analysis of Stransform, feature vectors extracted from signal interference in the form of time-frequency representation of the curves.…”

Section: S( F)mentioning

confidence: 99%

Fault disturbances classification analysis using adaptive neuro-fuzzy inferences system

Shahbudin

Kassim

Mohamad

et al. 2019

IJEECS

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“…Support vector machine (SVM) is a classification method successfully applied to diagnostic and prognostic problems (Wenda et al, 2010 ), which is a supervised learning algorithm popular for its strong theoretical foundation, ability to scale to large datasets, flexibility, and most importantly, accuracy (Chen et al, 2012 ). Such methods have been used to differentiate patients with psychiatric disorders and healthy controls (Zhu et al, 2018 ; Li et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Gray Matter Volume and Functional Connectivity in Hypochondriasis: A Magnetic Resonance Imaging and Support Vector Machine Analysis

Shen

Zhao

et al. 2020

Front. Hum. Neurosci.

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Objective: Patients with hypochondriasis hold unexplainable beliefs and a fear of having a lethal disease, with poor compliances and treatment response to psychotropic drugs. Although several studies have demonstrated that patients with hypochondriasis demonstrate abnormalities in brain structure and function, gray matter volume (GMV) and functional connectivity (FC) in hypochondriasis still remain unclear.Methods: The present study collected T1-weighted and resting-state functional magnetic resonance images from 21 hypochondriasis patients and 22 well-matched healthy controls (HCs). We first analyzed the difference in the GMV between the two groups. We then used the regions showing a difference in GMV between two groups as seeds to perform functional connectivity (FC) analysis. Finally, a support vector machine (SVM) was applied to the imaging data to distinguish hypochondriasis patients from HCs.Results: Compared with the HCs, the hypochondriasis group showed decreased GMV in the left precuneus, and increased GMV in the left medial frontal gyrus. FC analyses revealed decreased FC between the left medial frontal gyrus and cuneus, and between the left precuneus and cuneus. A combination of both GMV and FC in the left precuneus, medial frontal gyrus, and cuneus was able to discriminate the hypochondriasis patients from HCs with a sensitivity of 0.98, specificity of 0.93, and accuracy of 0.95.Conclusion: Our study suggests that smaller left precuneus volumes and decreased FC between the left precuneus and cuneus seem to play an important role of hypochondriasis. Future studies are needed to confirm whether this finding is generalizable to patients with hypochondriasis.

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Classification of Power Quality Disturbances at Transmission System using Support Vector Machines

Shahbudin

Mohmad

Suliman

et al. 2017

IJEECS

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<p>Power Quality has become one of the important issues in modern smart grid environment. Smart grid generally utilizes computational intelligence method from the generation of electricity to electricity distribution to the customers. This is done for the safety, reliability, tenacity and efficiency of the system. The classification of power disturbances has become a major topic in maintaining power quality. These disturbances occur due to faults, natural causes, load switching, energizing transformer, starting large motor, as well as utilization of power electronic devices. The key issue is about maintaining the continuous supply of electricity to the end-users without any problem. If a problem occurs, it might increase the production cost significantly especially to large-scale industries. In this paper, S-transform is used to extract distinctive features of real data from transmission system, and Support Vector Machine was utilized to classify four types PQ disturbances namely, voltage sag, interruption, transient and normal voltage. Results obtained indicate that performance of the One Against One classifier produces high accuracy using k-fold cross validation and RBF kernel.</p>

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Web-based on mobile phone for automatic classification of power quality disturbance using the S-transform and support vector machines

Cited by 3 publications

References 9 publications

Fault disturbances classification analysis using adaptive neuro-fuzzy inferences system

Fault disturbances classification analysis using adaptive neuro-fuzzy inferences system

Gray Matter Volume and Functional Connectivity in Hypochondriasis: A Magnetic Resonance Imaging and Support Vector Machine Analysis

Classification of Power Quality Disturbances at Transmission System using Support Vector Machines

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