Proceedings ArticleDOI
Knowledge Discovery in Power Quality Data Using Support Vector Machine and S-Transform
K. Vivek,M. Gopal,B.K. Panigrahi +2 more
- pp 507-512
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TLDR
A comparison between the DAGSVM method and the one based on artificial neural network demonstrates the efficiency of the SVM method in classifying PQ disturbances.Abstract:
In this paper, we investigate the potential of Support Vector Machines (SVMs) for power quality data mining in electrical power systems. Modified wavelet transform, known as S-transform, has been used to extract unique features of the various power quality disturbances. Feature vectors from S-transform analysis are used to train the SVM classifier. Various multi-class SVM algorithms have been applied on the power quality data under study and the Directed Acyclic Graph (DAGSVM) algorithm is found to be performing well. A comparison between the DAGSVM method and the one based on Artificial Neural Network demonstrates the efficiency of the SVM method in classifying PQ disturbances.read more
Citations
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Dissertation
On tracing flicker sources and classification of voltage disturbances
TL;DR: It was concluded that it is possible to develop a classification system based on the Support Vector Machine method with “global settings” that can be used at any location without the need to retrain and shows sufficiently high classification accuracy when trained on data that originate from real disturbances.
Proceedings ArticleDOI
Anomaly Detection in Power Quality Measurements Using Proximity-Based Unsupervised Machine Learning Techniques
TL;DR: Four proximity-based machine learning (ML) techniques are applied to original (nontransformed) PQ data for automatic anomaly detection on unlabeled data, taking only the characteristics of the PQData into account.
Discovery and pattern classification of large scale harmonic measurements using data mining
TL;DR: This method of unsupervised learning, or clustering, has been shown to be able to detect anomalies and identify useful patterns within the monitored harmonic data set, and a novel technique has been developed to overcome this difficulty using the trend of the exponential of message length difference between consecutive mixture models.
Proceedings ArticleDOI
A fast oversampling orthogonal method for the discrete two dimensional S-transform computing
TL;DR: This method combines Brown's oversampling method with Drabycz's orthogonal method and reduces the time and memory consuming dramatically and the 2D discrete S-transform of the typical 24bits bmp image.
Book
Analysis and Pre-processing of Signals Observed: In Optical Feedback Self-Mixing Interferometry
TL;DR: This thesis studies the spectrum characteristics of OFSMI signals and outlines novel approaches to analysze and process the noisy signal at the time and frequency domain simultaneously and contributes to the framework of pre-processing and analyzing of OFMSI signals.
References
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Journal Article
Localisation of the complex spectrum : The S transform
TL;DR: The S transform as discussed by the authors is an extension to the ideas of the Gabor transform and the Wavelet transform, based on a moving and scalable localising Gaussian window and is shown here to have characteristics that are superior to either of the transforms.
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