Rough-Set-Based Feature Selection and Classification for Power Quality Sensing Device Employing Correlation Techniques

Dalai, Sovan; Chatterjee, Biswendu; Dey, Debangshu; Chakravorti, S.; Bhattacharya, Kesab

doi:10.1109/jsen.2012.2219144

Cited by 52 publications

(27 citation statements)

References 20 publications

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“…Results show that the performance is comparable with the results reported in the literatures [1], [2], [6], [9], [26]. Hence, the contribution of the present work is the development of a Cross-wavelet transform aided FLDA based method for feature selection and SVM based classification technique, effective for a standalone MPQ disturbance sensing module using general purpose microcontroller.…”

Section: Introductionsupporting

confidence: 83%

“…It was found that [29] multiple power quality events occur at the same period of time in many cases and the signals are basically superposition of more than one PQ disturbances. There are works on multiple PQ disturbances as reported in [1], [2], [6], [9], and [12]. However, these works do not present a methodology suitable for implementation in a standalone module.…”

Section: A Power Quality Disturbances and Relevant Signal Generationmentioning

confidence: 99%

“…Different algorithms, such as, rough set based analysis [1], wavelet based analysis [2]- [5], S-transform [6]- [8], Probabilistic neural network [9] etc. have been reported recently for sensing and classification of power quality disturbances.…”

Section: Introductionmentioning

confidence: 99%

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Cross-Spectrum Analysis-Based Scheme for Multiple Power Quality Disturbance Sensing Device

et al. 2015

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This paper presents a methodology of cross-wavelet transform aided Fischer linear discriminant analysis (FLDA)-based feature selection and classification for sensing simultaneous occurrence of multiple power quality disturbances. A linear support vector machine is used for classification of the extracted features as it suits well with FLDA. This scheme is implemented in a general purpose microcontroller as a standalone module and the performance of the standalone module for sensing simultaneous occurrence of multiple power quality disturbances is judged by both online and offline testing. Results show that the performance is comparable with the results reported in the literatures. Moreover, the scheme is immune to real life uncorrelated noises due to incorporation of cross spectrum analysis in the feature extraction phase. The present method is generic in nature and can be implemented for any other microcontrollerbased applications addressing topologically similar problems. IndexTerms-Fischer linear discriminant analysis, microcontroller, cross wavelet transform, multiple power quality disturbance, support vector machine, standalone module.

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Section: Introductionsupporting

confidence: 83%

Section: A Power Quality Disturbances and Relevant Signal Generationmentioning

confidence: 99%

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Cross-Spectrum Analysis-Based Scheme for Multiple Power Quality Disturbance Sensing Device

et al. 2015

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“…A RS-based scheme can significantly reduce the attributes while keeping meaningful information intact [25,26] without using prior knowledge. During the rule generation process in rough set theory, the dispensable attributes are removed so that the final decision table can contain only minimal or indispensable attributes in a suboptimal way.…”

Section: Rs-based Decision Rule With Minimal Attributesmentioning

confidence: 99%

Rough Set-Based Approach for Automatic Emotion Classification of Music

2017

J Inf Process Syst

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Music emotion is an important component in the field of music information retrieval and computational musicology. This paper proposes an approach for automatic emotion classification, based on rough set (RS) theory. In the proposed approach, four different sets of music features are extracted, representing dynamics, rhythm, spectral, and harmony. From the features, five different statistical parameters are considered as attributes, including up to the 4 th order central moments of each feature, and covariance components of mutual ones. The large number of attributes is controlled by RS-based approach, in which superfluous features are removed, to obtain indispensable ones. In addition, RS-based approach makes it possible to visualize which attributes play a significant role in the generated rules, and also determine the strength of each rule for classification. The experiments have been performed to find out which audio features and which of the different statistical parameters derived from them are important for emotion classification. Also, the resulting indispensable attributes and the usefulness of covariance components have been discussed. The overall classification accuracy with all statistical parameters has recorded comparatively better than currently existing methods on a pair of datasets.

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“…In past studies, feature selection was either in accordance with the filter method based on the features' statistical characteristics, which made it difficult to analyze the classification ability of the feature combination [13,14], or used the wrapper method combined with the particle swarm optimization [15], genetic algorithm [16], rough set theory [17] or other intelligent algorithms, then according to the classification results chose the optimal or sub-optimal feature subset, but the efficiency of the search algorithm is unsatisfactory. Meanwhile, existing feature selection methods have to select different feature subsets under different noise conditions, and this limits the application possibilities of feature selection methods in practical engineering.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest

Huang

Cai

et al. 2016

Entropy

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Power quality signal feature selection is an effective method to improve the accuracy and efficiency of power quality (PQ) disturbance classification. In this paper, an entropy-importance (EnI)-based random forest (RF) model for PQ feature selection and disturbance classification is proposed. Firstly, 35 kinds of signal features extracted from S-transform (ST) with random noise are used as the original input feature vector of RF classifier to recognize 15 kinds of PQ signals with six kinds of complex disturbance. During the RF training process, the classification ability of different features is quantified by EnI. Secondly, without considering the features with zero EnI, the optimal perturbation feature subset is obtained by applying the sequential forward search (SFS) method which considers the classification accuracy and feature dimension. Then, the reconstructed RF classifier is applied to identify disturbances. According to the simulation results, the classification accuracy is higher than that of other classifiers, and the feature selection effect of the new approach is better than SFS and sequential backward search (SBS) without EnI. With the same feature subset, the new method can maintain a classification accuracy above 99.7% under the condition of 30 dB or above, and the accuracy under 20 dB is 96.8%.

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Rough-Set-Based Feature Selection and Classification for Power Quality Sensing Device Employing Correlation Techniques

Cited by 52 publications

References 20 publications

Cross-Spectrum Analysis-Based Scheme for Multiple Power Quality Disturbance Sensing Device

Cross-Spectrum Analysis-Based Scheme for Multiple Power Quality Disturbance Sensing Device

Rough Set-Based Approach for Automatic Emotion Classification of Music

Feature Selection of Power Quality Disturbance Signals with an Entropy-Importance-Based Random Forest

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